S N Deepa

National Institute of Technology Calicut | NITC · Department of Electrical Engineering

Degenerative musculoskeletal disease known as Osteoarthritis (OA) causes serious pain and abnormalities for humans and on detecting at an early stage, timely treatment shall be initiated to the patients at the earliest to overcome this pain. In this research study, X-ray images are captured from the humans and the proposed Gaussian Aquila Optimizer...

The present scenario of global climatic change challenges the sustainability and existence of water bodies around the globe. Due to which, it is always important and necessary to forecast the streamflow of rivers with respect to natural precipitation process. In this research study, novel enhanced variational mode decomposition (EVMD) with deep sup...

In this paper, Human fatty liver volume is measured using the proposed Human Fatty Liver (HFL) device. HFL device consist of Nano Graphene Polyvinyl (NGP) sensor and Node MCU microcontroller. NGP sensor acquires the electromagnetic radiations from fatty liver, which arise due to dielectric materials in fat. Continuous monitoring of fatty liver volu...

In this article, a new modified Spiking Neural Network (SNN) model, a form of machine learning (ML) model is employed for forecasting wind speed using real-time wind farm statistics from the site locations. Spiking Neural Network is a third-generation neural network model based on the timing of spikes produced in brain neurons. The development of s...

Machine learning neural network (NN) algorithms are being applied for the past few years in all engineering and science domain, economic sectors, image processing synthesis and analysis, and so on. Due to this, this paper work considered employing these machine learning neural models for forecasting application in respect of renewable energy applic...

In mobile ad-hoc networks (MANETs) always the quality-of-service (QoS) routing problem remains to exist and as a non-deterministic polynomial hard problem it is necessary to improve the QoS parameters to the most possible extent. For the considered MANET model, it is important to develop a suitable model that is capable of improving and enhancing t...

Policy makers and researchers are looking towards renewable energy sources due to the rising realization in emissions of greenhouse gases and the implications of these pollutants, as well as the ever-increasing need for electric energy. These renewable energy sources are connected in a distributed manner along with the loads constituting microgrids...

In this article, we propose a rectangular hollow core Photonic Crystal Fiber (PCF) sensor for high detection of women’s reproductive hormones (progesterone and estradiol) in the blood sample at THz regime (0.8 THz to 1.7 THz). The numerical sensing performances are evaluated by the full vector finite element method (FVFEM). We have achieved improve...

Rapid development of low-cost sensors and smart sensors in this twentieth century makes the electronic based industries to get into the plenty of new solutions in all the sectors. In this line software tools also support in nice a way to implement the task with less effort. This proposed work going to enable such kind of an industrial need applicat...

Continuous Stirred tank reactor is a chemical reactor system which exhibits complex non-linear dynamic characteristics. The quality of final product is based on the design of the controller. The mathematical modeling of CSTR is designed based on first principle method. Conventional PID controllers Ziegler-Nichols, Tyreus-Luyben, Cohen-Coon and IMC...

In Recent days, cloud computing technology is used as a remote-based virtual computer resource utilization to offer consumers quick and accurate massive data services. In the cloud, infrastructure as services have a significant impact on computing efficiency by Infrastructure as a services (IaaS). Cloud computing uses on-demand resource provisionin...

A novel model based approach to design a full order state observer for estimating the states of a three tank process has been attempted in this research study. State estimation has been a methodology that integrates the prediction from exact models pertaining to the system and achieves consistent estimation of the non-measurable variables. This stu...

Image segmentation is one of the most significant tasks in image analysis, and it plays an imperative job in image processing to analyse and attain meaningful information. Moreover, image segmentation is a major process of object recognition and categorization in computer vision domain. Image segmentation utilizes the image features for separating...

Image classification becomes a popular research area in computer vision due to the increasing development of image indexing and retrieval tasks. This paper proposes a Flower Henry Gas Solubility Optimization-based Deep Convolution Neural Network (FHGSO-based Deep CNN) for image classification. Initially, the input image is pre-processed through the...

Hyperspectral imaging is highly important with respect to the detection, identification and classification of various natural resources—minerals, earth’s natural eruptions, vegetation and related man-made materials and other existing backgrounds. In this study, a novel deep learning-based fuzzy-twin proximal support vector machine (DL-FTPSVM) kerne...

Wind energy is fast developing energy resource as it is renewable, pollution free and abundant. The nonlinear and fluctuation of wind are large demand for enhancing the reliability and accuracy of the power system that combines the wind speed. With an exact wind speed data, power system operators predict the power output for system planning and sch...

Diabetes is a serious metabolic disorder with high rate of prevalence worldwide; the disease has the characteristics of improper secretion of insulin in pancreas that results in high glucose level in blood. The disease is also associated with other complications such as cardiovascular disease, retinopathy, neuropathy and nephropathy. The developmen...

Nonlinear radial basis function neural network (RBFNN) model and a wavelet neural network (WNN) model are developed in this research study to perform multi-step wind speed forecasting of the considered wind farm target sites. Wind speed forecasting is one of the most essential predictions to be done in the power generation sector because this facil...

Deep learning based novel intelligent neural network models are developed in this research study and employed for performing multi-step wind speed and wind power forecasting for the data pertaining to certain wind farms. It has always been tedious to predict wind speed and wind power accurately due the existence of non-linearity in the wind farm da...

Diabetes Mellitus (DM) is a serious health problem that affects majority of peoples worldwide, the conventional diagnosis procedure estimates the amount of glucose level in blood and the treatment is to regulate the blood glucose to desired level. As an alternative, an ancient therapy that has been followed for more than two millennium period is th...

Several control strategies are proposed and developed to enhance the performance of the various underactuated systems, particularly inverted pendulum. This paper presents the dual-mode fractional-order control with a reference model for pitch and angle control of an inverted pendulum. An inertia weighted PSO is utilized for optimal tuning of the FO...

In this paper, type-2 fuzzy logic design is employed to find the weight values of the radial basis function (RBF) neural network model, and thereby, the trained RBF neural network (RBFNN) model is intended to perform network energy optimization of the cloud-assisted internet of things in wireless sensor networks (WSNs). RBF neural model comes under...

Purpose Limitations encountered with the models developed in the previous studies had occurrences of global minima; due to which this study developed a new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization. Ubiquitous machine l...

One of the common non-linear process control problems is the continuous stirred tank reactor problem and the reactor is applied widely in chemical process industries. It is of high importance to develop a suitable controller scheme for the concentration and temperature control of the considered non-linear continuous stirred tank reactor (CSTR) mode...

In this paper, a new adaptive extreme learning machine (ELM) neural network‐Fuzzy system framework is developed for effective and efficient network energy optimization of internet‐of‐things (IoTs) sensor nodes in wireless sensor networks (WSNs). Each sensor nodes in a WSN communicates with one another in varied methods to transfer the data from IoT...

The growth of swarm intelligence approaches and machine learning models in the field of medical image processing is extravagant, and the applicability of these approaches for various types of cancer classification has as well grown in the recent years. Considering the growth of these machine learning models, in this work attempt is taken to develop...

In this paper, an effective network energy optimization is carried out for the internet of things (IoTs) sensor nodes in respect of the wireless sensor networks. A capsule neural network architectural model is proposed here to achieve better performance by minimizing the network energy overhead for the wireless sensor network (WSN) aided internet o...

Jordan—Elman neural network (JENN) is a class of recurrent neural network that utilizes their internal unit to process pattern of samples. JENN simply extends the multilayer perceptron by enrolling context neurons which are those internal units. The main focus of this paper is to design and develop an effective controller model for doubly fed induc...

This paper proposes a hybrid multi-step wind speed prediction model based on combination of singular spectrum analysis (SSA), variational mode decomposition (VMD) and support vector machine (SVM) and was applied for sustainable renewable energy application. In the proposed SSA–VMD–SVM model, the SSA was applied to eliminate the noise and to approxi...

Design of controller for a doubly fed induction generator driven by a variable speed wind turbine employing deep learning neural networks whose weights are tuned by grey artificial bee colony algorithm is developed and simulated in this work. This paper presents the mathematical modelling of the doubly fed induction generator (DFIG) and the control...

There is a growing demand for power from day to day. At present, the development achieved in power production from wind is highly significant. In this work, an optimized nonlinear neural network architectural model integrated with a modified firefly algorithm and particle swarm optimization is proposed to perform multistep wind-speed forecasting fo...

Digital signal filtering is one of the prime area which is frequently used in practical applications. In the class of digital filters, the prominent filters include – filters with finite impulse response (FIR) and filters with infinite impulse response (IIR). Low pass, high pass, band pass and band stop filters are the different types of filters th...

Object segmentation is a prominent and challenging issue pertaining to image analysis and computer vision applications. This segmentation enables a higher number of applications like image retrieval, object recognition and object reconstruction. Considering this importance of object segmentation, the ultimate aim of the proposed research work in th...

An adaptive control with reference model provides the solution for uncertainty in dynamics, transient performance characteristics of autonomous systems. The high frequency oscillation and poor settling time are the important challenges in adaptive control methodology. This article proposes the Model Reference Adaptive Control (MRAC) augmented with...

A swift improvement in the development of technologies in this communication era has created an enormous traffic comprising of multimedia data to cloud networks. The multimedia applications are very sensitive to quality of service (QoS) parameters. The throughput of packets is proportionate to the quality of the received multimedia data. The aim of...

The focus is made to develop predictor models for wind speed prediction employing the support vector machine neural models. Basically, support vector machines (SVM) is employed as classifiers, but this contribution models variant of SVM to act as predictors. A developed model of linear support vector machine (LSVM) and proximal support vector machi...

In order to minimize the power loss and to control the voltage in the power systems, the proposed momentum-based wavelet neural network and proposed momentum-based double wavelet neural network are proposed in this paper. The training data are obtained by using linear programming method by solving several abnormal conditions. The control variables...

This paper proposes a novel method to select hidden neurons in ELMAN neural networks for wind speed prediction application. Either over fitting or under fitting problem caused due to the random choice of hidden neuron numbers in artificial neural network. This paper suggests the solution to solve either over fitting or under fitting problems. In or...

In this paper methodologies are proposed to estimate the number of hidden neurons that are to be placed numbers in the hidden layer of artificial neural networks (ANN) and certain new criteria are evolved for fixing this hidden neuron in multilayer perceptron neural networks. On the computation of the number of hidden neurons, the developed neural...

Continuous Stirred Tank Reactor (CSTR) plays a vital role in chemical process industries which exhibits of highly non linear behaviorand has wide operating ranges. Here it is necessary, proper analysis of CSTR in chemical industries in order to get the desired output. Unfortunately, in real case the behavior of the CSTR is very different from that...

In this paper, a deep learning neural network model predictive controller (DLNNMPC) is designed to analyse the performance of a non-linear continuous stirred tank reactor (CSTR) that performs parallel and series reactions. The data generated employing the state space model of CSTR is used to train the designed deep learning neural network controlle...

The intermittent and volatility of solar energy and wind energy productions are caused because of the solar and wind resources (solar irradiance and wind speed) irregularity and haphazardness. The pressure regards with the planning and control of the solar farm, the wind farm and energy system are relieving based on the forecasts of spots on the so...

Solar irradiance forecasting is an ongoing research area, because of the improved and advanced utilization of renewable energy resource plenty of researchers have turned their attention to constitute intelligent solar irradiance forecasting tool. This paper provides the overview of the solar irradiance forecasting process, review the researches reg...

This paper focuses on the visual-based colour image segmentation with a global biotic cross pollination algorithm (GBCPA). The global biotic cross pollination algorithm segments the structurally challenging objects based on the colour, edge, entropy and edge information in the CIE L*a*b* colour space. The L*a*b* colour space is a colour-opponent sp...

In line to meet the energy exigency, renewable energies like wind and solar receive remarkable popularity, expeditious enlargement of power generation from wind and solar energy entails acute forecasting of wind speed and solar irradiation therefore, it has been an intensive research field in recent and past years. This paper endeavor prediction of...

This study is intended to propose new criteria to decide appropriate hidden layer neuron numbers in Recursive Radial Basis Function Networks (RRBFN) and successfully applied to the wind speed forecasting application in renewable energy system. Purpose of the proposed methodology eliminate both either over fitting or under fitting issues. The proper...

Along with wind energy, accurate forecasting of wind speed are the basics of providing efficient power service provision to consumers. This article discusses the knowledge related to the wind energy system and performs a comparative study of wind speed forecasting in wind energy systems based on various available methods, such as persistence, physi...

In this research carried out work on modeling innovative neural network based on a mixture of six self governing artificial neural network such as three feed forward neural networks (Radial basis function neural network (RBFNN), Multi-layer perceptron neural network (MLPNN) and Back propagation neural network (BPNN)), one feedback neural network (E...

In the modern market of power system, obtaining an optimal placement and setting up the FACTS devices epitomizes an onerous optimization problem. This is due to its cogent objective function along with multimodal nature. This study presents a solution methodology for optimal placement of Thyristor Controlled Series Capacitor (TCSC) and Static VAR C...

A proposed real coded genetic algorithm based radial basis function neural network classifier is employed to perform effective classification of healthy and cancer affected lung images. Real Coded Genetic Algorithm (RCGA) is proposed to overcome the Hamming Cliff problem encountered with the Binary Coded Genetic Algorithm (BCGA). Radial Basis Funct...

Wind power forecasting is the major area of concern in wind power generation due to the unpredictability of wind speed. Existing soft computing and statistical methods focus towards deterministic wind forecasting and neglects the uncertainties associated with wind flow. In this paper, the wavelet decomposition combined with adaptive neuro fuzzy inf...

Purpose The loading and power variations in the power system, especially for the peak hours have abundant concussion on the loading patterns of the open access transmission system. During such unconditional state of loading the transmission line parameters and the line voltages show a substandard profile, which depicts exaction of congestion manage...

The scope of this paper is to forecast wind speed. Wind speed, temperature, wind direction, relative humidity, precipitation of water content and air pressure are the main factors make the wind speed forecasting as a complex problem and neural network performance is mainly influenced by proper hidden layer neuron units. This paper proposes new crit...

Wind speed forecasting is most needed due to its essentiality in wind farm and power system control and planning operation. Due to the increase of energy demands in order to meet the energy requirement wind energy receive a center of attraction because of its huge amount of availability and eco-friendly characteristics. Though numerous researches i...

This paper analyzes various earlier approaches for selection of hidden neuron numbers in artificial neural networks and proposes a novel criterion to select the hidden neuron numbers in improved back propagation networks for wind speed forecasting application. Either over fitting or under fitting problem is caused because of the random selection of...

Accurate wind speed forecasting is a challenging, crucial and important task because it highly impacts on the power system and wind farm planning, scheduling and control operation. This article presents comparative performance analysis on the wind speed forecasting application based on the six artificial neural network namely, back propagation netw...

In the past decades, numerous researchers suggested various approaches for wind speed prediction model, but still an exact wind speed prediction is of high thrust field. This paper introduces novel Ensemble neural networks for different time scale wind speed prediction, which is developed by means of the accumulation of absolute neural networks suc...

The main objective of this paper is to calculate the electric field and the magnetic field under high voltage transmission lines. The magnetic fields are produced whenever there is flow of electrons or whenever the electrical equipment is in use. The electric field is produced due to the presence of voltage. The electric field will exist no matter...

Recent years many flight control systems and industries are employing PID controllers to improve the dynamic behavior of the characteristics. In this paper, PID controller is developed to improve the stability and performance of general aviation aircraft system. Designing the optimum PID controller parameters for a pitch control aircraft is importa...

This paper deals with the simulation and modelling of a Quadruple-Tank interacting system using various controllers. Quadruple-Tank is a one kind of MIMO (Multiple Input Multiple Output) system. It is not possible to determine all the parameters used in Multi variable systems. The major issues in a multivariable process are that loop interaction ca...

Today many aircraft control systems and process control industries are employing classical controller such as Proportional Integral Derivative Controller (PID) to improve the system characteristics and dynamic performance. To improve the stability analysis and system performance of an aircraft, PID controller is employed in this paper. The safety o...

Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging...

This paper deals with renewable energy system needs a hybrid neural network model on predicting the accurate wind speed. The wind speed and wind direction is fluctuating in nature. Therefore short term and long term wind speed forecast is more significant for establishing the availability of wind power generation. The objective is to compute the wi...

This paper proposes a design method for determining the optimal proportional-integral-derivative (PID) controller parameters of a boost inverter using the evolutionary algorithms for photovoltaic (PV) system. This paper details how to employ the evolutionary algorithm to search efficiently the optimal PID controller parameters of boost inverter. A...

Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes...

The unregulated low voltage photovoltaic dc supply can be converted into regulated high-voltage ac supply in a single stage by the boost inverter. In this paper, a Fuzzy Logic based Dynamic Sliding Mode Control (FLDSMC) of the boost inverter for photovoltaic application is designed and simulated. FLDSMC essentially consists of a fuzzy knowledge bas...

The optimal location of Flexible AC Transmission Systems (FACTS) controllers in a multi-machine power system using proposed differential gravitational search algorithm (DGSA) optimization method is proposed in this paper. The main objective of this paper is to employ DGSA optimization technique to solve optimal power flow problem in the presence of...

One of the key tasks to perform in the complicated operation and planning of the power system is the optimal power flow. The Unified Power Flow Controller (UPFC) is one of the Flexible AC Transmission System (FACTS) powerful power electronics device. The UPFC is capable of providing complex control of power systems. This paper focuses on optimally...

The very low bitrate requirement for broadband video telephony application is achieved via joint adjustment of frame rate and frame bit allocation. The frame rate control normally modelled using parameters that are related to the residual (error) signal, such as frame difference (FD), displace frame difference (DFD). And motion fields, such as moti...

Flight dynamics deals principally with the natural response of the aircraft to longitudinal perturbations that typically consist of two underdamped oscillatory modes having rather different time scales. One of the modes has a relatively short period and is usually quite heavily damped. This is called the short period mode. The other mode has a much...

This paper presents a new approach to select number of hidden neurons in neural network in renewable energy systems. The random selection of number of hidden neurons might cause over fitting and under fitting problems in neural networks. The proper selection of neurons in hidden layer is important in the design of neural network model. To fix hidde...

Medical diagnostics, a technique used for visualizing the internal structures and functions of human body, serves as a scientific tool to assist physicians and involves direct use of digital imaging system analysis. In this scenario, identification of brain tumors is complex in the diagnostic process. Magnetic resonance imaging (MRI) technique is n...

This paper proposes new criteria to fix hidden neuron in Multilayer Perceptron Networks for wind speed prediction in renewable energy systems. To fix hidden neurons, 101 various criteria are examined based on the estimated mean squared error. The results show that proposed approach performs better in terms of testing mean squared errors. The conver...

— this paper presents a digital current controller for H-bridge pulse width modulation (PWM) converters, whose sampling frequency equals quadruple of the switching frequency,. This current regulator detects the ac current and manipulates the voltage reference not only at the upper and lower peaks of the PWM triangle carrier but also at its zero cro...

The aim of this research is to find a method for providing better visual quality across the complete video sequence in H.264 video coding standard. H.264 video coding standard with its significantly improved coding efficiency finds important applications in various digital video streaming, storage and broadcast. To achieve comparable quality across...

Since usage of digital video is wide spread nowadays, quality considerations have become essential, and industry demand for video quality measurement is rising. This proposal provides a method of perceptual quality assessment in H.264 standard encoder using objective modeling. For this purpose, quality impairments are calculated and a model is deve...

For linear systems and nonlinear systems, classic controllers such as PID have been widely used in industrial control processes and in flight control systems because of their simple structure and robust performance in a wide range of operating conditions. Several numerical approaches such as Fuzzy Logic Controller (FLC) algorithm and evolutionary a...

This paper addresses a new approach for rotor parameter estimation of induction motors. Condition monitoring of electric motors avoids unexpected motor failures and greatly improves system reliability and maintainability. These are very important issues in motor-driven and power-electronics systems since they are very important issues in motor-driv...

This paper introduces the concept and practice of Neural Network architectures for wind speed prediction in wind farms. The wind speed prediction method has been analyzed by using back propagation network and radial basis function network. Artificial neural network is used to develop suitable architecture for predicting wind speed in wind farms. Th...

This paper proposes a Neural Network based hybrid computing model for wind speed prediction in renewable energy systems. Wind energy is one of the renewable energy sources which lower the cost of electricity production. Due to the fluctuation and nonlinearity of wind, the accurate wind speed prediction plays a major role in renewable energy systems...