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A modified machine learning classification for dental age assessment with effectual ACM-JO based segmentation

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2021年第2期17卷 95-104页

作者： Hemalatha, B. Rajkumar, N. Bannari Amman Inst Technol Comp Sci & Engn Dept Sathyamangalam 638401 Tamil Nadu India Hindusthan Coll Engn & Technol Comp Sci & Engn Dept Coimbatore 641032 Tamil Nadu India

Estimation of dental age plays a vital role in anthropology, forensics, and bio archaeology. Specific age estimation is mandatory for living and dead individuals, especially in young adolescents and children. Diverse detection of dental age schemes is calculated though they have certain limitations, such as reliability and prediction accuracy. To resolve this, a modified extreme learning machine with sparse representation classification (MELM-SRC) is used with dental image in this work. Initially, input image is preprocessed for reducing noise and smoothing in image using an anisotropic diffusion filter (ADF). Subsequently, teeth images are segmented using active contour model (ACM) with jaya optimisation (JO) and then morphological post processing has been applied on segmented result to progress classification accuracy. Next, certain features are extracted such as area, perimeter, solidity, diameter, major and minor axis length, and filled area to enhance prediction accuracy. Lastly, age has been classified with MELM-SRC. In this MELM, effectual features are classified using SRC to increase age classification accuracy. Simulation outcomes show anticipated MELM-SRC acquires superior performance than Demirjian method for dental age assessment and also other existing classification schemes such as radial basis function network (RBFN), and adaptive neuro fuzzy inference system (ANFIS) schemes.

关键词： dental age anisotropic diffusion filter ADF active contour model ACM jaya optimisation algorithm modified extreme learning machine MELM sparse representation classification SRC radial basis function network RBFN adaptive neuro fuzzy inference system ANFIS

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Range-based localisation of a wireless sensor network using jaya algorithm

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2019年第7期13卷 937-943页

作者： Manickam, Meenalochani Selvaraj, Sudha Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli Tamil Nadu India

Node localisation plays a significant role in wireless sensor networks, as most of the applications require exact location of sensor nodes. To obtain the exact location of sensor nodes, a range-based localisation method based on jaya optimisation using received signal strength indicator (RSSI) is proposed. The distance between the target node and the reference nodes is obtained from the measured RSSIs using regression-based log normal shadowing model. Further, improvement in the localisation accuracy is accomplished by formulating the location of target node as an optimisation problem. jaya optimisation algorithm is adopted, as it is parameter-free and efficient. The jaya algorithm is used for estimating the distance as well as the coordinates of the target node. The proposal is compared with particle swarm optimisation and validated through simulation and hardware experiments. The maximum localisation error using jaya and particle swarm optimisation through simulation is observed to be 0.08 and 0.37 m, respectively. Real-time experiments using jaya algorithm exhibited the maximum localisation error of about 0.14 m for indoor environment and 0.3 m for outdoor environment. The results of the proposed methodology show significant improvement in terms of accuracy.

关键词： wireless sensor networks log normal distribution particle swarm optimisation RSSI wireless sensor network jaya algorithm node localisation sensor nodes range-based localisation method signal strength indicator target node reference nodes localisation accuracy jaya optimisation algorithm particle swarm optimisation maximum localisation error

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On-line extreme learning algorithm based identification and non-linear model predictive controller for way-point tracking application of an autonomous underwater vehicle

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COGNITIVE COMPUTATION AND SYSTEMS 2019年第3期1卷 61-71页

作者： Rath, Biranchi Narayan Subudhi, Bidyadhar Natl Inst Technol Rourkela Dept Elect Engn Rourkela 769008 India Indian Inst Technol Goa Sch Elect Sci Goa Coll Engn Campus Ponda 403401 Goa India

In most of the surveillance applications of autonomous underwater vehicle (AUV), very often it is intended to follow the desired horizontal way-points, where some oceanography data need to be collected. In view of this, the motion planning algorithm using way-points is investigated in this study. The proposed work involves identification of dynamics of AUV and design of adaptive model predictive controllers which includes linear adaptive model predictive controller (LAMPC) and non-linear adaptive model predictive controller (NAMPC). Owing to the fast convergence rate and robustness property, on-line sequential extreme learning machine (OS-ELM) is employed for estimating the dynamics of AUV. To improve the OS-ELM modelling performance, jaya optimisation algorithm is applied to optimise the hidden layer parameters. The desired surveillance region is formulated in terms of way-points using heading angle obtained from desired line-of-sight path. Simulations are performed using MATLAB by applying proposed NAMPC, LAMPC and a previously reported optimal controller, namely inverse optimal self-tuning PID (IOSPID) controller. Subsequently, real-time experimentation is performed using a prototype AUV in a swimming pool. From the simulation and experimental results, it is observed that the proposed controller exhibit efficient tracking performance in face of actuator constraints as compared to LAMPC and IOSPID controller.

关键词： position control path planning optimal control self-adjusting systems mobile robots three-term control identification neurocontrollers optimisation autonomous underwater vehicles adaptive control nonlinear control systems predictive control remotely operated vehicles regression analysis robot dynamics learning systems control system synthesis desired surveillance region LAMPC reported optimal controller inverse optimal self-tuning PID controller IOSPID controller way-point tracking application autonomous underwater vehicle surveillance applications global positioning system coordinates underactuated AUV heading dynamics adaptive model predictive controllers system identification fast convergence rate robustness property on-line sequential extreme learning machine OS-ELM neural network modelling performance jaya optimisation algorithm forward-regression orthogonal least square nonlinear model predictive controller OS-ELM model surveying application line-of-sight path on-line extreme learning algorithm based identification horizontal way-points NAMPC error reduction ratio ERR criteria

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An enhanced jaya algorithm for solving nurse scheduling problem

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2019年第5期10卷 439-447页

作者： El-Ashmawi, Walaa H. Ali, Ahmed F. Suez Canal Univ Fac Comp & Informat Dept Comp Sci Ismaillia Egypt

Nurse Scheduling Problem (NSP) is one of the main optimisation problems that require an efficient assignment of a number of nurses to a number of shifts in order to cover the hospital's planning horizon demands. NSP is an NP-hard problem which subjects to a set of hard and soft constraints. Such problems can be solved by optimisation algorithms efficiently such as meta-heuristic algorithms. In this paper, we enhanced one of the most recent meta-heuristic algorithms which is called jaya for solving the NSP. The enhanced algorithm is called EJNSP (Enhanced jaya for Nurse Scheduling Problem). EJNSP focuses on maximising the nurses' preferences about shift requests and minimising the under-and over-staffing. EJNSP has two main strategies. First, it randomly generates an initial effective scheduling that satisfies a set of constraints. Second, it uses swap operators in order to satisfy the set of soft constraints to achieve an effective scheduling. A set of experiments have been applied to a set of the benchmark dataset with different numbers of nurses and shifts. The experimental results demonstrated that EJNSP algorithm achieved effective results for solving NSP in order to minimise the under-and over-staffing and satisfy the nurses' preferences.

关键词： nurse scheduling problem meta-heuristic algorithms jaya optimisation algorithm

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