Prof. Dr. H. S. Behera

• Ph.D (Engineering), MTech (Computer Science & Engineering)
• Head of Department at Veer Surendra Sai University of Technology(VSSUT), Govt. of Odisha, India

Swarm Intelligence (SI) has proven to be useful in solving issues that are difficult to solve using traditional mathematical methodologies by using a collective behavior of a decentralized or self-organized system. SI-based optimization algorithms use a collaborative trial-and-error process to identify a solution. The development of various efficie...

The Internet of Vehicles (IoV) refers to a network of vehicles that may exchange data and coordinate their movements via the use of sensors, wireless networking, and computer programs. With the advancement of technology, automation, and artificial intelligence, it is anticipated that the IoV will eventually replace conventional transportation netwo...

Over the last three decades, several researchers have been putting their efforts into developing non-deterministic fuzzy time series (FTS) models using the traditional fuzzy set. However, considering a set of membership values to each element of the time series, the hesitant fuzzy set glorifies the chances to capture the fuzziness and uncertainty d...

Stroke is a clinical condition wherein blood vessels inside the brain rupture, resulting in brain damage. Symptoms may appear if the brain's blood flow and other nutrients are disrupted. Early identification of different stroke warning signals can assist in lessening the severity of the stroke. This research study identifies early stroke diseases b...

ML is a subset of computing procedures that aims to imitate human astuteness by swotting from its surroundings. It has become a challenging task to diagnose the ailment and provide the appropriate treatment at the right time because of the increasing population and disease. The recent technological advancements have propelled the adoption of innova...

A 55-year-old lady with bamboo stick injury to her right eye suffered corneal laceration with retained wooden foreign body in the anterior chamber. In the first-sitting corneal laceration repair, lens aspiration with foreign body removal was done. Two days later, she developed signs and symptoms of endophthalmitis for which pars plana vitrectomy wi...

Predicting software defects is critical for ensuring software quality. Many supervised learning approaches have been used to detect defect-prone instances in recent years. However, the efficacy of these supervised learning approaches is still inadequate, and more sophisticated techniques will be required to boost the effectiveness of defect predict...

The purpose of IDS is to protect the confidentiality, integrity, and availability of a system. As computer networks become more vulnerable to intruder attacks, IDS is a vital component that analyses computer networks activities and categorises them as normal or abnormal. Several supervised ML algorithms exist to deal with high volumes of training d...

The immense increase in software technology has resulted in the convolution of software projects. Software effort estimation is fundamental to commence any software project and inaccurate estimation may lead to several complications and setbacks for present and future projects. Several techniques have been following for ages of the software effort...

Technology is moving toward more intelligent and connected devices in this digital world. The transport system is also not untouched by this phenomenon. Vehicles are becoming intelligent and autonomous with the use of sensors and communication techniques. Progress in IoT has given rise to a new field in transportation and vehicular networks, namely...

Developing software applications has become more perplexing nowadays due to the huge usage of software applications. Under such circumstances, developing software without defects is a very challenging task. So, detecting defects in software modules is necessary for the developers to allocate appropriate sources for the project. Knowing the defects...

Early prediction of diabetes is often needed for a clinically effective outcome due to the existence of a relatively long asymptomatic period. Because of its long-term asymptomatic period, about 50% of the people suffering from diabetes are unidentified. It is only possible to make an early diagnosis of diabetes by thoroughly examining both common...

Any mechanism aims to formulate the existence of a human being simple and comfortable. Big data is using to take out the important data from a huge quantity of structured and unstructured data. From the last few years, usage of learning management system has been rapidly rising. Students have initiated utilizing cell phones, mainly smartphones that...

With the advances in technology, IoT devices have become an integral part of our daily lives due to their rapid expansion and deployment. As the IoT devices communicate continuously leads to concerns about privacy and security due to vulnerabilities which the attackers can exploit. The raw observations of sensor nodes influence the decision-making...

Alcohol consumption is the global yoke of injury and disease attributable as per the early study. The excessive intake of alcohol is coupled with unconstructive consequences and jeopardizing future prospects. This paper presents an ensemble model made of an array of five chemical compounds of quartz crystal microbalance (QCM) sensors to find the co...

Model with better learning ability and lower structural complexity is desirous for accurate exchange rate forecasting. Faster convergence to optimal solutions has always been a goal for the researcher in building forecasting models. And this is achieved by extreme learning machines (ELMs) due to their single hidden layer architecture and superior g...

Electronic control units (ECUs) in today’s vehicles interact with one another through the vehicle’s internal network to maintain the vehicle’s performance and safety. Because of wide variety of sensors, enhanced driving capabilities, Vehicle-to-Everything (V2X) communication, emerging Connected and Automated Vehicles (CAVs) will have more ECUs and...

To ensure software quality, software defect prediction plays a prominent role for the software developers and practitioners. Software defect prediction can assist us with distinguishing software defect modules and enhance the software quality. In present days, many supervised machine learning algorithms have proved their efficacy to identify defect...

The surge of the novel COVID-19 caused a tremendous effect on the health and life of the people resulting in more than 4.4 million confirmed cases in 213 countries of the world as of May 14, 2020. In India, the number of cases is constantly increasing since the first case reported on January 30, 2020, resulting in a total of 81,997 cases including...

The essential role of intrusion detection is to manage the critical infrastructure to detect malicious activity competently concerning the Internet of Things (IoT). The IoT network is used to communicate and control information among various components composing a critical system. The essential inclination of infrastructure swerves to confront secu...

Over the years, numerous fuzzy time-series forecasting (FTSF) models have been developed to handle the uncertainty and non-determinism in the time-series (TS) data. To handle the non-determinism and indeterminacy, researchers have considered either intuitionistic fuzzy set or hesitant fuzzy set theory. However, in both the fuzzy set theories (FST),...

An anomaly exposure system's foremost objective is to categorize the behavior of the system into normal and untruthful actions. To estimate the possible incidents, the administrators of smart cities have to apply anomaly detection engines to avert data from being jeopardized by errors or attacks. This article aims to propose a novel deep learning‐b...

After the World War II, every country throughout the world is experiencing the biggest crisis induced by the devastating Coronavirus disease (COVID-19), which initially arose in the city of Wuhan in December 2019. This global pandemic has severely affected not only the health of billions of people but also the economy of countries all over the worl...

The dynamic nonlinearity approach, coupled with the exchange rate data series, makes its future predictions difficult. Sophisticated methods are highly desired for effective prediction of such data. Artificial neural networks (ANNs) have shown their ability to model and predict such data. This article presents a multi-verse optimizer (MVO) based mu...

The high-dimensional features in the data may affect the performance of the classification model as all of them are not useful. The selection of relevant optimal features is a tedious task, especially the data in which the number of features is high. This paper proposed a new feature selection (FS) approach based on the artificial electric field al...

Universe of discourse (UOD) Number of interval (NOI) Length of interval (LOI) Ratio trend variation (RTV) Probabilistic intuitionistic fuzzy set (PIFS) Fuzzy logical relationships (FLRs) Support vector machine (SVM) A B S T R A C T The present research proposes a novel probabilistic intuitionistic fuzzy time series forecasting (PIFTSF) model using...

In 21st century, the surge of novel coronavirus (COVID-19) with its origin in Wuhan city of south China has caused a devasting effect not only on the public health but also on the economy of the countries all over the world. Early identification of the disease is the only significant way of combatting with COVID-19 infection. Though RT-PCR (Reverse...

In the twenty-first century, the novel coronavirus (COVID-19) with its origin in the city of Wuhan has been spreading expeditiously and infecting more than 4.9 million population of the world as of May 19, 2020. As it is inducing serious threat to the global health, it is necessary to develop accurate prediction models and early diagnosis tools of...

The IoT is the next age of communication, as the rapidity of connecting physical objects around us to the internet is mounting swiftly. The inanimate physical devices can be empowered to create, receive, and exchange data into information networks to provide highly developed intelligent services without any human intervention. The future IoT applic...

This book consists of peer-reviewed papers presented at the First International Conference on Intelligent Computing in Control and Communication (ICCC 2020). It comprises interesting topics in the field of applications of control engineering, communication and computing technology. As the current world is witnessing the use of various intelligent t...

Exchange rates are highly fluctuating by nature, thus difficult to forecast. Artificial neural networks (ANN) have proved to be better than statistical methods. Inadequate training data may lead the model to reach suboptimal solution resulting, poor accuracy as ANN-based forecasts are data driven. To enhance forecasting accuracy, we suggests a meth...

In the software engineering, estimation of the effort, time and cost required for the development of software projects is an important issue. It is a very difficult task for project managers to predict the cost and effort needed in the premature stages of planning. Software estimation ahead of development can reduce the risk and increase the succes...

1 Over the past few decades, time series forecasting (TSF) has been predominantly performed using different artificial neural network (ANN) models. However, the performance of ANN models in TSF has not yet been fully explored due to several 1 issues like the determination of near-optimal ANN architecture for a time series and the efficiency of trai...

In the original publication, ‘ALGORITHM 1’ was missed out completely during typesetting.

In the past few years, non-stochastic fuzzy time series (FTS) models have drawn remarkable attention of researchers from different domains. Unlike traditional stochastic models, FTS models do not require any strict assumption on the characteristics of data to be modeled and are applicable to time series even with uncertainty. However, the effective...

In recent years, Jaya optimization algorithm has been successfully applied in several optimization problems. This paper presents a novel feature selection (FS) approach based on Jaya optimization algorithm (FSJaya) along with supervised machine learning techniques to select the optimal features. This approach uses a search technique to find the bes...

The evolvement of the fuzzy system has shown influential and successful in many universal approximation capabilities and applications. This paper proposes a hybrid Neuro-Fuzzy and Feature Reduction (NF-FR) model for data analysis. This proposed NF-FR model uses a feature-based class belongingness fuzzification process for all the patterns. During t...

In recent years, due to advancement in medical technologies and its devices, a large volume of medical data is generated continuously from different sources at every moment. Analyzing these large volumes of medical data and correctly diagnosing the diseases are challenging tasks. Generally, these medical data contain uncertain, imprecise, and incom...

Now-a-days, a large volume of biomedical data are continuously generated from various biomedical devices and experiments due to the rapid technological advancement in medical science. The effective analysis of these biomedical data such as extracting the significant features biologically and diagnostically is really a challenging task. This paper p...

Over the years Fuzzy Time Series (FTS) has used more popularly for forecasting the real time data. Generally in FTS method the membership value are not considering for the forecasting purpose, and it consider as a drawback in the forecasting process. Recently some researchers have overcome this problem by introducing the artificial neural network (...

In recent time, fuzzy time series forecasting is a major research among the researchers and many of the researchers have contributed their research to increase the forecasting efficiency in the model. There are some factors, which influence the accuracy rate, where divide the universe of discourse is one of the major factors to increase the accurac...

Fuzzy time series forecasting (FTSF) methods avoid the basic assumptions of traditional time series forecasting (TSF) methods. The FTSF methods consist of four stages namely determination of effective length of interval, fuzzification of crisp time series data, modeling of fuzzy logical relationships (FLRs) and defuzzification. All the four stages...

This proceeding discuss the latest solutions, scientific findings and methods for solving intriguing problems in the fields of data mining, computational intelligence, big data analytics, and soft computing. This gathers outstanding papers from the fifth International Conference on “Computational Intelligence in Data Mining” (ICCIDM), and offer a “...

Successful prediction of stock indices could yield significant profit and hence require an efficient prediction system. Higher order neural networks (HONN) have several advantages over traditional neural networks such as stronger approximation, higher fault tolerance capacity and faster convergence characteristics. This paper proposes an adaptive s...

Uncertainty and complexity associated with the stock data make the exact determination of future prices impossible. Successful prediction of a stock future price requires an efficient prediction system. This paper proposes an artificial chemical reaction optimization based functional link network termed as ACFLN for stock market forecasting. The ef...

Medical disease classification using machine learning algorithms is a challenging task due to the nature of data, which can contain incomplete, uncertain, and imprecise information. The availability of such information in the dataset affects the performance of the classification model. In this paper, a Linguistic Neuro-Fuzzy with Feature Extraction...

Achieving improved prediction accuracy with minimal input data and computationally less complex model is a challenge in financial time series forecasting research. Constructing the model from training data and evaluate it on test data is a common methodology which requires lots of human interventions. This paper developed three evolving higher orde...

Both physical and chemical objects, properties, behaviours have remained a great inspiration for the optimization community to develop competitive algorithms in contrast to nature-inspired, swarm- and evolutionary-based algorithms. Although the number of developments in both the areas is only a few, still those algorithms are quite efficient to com...

Prediction of stock index remains a challenging task of the financial time series prediction process. Random fluctuations in the stock index make it difficult to predict. Usually the time series prediction is based on the observations of past trend over a period of time. In general, the curve the time series data follows has a linear part and a non...

Despite over more than twenty years of research on fuzzy time series forecasting (TSF) and several studies indicating superior performance, an appropriate computationally efficient method have not been developed to predict various time series using fuzzy TSF method. Motivated by this, in this paper a computationally efficient method is proposed to...

This paper presents the performance analysis of a newly developed elitist teaching–learning-based optimization algorithm applied with an efficient higher-order Jordan Pi-sigma neural network (JPSNN) for real-world data classification. Teaching–learning-based optimization (TLBO) algorithm is a recent metaheuristic, which is inspired through the teac...

In the area of neurocognition, classification of data is one of the most important phases. Conventional biologically inspired neural network models such as multilayer perceptrons (MLPs) are capable of learning and generalizing from exemplary patterns and are considered to be a popular choice for many different classification tasks. However, in the...

This paper presents a new hybrid ARIMA-ANN model for time series forecasting. In this model, the time series is first decomposed into low-volatile and high-volatile components using a fuzzy filter. The low-volatile component is modeled using ARIMA and high-volatile component is modeled using ANN. The final prediction is obtained by combining the pr...

Among some of the competent optimization algorithms, nature inspired algorithms are quite popular due to their flexibility and ease of use in diversified domains. Moreover, balancing between exploration and exploitation is one of the important aspects of nature inspired optimizations. In this paper, a recently developed nature inspired algorithm su...

Financial time series forecasting has been regarded as a challenging issue because of successful prediction could yield significant profit, hence require an efficient prediction system. Conventional ANN based models are not competent systems. Higher order neural networks have several advantages over traditional neural networks such as stronger appr...

Over the past few decades, a large literature has evolved to forecast time series using various linear, nonlinear and hybrid linear–nonlinear models. Recently, hybrid models by suitably combining linear models like autoregressive integrated moving average (ARIMA) with nonlinear models like artificial neural network (ANN) have become popular due to...

Fuzzy c-means clustering is one of the popularly used algorithms in various diversified areas of applications due to its ease of implementation and suitability of parameter selection, but it suffers from one major limitation like easy stuck at local optima positions. Particle swarm optimization is a globally adopted metaheuristic technique used to...

Nature-inspired algorithms have evolved as a hot topic of research interest around the globe. Since the last decade, K-means clustering has become an attractive area for researchers towards solving many real-world clustering problems. But, unfortunately K-means does not work well for non-globular clusters. Firefly algorithm is a recently developed...

Hybridization of two or more algorithms has always been a keen interest of research due to the quality of improvement in searching capability. Taking the positive insights of both the algorithms, the developed hybrid algorithm tries to minimize the substantial limitations. Clustering is an unsupervised learning method, which groups the data accordi...

Prediction of stock index remains a challenging task of the financial time series prediction process. Though different non-linear prediction models are in use, their prediction accuracy does not improve beyond certain level. In order to improve the forecasting accuracy, this paper proposes a chemical reaction optimization based virtual data positio...

Variations occur in the trends of financial time series data due to several reasons. Such random fluctuations lead to a sudden fall after a steady increase or a sudden rise after a gradual fall in the trend of financial time series data; this makes it difficult to predict. This research work explores the impact of virtual data positions (VDPs) on f...

This chapter presents two higher order neural networks (HONN) for efficient prediction of stock market behavior. The models include Pi-Sigma, and Sigma-Pi higher order neural network models. Along with the traditional gradient descent learning, how the evolutionary computation technique such as genetic algorithm (GA) can be used effectively for the...

Multilayer neural networks are commonly and frequently used technique for mapping complex nonlinear input-output relationship. However, they add more computational cost due to structural complexity in architecture. This chapter presents different functional link networks (FLN), a class of higher order neural network (HONN). FLNs are capable to hand...

The book presents high quality papers presented at the International Conference on Computational Intelligence in Data Mining (ICCIDM 2016) organized by School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India during December 10 – 11, 2016. The book disseminates the knowledge about innovative, act...

Multilayer neural network based classifiers have been proven their better approximation and generalization ability in medical data classification. However they are characterize with both computational and structural complexities. This article proposes an Evolving Functional Link Network (EFLN) for medical data classification. First, the input signa...

In this paper, an enhanced version of Harmony Search (HS), called Tournament Selective Harmony Search (TSHS) is used to obtain an optimal set of weights for Functional Link Artificial Neural Network (FLANN) with Gradient Descent Learning (GDL) for the task of classification in data mining. The TSHS performs better than HS and Improved HS (IHS) by a...

In this paper, a higher order neural network called Pi-Sigma neural network with an improved Particle swarm optimization has been proposed for data classification. The proposed method is compared with some of the other classifiers like PSO-PSNN, GA-PSNN and only PSNN. Simulation results reveal that, the proposed IPSO-PSNN outperforms others and has...

Higher order neural networks pay more attention due to greater computational capabilities with good learning and storage capacity than the existing traditional neural networks. In this work, a novel attempt has been made for effective optimization of the performance of a higher order neural network (in particular Pi-Sigma neural network) for classi...

This paper proposes a new methodology to optimize trajectory of the path for multi-robots using Improved Gravitational Search Algorithm (IGSA) in a dynamic environment. GSA is improved based on memory information,social, cognitive factor of PSO(Particle swarm optimization) and then,population for next generation is decided by the greedy strategy. A...

This paper proposes a new methodology to optimize trajectory of the path for multi-robots using improved gravitational search algorithm (IGSA) in clutter environment. Classical GSA has been improved in this paper based on the communication and memory characteristics of particle swarm optimization (PSO). IGSA technique is incorporated into the multi...

This paper proposed a novel approach to determine the optimal trajectory of the path for multi-robots in a clutter environment using hybridization of improved particle swarm optimization (IPSO) with differentially perturbed velocity (DV) algorithm. The objective of the algorithm is to minimize the maximum path length that corresponds to minimize th...

Since its inception, Fuzzy c-means (FCM) technique has been widely used in data clustering. The advantages of FCM such as balancing of individual number of cluster points, drifting of small cluster centers to large neighboring cluster centers, and presence of fuzzy factor, make it more popular. However, early trapping at local minima and high sensi...

Successful prediction of stock indices could yield significant profit and hence require an efficient prediction system. Higher order neural networks (HONN) have several advantages over traditional neural networks such as stronger approximation, higher fault tolerance capacity and faster convergence characteristics. This paper proposes an adaptive s...