Grey wolf optimization based clustering of hybrid fingerprint for efficient de-duplication

Malhotra, Jyoti; Bakal, J. W.

doi:10.3233/mgs-180285

Cited by 14 publications

(6 citation statements)

References 31 publications

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“…However, this method requires a large dataset for processing and training. Dey, Ashour, Shi, et al (2017) developed a home-based wireless electro cardiography (ECG) (Malhotra et al, 2018a;) monitoring system using the Zigbee technology. This system is very useful for observing the people activity in their homes and periodically monitoring the physicians for healthcare.…”

Section: Organizationmentioning

confidence: 99%

Human activity recognition by wireless body area networks through multi‐objective feature selection with deep learning

Boga

Kumar

2022

Expert Systems

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Wireless body area network (WBAN) is a novel technology with the incorporation of numerous types of devices, which is also employed in health monitoring applications.Human activity recognition (HAR) receives more interest in recent times along with wearable sensors. HAR system provides information about a person's identity, personality, and psychological state. In the present scenario, it becomes important to model the active learning paradigms with the help of wearable sensors for analysing human activities. Although various deep learning models and existing algorithms secure better outcomes through the sensor data analysis regarding HAR, the decision-making evaluation seems to be a complex one. The main intent of this paper is to implement the WBAN-based HAR system using the improved deep learning model. By connecting the wearable sensors, the signals are gathered regarding human activity from diverse benchmark sources. After collecting the required signals, the pre-processing of the input signals is done using artefact removal and median filtering. Further, the feature extraction is performed, which intends to extract a set of features by utilizing short-time Fourier transform (STFT) and statistical features. For reducing the feature-length, a multi-objective-based optimal feature selection is adopted. Human activities such as 'walking, walking upstairs, walking downstairs, sitting, standing, lying, and jogging' are recognized with help of selected optimal features. The optimized probabilistic neural network (PNN) and convolutional neural network (CNN) are combined and named as adaptive probabilistic-based CNN (AP-CNN). The effective performance of optimal feature selection and recognition is accomplished by incorporating the developed backward updating position-based sea lion optimization algorithm (BU-SLnO). Finally, the performance of the BU-SLnO-AP-CNN-based suggested model is analysed, which shows 1.001%, 1.237%, 0.811%, and 0.859% advanced than SLnO-AP-CNN, feed-forward-AP-CNN (FF-AP-CNN), grey wolf optimization-AP-CNN (GWO-AP-CNN), particle swarm optimization-AP-CNN (PSO-AP-CNN) when observing the dataset 1. The experimental outcomes from comparison with various classification techniques demonstrate the efficiency of the developed technique.

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Section: Organizationmentioning

confidence: 99%

Human activity recognition by wireless body area networks through multi‐objective feature selection with deep learning

Boga

Kumar

2022

Expert Systems

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“…Opinion mining and sentiment analysis help researchers to gain insight into public emotions (Sharma and Ghose, 2020). In literature, several optimization algorithms, such as genetic algorithm (Menaga and Saravanan, 2018; Rajakumar, 2013), grey wolf optimization (GWO) (Malhotra and Bakal, 2018; Ninu Preetha et al , 2018), and so on are used for enhancing the sentiment analysis process. Sentiment analysis is accomplished in three distinct levels such as document level, sentence level and aspect level (Liu, 2012).…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis on newspaper article reviews: contribution towards improved rider optimization-based hybrid classifier

Pandiaraj¹,

Sundar²,

Pavalarajan

2021

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Purpose Up to date development in sentiment analysis has resulted in a symbolic growth in the volume of study, especially on more subjective text types, namely, product or movie reviews. The key difference between these texts with news articles is that their target is defined and unique across the text. Hence, the reviews on newspaper articles can deal with three subtasks: correctly spotting the target, splitting the good and bad content from the reviews on the concerned target and evaluating different opinions provided in a detailed manner. On defining these tasks, this paper aims to implement a new sentiment analysis model for article reviews from the newspaper. Design/methodology/approach Here, tweets from various newspaper articles are taken and the sentiment analysis process is done with pre-processing, semantic word extraction, feature extraction and classification. Initially, the pre-processing phase is performed, in which different steps such as stop word removal, stemming, blank space removal are carried out and it results in producing the keywords that speak about positive, negative or neutral. Further, semantic words (similar) are extracted from the available dictionary by matching the keywords. Next, the feature extraction is done for the extracted keywords and semantic words using holoentropy to attain information statistics, which results in the attainment of maximum related information. Here, two categories of holoentropy features are extracted: joint holoentropy and cross holoentropy. These extracted features of entire keywords are finally subjected to a hybrid classifier, which merges the beneficial concepts of neural network (NN), and deep belief network (DBN). For improving the performance of sentiment classification, modification is done by inducing the idea of a modified rider optimization algorithm (ROA), so-called new steering updated ROA (NSU-ROA) into NN and DBN for weight update. Hence, the average of both improved classifiers will provide the classified sentiment as positive, negative or neutral from the reviews of newspaper articles effectively. Findings Three data sets were considered for experimentation. The results have shown that the developed NSU-ROA + DBN + NN attained high accuracy, which was 2.6% superior to particle swarm optimization, 3% superior to FireFly, 3.8% superior to grey wolf optimization, 5.5% superior to whale optimization algorithm and 3.2% superior to ROA-based DBN + NN from data set 1. The classification analysis has shown that the accuracy of the proposed NSU − DBN + NN was 3.4% enhanced than DBN + NN, 25% enhanced than DBN and 28.5% enhanced than NN and 32.3% enhanced than support vector machine from data set 2. Thus, the effective performance of the proposed NSU − ROA + DBN + NN on sentiment analysis of newspaper articles has been proved. Originality/value This paper adopts the latest optimization algorithm called the NSU-ROA to effectively recognize the sentiments of the newspapers with NN and DBN. This is the first work that uses NSU-ROA-based optimization for accurate identification of sentiments from newspaper articles.

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“…The metaheuristic is an advanced process to generate or choose, identify a heuristic that can offer a better solution to an optimization problem, particularly with imperfect information or limited computation capacity 31,32 . These metaheuristics comprise of Particle Swarm Optimization (PSO), 33 Evolutionary computation, Ant Colony Optimization (ACO), Rider Optimization Algorithm (ROA), and Genetic Algorithm (GA). Among them, ROA 34 is a novel computing method, specifically fictional computing, which performs a series of procedures to resolve the problems of optimizations.…”

Section: Introductionmentioning

confidence: 99%

Impact of hybridized rider optimization with cuckoo search algorithm on optimal VANET routing

Gnanasekar¹,

Samiappan

2021

Int J Communication

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Summary VANET routing is a serious concern due to some of the limitations in terms of routing costs. More research has already worked on VANET routing strategies for enhancing the performance of the network. However, optimal route selection is a tedious or complex issue in this scenario. To cope with this issue, this research work attempts to introduce a new VANET routing model that considers certain network quality measures such as congestion, travel, collision, and QoS awareness cost. A new cost model is designed based on the given constraints. Along with this, the fuzzification of the QoS factor is considered along with the total routing cost. This work determines the optimal route selection with minimized routing cost by employing a novel optimization algorithm called Rider Integrated Cuckoo Search (RI‐CS) that hybridizes the concept of Rider Optimization Algorithm (ROA) and Cuckoo Search (CS). Finally, the performance of the proposed RI‐CS is analyzed against the conventional models like Particle Swarm Optimization (PSO), Firefly algorithm (FA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WAO), ROA, CS, Mean Computing Jaya Algorithm (MC‐JA), and Grey Updated Butterfly Operator (GU‐BO) in terms of certain cost analysis.

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Grey wolf optimization based clustering of hybrid fingerprint for efficient de-duplication

Cited by 14 publications

References 31 publications

Human activity recognition by wireless body area networks through multi‐objective feature selection with deep learning

Human activity recognition by wireless body area networks through multi‐objective feature selection with deep learning

Sentiment analysis on newspaper article reviews: contribution towards improved rider optimization-based hybrid classifier

Impact of hybridized rider optimization with cuckoo search algorithm on optimal VANET routing

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