Improved wolf swarm optimization with deep-learning-based movement analysis and self-regulated human activity recognition

Thanarajan, Tamilvizhi; Alotaibi, Youseef; Rajendran, Surendran; Nagappan, Krishnaraj

doi:10.3934/math.2023629

Cited by 31 publications

(6 citation statements)

References 29 publications

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“…The FOA technique gains a fitness function (FF) utilizing 3 input parameters distance to neighbors, energy for CH selection, and trust level [19].…”

Section: Design Of Clustering Processmentioning

confidence: 99%

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Alotaibi,

Rajasekar,

Jayalakshmi

et al. 2024

CMC

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Rapid development in Information Technology (IT) has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle (V2V) transmission. Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data. The communication occurs directly between V2V and Base Station (BS) units such as the Road Side Unit (RSU), named as a Vehicle to Infrastructure (V2I). However, the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time. Therefore, the scheme of an effectual routing protocol for reliable and stable communications is significant. Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment. Therefore, this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing (FOA-EECPCR) technique in VANETS. The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET. To accomplish this, the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy, distance, and trust level. For the routing process, the Sparrow Search Algorithm (SSA) is derived with a fitness function that encompasses two variables, namely, energy and distance. A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method. The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

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“…The FOA technique gains a fitness function (FF) utilizing 3 input parameters distance to neighbors, energy for CH selection, and trust level [19].…”

Section: Design Of Clustering Processmentioning

confidence: 99%

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Alotaibi,

Rajasekar,

Jayalakshmi

et al. 2024

CMC

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“…The optimized extraction procedure may enable greener EC preparation and consumption by using less coffee powder to generate the same amazing output. Thanarajan et al [23] proposed using an Electronic Nose Application to control coffee output quality by odor categorization. Instant coffee samples are collected and analyzed depending on temperature, concentration, and brand, achieving 97.1% overall odor uniqueness.…”

Section: Related Workmentioning

confidence: 99%

Using Hierarchical Agglomerative Clustering in E-Nose for Coffee Aroma Profiling: Identification, Quantification, and Disease Detection

Selvanarayanan,

Rajandran,

Alotaibi

2023

I2M

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Numerous coffee devotees believe that the coffee smell plays a vital role in the coffeedrinking insight, complementing the taste and enhancing delight. In the traditional strategy, aroma patterns and profiles are observed by extensive investigation of human olfaction. However, the outcome tends to be imprecise. Tackling the difficulties encountered in distinct scent profiles linked to various coffee bean varieties, including Arabica, Robusta, Monsoon Malabar, Chikmagalur, and Coorg coffee, as well as diverse roasting techniques, through the utilization of Electronic Nose Applications for the investigation of coffee aromas. The suggested methodology employs e-nose technology utilizing conducting polymer sensors to detect aroma volatile chemicals found in coffee, including furaneol, 2-methylisoborneol, and 3-methylindole. The e-nose olfactory characteristics of coffee beans at various stages of roasting are systematically examined and discernible patterns are duly identified. The average intensity of the coffee aroma perceived at a distance of 10 centimeters was rated as 3.9 on a scale of 5. The observed standard deviation of coffee aroma intensity at a distance of 10 centimeters was determined to be 3.8 on a scale of 5. The p-value associated with the disparity in average fragrance scores was determined to be 0.05.

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“…As a result, the accuracy of the classifier model is degraded if inappropriate or no preprocessing is performed. Therefore, data preprocessing or cleaning is carried out before encoding ( Thanarajan et al, 2023 ). In this study, Python’s NLP tool is utilized for preprocessing the tweeter dataset.…”

Section: The Proposed Modelmentioning

confidence: 99%

Computational linguistics based text emotion analysis using enhanced beetle antenna search with deep learning during COVID-19 pandemic

Alotaibi,

Selvi Sundarapandi,

et al. 2023

PeerJ Computer Science

Self Cite

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Computational intelligence and nature-inspired computing have changed the way biologically and linguistically driven computing paradigms are made. In the last few decades, they have been used more and more to solve optimisation problems in the real world. Computational linguistics has its roots in linguistics, but most of the studies being done today are led by computer scientists. Data-driven and machine-learning methods have become more popular than handwritten language rules, which shows this shift. This study uses a new method called Computational Linguistics-based mood Analysis using Enhanced Beetle Antenna Search with deep learning (CLSA-EBASDL) to tackle the important problem of mood analysis during the COVID-19 pandemic. We sought to determine how people felt about the COVID-19 pandemic by studying social media texts. The method is made up of three main steps. First, data pre-processing changes raw data into a shape that can be used. After that, word embedding is done using the ‘bi-directional encoder representations of transformers (BERT) process. An attention-based bidirectional long short-term memory (ABiLSTM) network is at the heart of mood classification. The Enhanced Beetle Antenna Search (EBAS) method, in particular, fine-tunes hyperparameters so that the ABiLSTM model works at its best. Many tests show that the CLSA-EBASDL method works better than others. Comparative studies show that it works, making it the best method for analysing opinion during the COVID-19 pandemic.

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Improved wolf swarm optimization with deep-learning-based movement analysis and self-regulated human activity recognition

Cited by 31 publications

References 29 publications

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Using Hierarchical Agglomerative Clustering in E-Nose for Coffee Aroma Profiling: Identification, Quantification, and Disease Detection

Computational linguistics based text emotion analysis using enhanced beetle antenna search with deep learning during COVID-19 pandemic

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