Journal of Intelligent & Fuzzy Systems - Volume 38, issue 5

Authors: Zhilenkov, Anton A. | Chernyi, Sergei G. | Sokolov, Sergei S. | Nyrkov, Anatoliy P.

Article Type: Research Article

Abstract: The safe and reliable navigation of such autonomous systems as unmanned aerial vehicles (UAV) is a complex open problem in robotics, where a robotic system must simultaneously do many tasks of perception, control and localization. This task is especially complicated when working in an uncontrolled, unpredictable environment, for example, on city streets, in wooded areas, etc. In these cases, the autonomous agent must not only be guided to avoid collisions, but also interact safely with other agents in the environment. The developed system allows navigation of unmanned aerial vehicles in difficult environmental conditions. The results of training and the operation …of the autonomous navigation system in the forest are presented. The system finds and follows the paths that are fairly difficult to distinguish. The results of field experiments are presented. Presentation of the model is presented on the youtube.com channel. Show more

Keywords: Localization, agent, robotic, control

DOI: 10.3233/JIFS-179741

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6619-6625, 2020

Authors: Mor, Navdeep | Sood, Hemant | Goyal, Tripta

Article Type: Research Article

Abstract: Over the last few years, road accidents in developing countries are increasing at an alarming rate. In India, almost 3% of GDP is getting wasted in road accidents, which not only cause social problems but, also, imposes a huge burden on the Indian economy. Various researches have been done to analyze this situation using different methods and techniques on different stretches and intersections. This paper makes one of the first attempts to develop an Accident Prediction Model (APM) in the Indian State of Haryana. This study describes the procedure for collection and analysis of accident data, as well as the …detailed methodology used to develop APMs. The Models were developed using one of the most common algorithms of machine learning i.e. linear regression technique. Results obtained from APM of Haryana State were compared with the results given by some of the highly successful APMs like Smeed’s Model, Valli’s Model and their comparisons were discussed to find the most efficient model. It was observed that the proposed model shows highly accurate results in predicting road accidents in Haryana. The output of this work can be used for theoretical as well as practical applications like road safety management for improving existing conditions of the road network in Haryana and to regulate new traffic safety policies in the future. Show more

Keywords: Accident prediction model, linear regression, road safety, accidents

DOI: 10.3233/JIFS-179742

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6627-6636, 2020

Authors: Shukla, Alok Kumar | Pippal, Sanjeev Kumar | Gupta, Srishti | Ramachandra Reddy, B. | Tripathi, Diwakar

Article Type: Research Article

Abstract: Feature selection is a pre-processing method that identifies the significant features from high-dimensional data and able to diminish the computational cost of the learning algorithm because of removing the irrelevant and redundant features. It has traditionally been applied in a wide range of problems that include biological data processing, pattern recognition, and computer vision. The aim of this paper is to identify the best feature subsets from the benchmark datasets which improve the performance of the classifiers. Existing filter-based feature selection approaches fail to choose the relevant features from the original feature sets. To obtain the tiny subset of relevant …features, we have introduced a novel filter-based feature selection method, called ReCFS. The proposed method is a combination of both feature-feature correlation and nearest neighbor weighted features to find an optimal subset of features to minimize correlation among features. The effectiveness of the selected feature subset by proposed method is evaluated by using two classifiers such as Naïve Bayes and K-Nearest Neighbour on real-life datasets. For the diverse performance measurements, the experiments are conducted on eight real-life datasets of varied dimensionality and number of instances. The result demonstrates that the proposed method has found promising feature subsets which improved the classification accuracy over competing feature selection methods Show more

Keywords: Machine learning, relief-F, correlation feature selection, classification, naïve bayes

DOI: 10.3233/JIFS-179743

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6637-6648, 2020

Authors: Pradhan, Rosy | Majhi, Santosh Kumar | Jaypuria, Jemarani

Article Type: Research Article

Abstract: Moth-Flame optimization is a meta-heuristic algorithm based on the navigation behaviour of moths. Generally, moth’s poses a very effective mechanism called transverse orientation while moving a long distance in night and maintain of fixed angle with respect to the moon. MFO suffers with local optima and stagnation problem, in order to improve the performance and exploration rate of the existing algorithm and for solving the complex real world problems, a new version of MFO algorithms is proposed by adding the concept of orthogonality feature. The modified algorithm is termed as orthogonal Moth-Flame optimization (OMFO) algorithm. The main objective of this …OMFO is going to solve the convergence problem to minimization of the search space and avoid the local optima. The proposed method can also be used to maintain the balance between exploration and exploitation. In this work, a set of 28 standard IEEE CEC 2017 benchmark test functions with 10 and 30 dimensions are used to evaluate and compare between the obtained results which prove that the proposed OMFO gives very promising and competitive performance as well as achieve better performance over original MFO algorithm with high stability over searching method. The efficiency of the proposed method is verified by applying in model order reduction problem. The performed analysis such as statistical measure, convergence analysis and complexity measure reveal that the proposed method is reliable and efficient in solving practical optimization problems. Show more

Keywords: Meta-heuristic, optimization, MFO, orthogonality, model order reduction

DOI: 10.3233/JIFS-179744

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6649-6661, 2020

Authors: Jayachitra Devi, Salam | Singh, Buddha

Article Type: Research Article

Abstract: Link prediction tremendously gained interest in the field of machine learning and data mining due to its real world applicability on various fields such as in social network analysis, biomedicine, e-commerce, scientific community, etc. Several link prediction methods have been developed which mainly focuses on the topological features of the network structure, to figure out the link prediction problem. Here, the main aim of this paper is to perform feature extraction from the given real time complex network using subgraph extraction technique and labeling of the vertices in the subgraph according to the distance from the vertex associated with each …target link. This proposed model helps to learn the topological pattern from the extracted subgraph without using the topological properties of each vertex. The Geodesic distance measure is used in labeling of the vertices in the subgraph. The feature extraction is carried out with different size of the subgraph as K  = 10and K  = 15. Then the features are fit to different machine learning classification model. For the evaluation purpose, area under the ROC curve (AUC) metric is used. Further, comparative analysis of the existing link prediction methods is performed to have a clear picture of their variability in the performance of each network. Later, the experimental results obtained from different machine learning classifiers based on AUC metric have been presented. From the analysis, we can conclude that AdaBoost, Adaptive Logistic Regression, Bagging and Random forest maintain great performance comparatively on all the network. Finally, comparative analysis has been carried out between some best existing methods, and four best classification models, to make visible that link prediction based on classification models works well across several varieties of complex networks and solve the link prediction problem with superior performance and with robustness. Show more

Keywords: Link prediction, geodesic distance, classification model, complex network, data mining

DOI: 10.3233/JIFS-179745

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6663-6675, 2020

Authors: Panda, Nibedan | Majhi, Santosh Kumar | Singh, Sarishma | Khanna, Abhirup

Article Type: Research Article

Abstract: Success behind nature inspired evolutionary metaheuristic algorithms lies in its seemly combination of operator’s castoff for smooth balance between exploration and exploitation. The deficit in such combination leads to untimely convergence of an algorithm, simultaneously failed to attain global optimum by stocking in local optimum. This work represents atypical algorithm termed as OBL-MO-SHO to improve the performance of existing SHO. To deal with more intricate realistic problems and to enhance the explorative and exploitative strength of SHO, we have integrated the oppositional learning concept with mutation operator. The proposed algorithm OBL-MO-SHO (oppositional spotted hyena optimizer with mutation operator) reveals promising …performance in terms of achieving global optimum and superior convergence rate which confirms its improved exploration and exploitation capability within searching region. To establish competency of proposed OBL-MO-SHO algorithm the same is appraised by means of standard functions set belongs to IEEE CEC 2017. The efficacy of said method has been proven by means of various performance metrics and the outcomes also compared with state-of-the-art algorithms. To scrutinize its uniqueness statistically, Friedman and Holms test has been performed as one non-parametric test. Additionally as an application to unravel real world intricate difficulties the said OBL-MO-SHO algorithm has been castoff to train wavelet neural network by considering datasets selected from UCI depository. The reported results unveils that the evolved OBL-MO-SHO might be one potential algorithm for enlightening different optimization difficulties effectively. Show more

Keywords: Swarm intelligence, spotted hyena optimizer, opposition-based learning, mutation operator, optimization, classification, wavelet neural network (WNN)

DOI: 10.3233/JIFS-179746

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6677-6690, 2020

Authors: Bhardwaj, Shubham | Geraldine Bessie Amali, D | Phadke, Amrut | Umadevi, K.S. | Balakrishnan, P.

Article Type: Research Article

Abstract: Metaheuristic algorithms are a family of algorithms that help solve NP-hard problems by providing near-optimal solutions in a reasonable amount of time. Galactic Swarm Optimization (GSO) is the state-of-the-art metaheuristic algorithm that takes inspiration from the motion of stars and galaxies under the influence of gravity. In this paper, a new scalable algorithm is proposed to help overcome the inherent sequential nature of GSO and helps the modified version of the GSO algorithm to utilize the full computing capacity of the hardware efficiently. The modified algorithm includes new features to tackle the problem of training an Artificial Neural Network. The …proposed algorithm is compared with Stochastic Gradient Descent based on performance and accuracy. The algorithm’s performance was evaluated based on per-CPU utilization on multiple platforms. Experimental results have shown that PGSO outperforms GSO and other competitors like PSO in a variety of challenging settings. Show more

Keywords: nature inspired metaheuristic, parallel computation, galactic swarm optimization, artificial neural networks

DOI: 10.3233/JIFS-179747

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6691-6701, 2020

Authors: Rawat, Anuj | Jha, S.K. | Kumar, Bhavnesh | Mohan, Vijay

Article Type: Research Article

Abstract: This paper presents a fractional order nonlinear Proportional Integral Derivative (FONPID) controller to efficiently achieve the Maximum Power Point Tracking (MPPT) in Photovoltaic (PV) systems working under rapidly varying solar intensity and the temperature. In this paper, comparisons have been made among different techniques in respect of the extent of energy extracted from the photovoltaic (PV) system using MATLAB platforms. Gains of the proposed FONPID controllers are optimally tuned using a meta-heuristic based Elitist Teaching Learning Based Optimization (ETLBO) algorithm. The performance assessment of the FONPID controller is made in terms of efficiency, settling time, rise time and ripple. The …ETLBO tuned FONPID controller outperforms the other controller such as PID, Nonlinear PID (NPID), Fractional order PID (FOPID) and perturb and observe (P & O) technique. Therefore, in view of the meticulous investigation it is inferred that the proposed FONPID controller is an emerging MPPT technique with highest tracking efficiency and negligible ripple. Show more

Keywords: Fractional order nonlinear proportional-integral derivative (FONPID), maximum power point tracking (MPPT), elitist teaching learning based optimization (ETLBO)

DOI: 10.3233/JIFS-179748

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6703-6713, 2020

Authors: Chauhan, Urvashi | Singh, Vijander | Kumar, Bhavnesh | Rani, Asha

Article Type: Research Article

Abstract: This article proposes an improved multiverse optimization (IMVO) assisted maximum power point tacking (MPPT) algorithm for attaining maximum global power from photovoltaic system under partial shading condition. The proposed control scheme overcomes the difficulties occurring in traditional MPPT algorithms such as difficulty in attaining global maximum power under partial shading condition and incapability of handling oscillations in power at maximum power point. The algorithm is an amalgamation of IMVO and direct duty cycle control approach. The wormhole existence probability and time distance ratio are considered to be adaptive in improved MVO so as to ensure precise exploration and exploitation. In …this work multi crystal solar panel, KC130GT by M/S Kyocera, is analyzed for dynamic profiles of irradiance. Traditional P&O MPPT and improved particle swarm optimization MPPT (IPSO MPPT) are also designed for comparative analysis. The suggested IMVO MPPT proves to be superior in terms of power tracking performance, average efficiency and convergence capability as compared to other designed controllers. Show more

Keywords: Solar PV system, MPPT, multi verse optimization, improved multi verse optimization

DOI: 10.3233/JIFS-179749

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6715-6726, 2020

Authors: Nangrani, S.P. | Joshi, K.D.

Article Type: Research Article

Abstract: Engineering systems are nowadays expanding beyond expected limits and their complexity is also increasing. One of the largest integrated system is power system. Some well-designed power system experience strange situation and suffer through chaos owing to weak dynamic performance. Stability issues also haunt power system in such cases. Loss of stability investigation in power system leads to evidence of chaos as intermediate state quite often. Black swan theory tells us to be ready for unseen unruly behavior at any time. Noah and Joseph effects are also surfacing in almost every large expanding power system from different parts of world. Complexity …of system component behavior and complex stability boundaries pose a threat and ready to push power system where chaos is prevalent. It is debatable to see whether chaos leads to instability. This paper closely summarizes the chaos studies in light of reported research and advocates strongly the inclusion of advancement in chaos theory for detailed investigation post disturbance. This paper deals with a comprehensive review of strange behavior of nonlinear dynamic system and relevance of such studies for future anomalous behavior in the light of complexity science applied to engineering disasters such as blackouts. It is targeted to provide direction for futuristic complexity arising due to working on the brink of instability for economic reasons and to have certain preparation before inevitable blackouts, disasters and failures. A classified list of more than 50 relevant research publications is also given for quick reference. Show more

Keywords: Lyapunov exponent (LE), single machine infinite bus (SMIB), dynamic stability assessment (DSA)

DOI: 10.3233/JIFS-179750

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6727-6737, 2020