A Novel Approach for Vehicle Type Classification and Speed Prediction Using Deep Learning

Madhan, E. S.; Neelakandan, S.; Annamalai, R

doi:10.1166/jctn.2020.8877

Cited by 48 publications

(24 citation statements)

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“…Zhu and Wei [14] offer the EERBLC method, a localization-free routing mechanism. EERBLC protocols are divided into three stages: cluster update and maintenance, transmission routing, and layer and uneven cluster construction.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

Neelakandan¹,

Mohan

Alotaibi

et al. 2022

Sensors

126

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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.

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Section: Literature Reviewmentioning

confidence: 99%

An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

Neelakandan¹,

Mohan

Alotaibi

et al. 2022

Sensors

126

View full text Add to dashboard Cite

show abstract

“…In [14] a model is been proposed for unprocessed raw data called the Functional Link Artificial Neural Network (FLANN) model, it also specifies the effectiveness of the model for time series seasonal forecasting. Time series forecasting with Fuzzy approaches are discussed in [15,16].…”

Section: Related Workmentioning

confidence: 99%

Forecasting of Trend-Cycle Time Series Using Hybrid Model Linear Regression

Ashwini¹,

Nagaveni²,

Singh³

2022

Intelligent Automation &Amp; Soft Computing

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“…Rather than Principal Component Analysis (PCA) [18][19][20], the Boruta algorithm [21,22] is used for the feature selection process for dimensionality reduction problem. This developed research can derive substantial patterns from factual customers transmit and transactional data [23,24] authenticated by the financial institutions.…”

Section: Knn [6]mentioning

confidence: 99%

Adaptive XGBOOST Hyper Tuned Meta Classifier for Prediction of Churn Customers

Srikanth¹,

Papineni²,

Sridevi³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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In India, the banks have a formidable edge in maintaining their customer retention ratio for past few decades. Downfall makes the private banks to reduce their operations and the nationalised banks merge with other banks. The researchers have used the traditional and ensemble algorithms with relevant feature engineering techniques to better classify the customers. The proposed algorithm uses a Meta classifier instead of an ensemble algorithm with an adaptive genetic algorithm for feature selection. Churn prediction is the number of customers who wants to terminate their services in the banking sector. The model considers twelve attributes like credit score, geography, gender, age, etc, to predict customer churn. The project consists of five modules as follows. First is the pre-processing module that identifies the missing data and fills the value with mean and mode. Second is the data transformation module where, the categorical data is converted into numerical data using label encoding to fasten the computations. The converted numerical data is normalized using the standard scalar technique. The feature selection module identifies the essential attributes using DragonFly and Firefly (Hybrid Fly) algorithms. The classification module designs an intelligent Meta learner, which combines the Ensemble Algorithm Extreme Gradient Boosting (XGBOOST) with base classifiers as "Extra Tree Classifier" and "Logistic Regression" to predict the churn customers.

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A Novel Approach for Vehicle Type Classification and Speed Prediction Using Deep Learning

Cited by 48 publications

References 0 publications

An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

Forecasting of Trend-Cycle Time Series Using Hybrid Model Linear Regression

Adaptive XGBOOST Hyper Tuned Meta Classifier for Prediction of Churn Customers

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