Journal of Intelligent & Fuzzy Systems - Volume 42, issue 2

Authors: Goel, Sonia | Tushir, Meena

Article Type: Research Article

Abstract: Semi-supervised clustering technique partitions the unlabeled data based on prior knowledge of labeled data. Most of the semi-supervised clustering algorithms exist only for the clustering of complete data, i.e., the data sets with no missing features. In this paper, an effort has been made to check the effectiveness of semi-supervised clustering when applied to incomplete data sets. The novelty of this approach is that it considers the missing features along with available knowledge (labels) of the data set. The linear interpolation imputation technique initially imputes the missing features of the data set, thus completing the data set. A semi-supervised clustering …is now employed on this complete data set, and missing features are regularly updated within the clustering process. In the proposed work, the labeled percentage range used is 30, 40, 50, and 60% of the total data. Data is further altered by arbitrarily eliminating certain features of its components, which makes the data incomplete with partial labeling. The proposed algorithm utilizes both labeled and unlabeled data, along with certain missing values in the data. The proposed algorithm is evaluated using three performance indices, namely the misclassification rate, random index metric, and error rate. Despite the additional missing features, the proposed algorithm has been successfully implemented on real data sets and showed better/competing results than well-known standard semi-supervised clustering methods. Show more

Keywords: Semi-supervised clustering, labeled and unlabeled data, incomplete data, and interpolation

DOI: 10.3233/JIFS-189744

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 727-739, 2022

Authors: Azeem, Abdul | Malik, Hasmat | Jamil, Majid

Article Type: Research Article

Abstract: This paper proposed a hybrid intelligent approach based on empirical mode decomposition (EMD), artificial neural network (ANN) and J48 algorithm of machine learning for real-time harmonics analysis of digital substation’s equipment based on IEC-61850 using explanatory input variables based on laboratory proto-type real-time recorded database. In the proposed hybrid model, these variables are first extracted then diagnostic of power transformer harmonics of digital substation is evaluated/analyzed to perform the long term as well as the short term goal and planning in the electrical power network. In this paper, firstly, experimental analysis is performed to validate the laboratory prototype setup using …FFT (fast Fourier transform), STFT (short-time Fourier transform) and CWT (continuous wavelet transform). Then, features are extracted from experimental dataset using EMD (empirical mode decomposition) method. The IMFs (intrinsic mode functions) have generated from EMD, which are used as an input variable to the two different diagnostic models, i.e., ANN and J48 algorithm. In order to validate the performance and accuracy of the proposed hybrid model, a comparative analysis is performed by using ANN and J48 method (with and without EMD method) and the results are compared. Obtained results shows that the proposed hybrid diagnostics approach for harmonics analysis has outperformance characteristics. Show more

Keywords: ANN, explanatory feature, J48 algorithm, EMD, IEC-61850, feature extraction, digital substation, real-time, harmonics, power transformer, diagnosis, incipient level

DOI: 10.3233/JIFS-189745

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 741-754, 2022

Authors: Ray, Papia | Salkuti, Surender Reddy | Biswal, Monalisa

Article Type: Research Article

Abstract: In this paper, two accurate hybrid islanding detection schemes are proposed based on Wavelet Transform and Stockwell transform (S-transform). The proposed methods use the potential of sequence voltage (negative) retrieved at the target Distributed Generation (DG) location of the distribution network under study. In one of the schemes, Discrete Wavelet transform (DWT) is applied to process the negative sequence voltage signal and for its decomposition, which is further used to extract six statistical features like energy, entropy, mean, kurtosis, standard deviation, and skewness from the reconstructed DWT coefficients. Test and train data sets are generated with the wide variation of …loading conditions, and optimal features are chosen from the full feature set by forward feature selection method (FFS) during the training process by an artificial neural network (ANN). After that, the trained system is tested to get the detection result. Another scheme presented in this paper for islanding detection is based on S-transform, which is used to decompose the negative sequence voltage signal. Amplitude, frequency, and phase are the three coefficients acquired from the pre-processing of the raw signal by S-transform. Then the cumulative sums of the energy content of the S-transform coefficients are determined and are compared with a threshold value to get the detection result. The proposed schemes are tested in a distribution network consisting of two 9 MW wind farm driven by six 1.5 MW wind turbine connected to 120 kV main grid through a 25 kV, 30 km feeder. Several cases have been investigated like normal condition, islanding, DG line trip, disconnection of point of common coupling, and sudden change in load to test the performance of the proposed schemes. It can be observed from the results that both the approaches gave high accuracy in the detection of islanding conditions and demarcates properly from the non-islanding state. However, results show that the S-transform based approach provides a better resolution and quick detection of islanding than the wavelet transform approach. Show more

Keywords: Artificial neural network, islanding detection, wavelet transforms, distributed generation, S-transform

DOI: 10.3233/JIFS-189746

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 755-766, 2022

Authors: Gautam, Abhinav K. | Tariq, Mohd | Verma, Kripa Shankar | Pandey, Jai Prakash

Article Type: Research Article

Abstract: A Maximum Power Tracking Technique (MPPT) for Photovoltaic Powered e-Vehicles via Black Widow Optimization Technique is introduced. The proposed system addresses the problems of conventional MPPT methods via a black widow spider-inspired optimization approach. As a result, the design would require fewer iterations to achieve prime conditions, thus increasing the complete efficiency of the proposed system. Field-oriented control (FOC) is used for speed control of the BLDC engine (e-vehicle). The proposed model was first designed, and then simulated in MATLAB environment. The simulink results run in parallel with the Typhoon HIL 402 setup. The results obtained the superior performance of …the BWO-based MPPT technique. Details of the modeling of a new MPPT used for PV-driven BLDC-based e-vehicles are also discussed in this paper. There are many factors involved in a real situation for poor efficiencies, such as shade, irregular sunlight, and weather conditions, which show the non-linear characteristics of PV. The MPPT approach discussed in this article may be used to increase overall productivity and minimize costs for the operation of e-vehicles based on the PV framework. Show more

Keywords: MPPT, BWO, electric vehicle, BLDC, battery, VSI, boost converter

DOI: 10.3233/JIFS-189747

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 767-777, 2022

Authors: Fatema, Nuzhat | Farkoush, Saeid Gholami | Hasan, Mashhood | Malik, H

Article Type: Research Article

Abstract: In this paper, a novel hybrid approach for deterministic and probabilistic occupancy detection is proposed with a novel heuristic optimization and Back-Propagation (BP) based algorithms. Generally, PB based neural network (BPNN) suffers with the optimal value of weight, bias, trapping problem in local minima and sluggish convergence rate. In this paper, the GSA (Gravitational Search Algorithm) is implemented as a new training technique for BPNN is order to enhance the performance of the BPNN algorithm by decreasing the problem of trapping in local minima, enhance the convergence rate and optimize the weight and bias value to reduce the overall error. …The experimental results of BPNN with and without GSA are demonstrated and presented for fair comparison and adoptability. The demonstrated results show that BPNNGSA has outperformance for training and testing phase in form of enhancement of processing speed, convergence rate and avoiding the trapping problem of standard BPNN. The whole study is analyzed and demonstrated by using R language open access platform. The proposed approach is validated with different hidden-layer neurons for both experimental studies based on BPNN and BPNNGSA. Show more

Keywords: Gravitational search algorithm, back-propagation algorithm, neural network, machine learning, optimization, occupancy, smart building

DOI: 10.3233/JIFS-189748

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 779-791, 2022

Authors: Anees, Mohd. Anas | Tariq, Mohd | Lodi, Kaif Ahmed | Alam, Mahetab | Chakrabortty, Ripon K. | Ryan, Michael J.

Article Type: Research Article

Abstract: This paper proposes a model predictive control strategy for 15 level Packed-U-Cell inverter that satisfies multiple-objectives of low current total harmonic distortion (THD), capacitor voltage balances, supply of desired active and reactive power, as well as lower switching and lower voltage stresses on the switching devices. The proposed device performs well under dynamic conditions and can successfully track the current command during step changes in the power demand. A detailed modeling is presented and discussed. MATLAB/Simulink is used for obtaining the simulation results, and the results are validated in the real time by using a hardware-in-the-loop (HIL) Typhoon 402 real-time …emulator. Show more

Keywords: Model predictive control, packed-U-Cell, reactive power compensation, multilevel inverter

DOI: 10.3233/JIFS-189749

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 793-806, 2022

Authors: Pervez, Imran | Sarwar, Adil | Alam, Afroz | Tariq, Mohd | Chakrabortty, Ripon K. | Ryan, Michael J.

Article Type: Research Article

Abstract: Due to its clean and abundant availability, solar energy is popular as a source to generate electricity. Solar photovoltaic (PV) technology converts sunlight incident on the solar PV panel or array directly into non-linear DC electricity. However, the non-linear nature of the solar panels’ power needs to be tracked for its efficient utilization. The problem of non-linearity becomes more prominent when the solar PV array is shaded, even leading to high power losses and concentrated heating in some areas (hotspot condition) of the PV array. Bypass diodes used to eliminate the shading effect cause multiple peaks of power on the …power versus voltage (P-V) curve and make the tracking problem quite complex. Conventional algorithms to track the optimal power point cannot search the complete P-V curve and often become trapped in local optima. More recently, metaheuristic algorithms have been employed for maximum power point tracking. Being stochastic, these algorithms explore the complete search area, thereby eliminating any chance of becoming trapped stuck in local optima. This paper proposes a hybridized version of two metaheuristic algorithms, Radial Movement Optimization and teaching-learning based optimization (RMOTLBO). The algorithm has been discussed in detail and applied to multiple shading patterns in a solar PV generation system. It successfully tracks the maximum power point (MPP) in a lesser amount of time and lesser fluctuations. Show more

Keywords: Maximum power point tracking, metaheuristic algorithms, partial shading, photovoltaic

DOI: 10.3233/JIFS-189750

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 807-816, 2022

Authors: Riyaz, Ahmed | Sadhu, Pradip Kumar | Iqbal, Atif | Tariq, Mohd

Article Type: Research Article

Abstract: The most installed Renewable Energy Sources (RES) in micro-grids (MG) are Photovoltaic (PV) power and wind power. Due to the intermittent behaviour of renewable sources, parallel operation of RES and battery storage known as hybrid system is important particularly in remote micro-grids to reduce the fuel consumption by diesel generators and continuity of supply to the load. In this paper, multilevel inverter called Packed E-Cell (PEC) is used for parallel operation of RES and battery storage optimally for micro-grid applications. The PEC requires less components compared to other Multi-level inverters (MLI) topology with relatively low total harmonic distortion (THD). Further, …selective harmonic technique based on optimization principle is used to enhance the harmonic profile using low frequency switching technique. The 3rd and 5th harmonics are eliminated using Genetic Algorithm (GA) optimization technique. The simulation-based analysis is done using Simulink/MATLAB and the results obtained for THD in the output current and voltage are presented and discussed in the paper. A comparative analysis is also presented with high frequency modulation technique phase disposition pulse width modulation (PDPWM) technique. The experimental validation of the proposed scheme is done using Typhoon HIL (hardware in loop). Show more

Keywords: Renewable energy sources (RES), packed E-Cell (PEC), genetic algorithm (GA), total harmonic distortion (THD), selective harmonic elimination (SHE)

DOI: 10.3233/JIFS-189751

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 817-825, 2022

Authors: Malik, Hasmat | Ahmad, Md Waseem | Alotaibi, Majed A. | Almutairi, Abdulaziz

Article Type: Research Article

Abstract: PMU can directly measure positive sequence voltage, phase and system frequency. In this paper, the design and implementation for optimum placement of PMU in power system network (PSN) has been performed using 5 different intelligent approaches at an emulation platform. Different case studies based on IEEE 7, 14 and 30 bus system have been performed and analyzed. In the studies, PMU device is used for the measurement of voltage and current magnitude as well as its phase and its performance has been compared with measured real signals of PSN. PMU measurement gives the accurate results and reliability to PSN. But …PMUs are not economical, so PSN operator needs to install a minimum number of PMU in PSN so that system should be fully observable in a real-time scenario. In this paper for optimal placement of PMU, five different intelligent methods have been analyzed for three different bus systems and obtained results are compared. For the further validation of selected PMUs for the PSN, a state estimation using WLS algorithm has been performed using conventional data and PMU data on IEEE14 and IEEE30 bus systems. The obtained results for voltage estimation error and phase estimation error with and without PMU data are compared. Show more

Keywords: Condition monitoring, PMU, placement, wide area monitoring, smart grid

DOI: 10.3233/JIFS-189752

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 827-839, 2022

Authors: kaur, Surinder | Chaudhary, Gopal | Dinesh kumar, Javalkar

Article Type: Research Article

Abstract: Nowadays, Biometric systems are prevalent for personal recognition. But due to pandemic COVID 19, it is difficult to pursue a touch-based biometric system. To encourage a touchless biometric system, a less constrained multimodal personal identification system using palmprint and dorsal hand vein is presented. Hand based Touchless recognition system gives a higher user-friendly system and avoids the spread of coronavirus. A method using Convolution Neural Networks(CNN) to extract discriminative features from the data samples is proposed. A pre-trained function PCANeT is used in the experiments to show the performance of the system in fusion scheme. This method doesn’t require keeping …the palm in a specific position or at a certain distance like most other papers. Different patches of ROI are used at two different layers of CNN. Fusion of palmprint and dorsal hand vein is done for final result matching. Both Feature level and score level fusion methods are compared. Results shows the accuracy of upto 98.55% and 98.86% and Equal error rate (EER) of upto 1.22% and 0.93% for score level fusion and feature level fusion, respectively. Our method gives higher accurate results in a less constrained environment. Show more

Keywords: Biometrics, deep learning, feature level fusion, fusion, score level fusion

DOI: 10.3233/JIFS-189753

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 841-849, 2022

Authors: Asim, Mohammed | Agrawal, Piyush | Tariq, Mohd | Alamri, Basem

Article Type: Research Article

Abstract: Under partial shading conditions (PSC), most traditional maximum power point tracking (MPPT) techniques may not adopt GP (global peak). These strategies also often take a considerable amount of time to reach a full power point (MPP). Such obstacles can be eliminated by the use of metaheuristic strategies. This paper shows, in partial shading conditions, the MPPT technique for the photovoltaic system using the Bat Algorithm (BA). Simulations have been performed in the MATLAB® /Simulink setting to verify the efficacy of the proposed method. In MPPT applications, the results of the simulations emphasize the precision of the proposed technique. The algorithm …is also simple and efficient, on a low-cost microcontroller, it could be implemented. Hardware in Loop (HIL) validation is performed, with a Typhoon HIL 402 setup. Show more

Keywords: Photovoltaic system, partial shading conditions, maximum power point tracking, Bat algorithm

DOI: 10.3233/JIFS-189754

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 851-859, 2022

Authors: Sarita, Kumari | Devarapalli, Ramesh | Kumar, Sanjeev | Malik, H. | García Márquez, Fausto Pedro | Rai, Pankaj

Article Type: Research Article

Abstract: Online condition monitoring and predictive maintenance are crucial for the safe operation of equipments. This paper highlights an unsupervised statistical algorithm based on principal component analysis (PCA) for the predictive maintenance of industrial induced draft (ID) fan. The high vibration issues in ID fans cause the failure of the impellers and, sometimes, the complete breakdown of the fan-motor system. The condition monitoring system of the equipment should be reliable and avoid such a sudden breakdown or faults in the equipment. The proposed technique predicts the fault of the ID fan-motor system, being applicable for other rotating industrial equipment, and also …for which the failure data, or historical data, is not available. The major problem in the industry is the monitoring of each and every machinery individually. To avoid this problem, three identical ID fans are monitored together using the proposed technique. This helps in the prediction of the faulty part and also the time left for the complete breakdown of the fan-motor system. This helps in forecasting the maintenance schedule for the equipment before breakdown. From the results, it is observed that the PCA-based technique is a good fit for early fault detection and getting alarmed under fault condition as compared with the conventional methods, including signal trend and fast Fourier transform (FFT) analysis. Show more

Keywords: Machine learning, industry 4.0, PCA, condition monitoring, predictive maintenance, preprocessing

DOI: 10.3233/JIFS-189755

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 861-872, 2022

Authors: Alzubi, Omar A.

Article Type: Research Article

Abstract: Industrial Wireless Sensor Network (IWSN) includes numerous sensor nodes that collect data about target objects and transmit to sink nodes (SN). During data transmission among nodes, intrusion detection is carried to improve data security and privacy. Intrusion detection system (IDS) examines the network for intrusions based on user activities. Several works have been done in the field of intrusion detection and different measures are carried out to increase data security from the issues related to black hole, Sybil attack, Worm hole, identity replication attack and etc. In various existing approaches, secure data transmission is not achieved, therefore resulted in compromising …the security and privacy of IWSNs. Accurate intrusion detection is still challenging task in terms of improving security and intrusion detection rate. In order to improve intrusion detection rate (IDR) with minimum time, generalized Frechet Hyperbolic Deep and Dirichlet Secured (FHD-DS) data communication model is introduced. At first, Frechet Hyperbolic Deep Traffic (FHDT) feature extraction method is designed to extract more relevant network activities and inherent traffic features. With the help of extracted features, anomalous or normal data is predicted. Followed by Statistical Dirichlet Anomaly-based Intrusion Detection model is applied to discover intrusion. Here, Dirichlet distribution is evaluated to attain secure data transmission and significantly detect intrusions in WSNs. Experimental evaluation is carried out with KDD cup 99 dataset on factors such as IDR, intrusion detection time (IDT) and data delivery rate (DDR). The observed results show that the generalized FHD-DS data communication method achieves higher IDR with minimum time. Show more

Keywords: Deep learning, intrusion detection, industrial wireless sensor networks, IWSN security, Fréchet hyperbolic, statistical dirichlet distribution, machine learning, security

DOI: 10.3233/JIFS-189756

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 873-883, 2022

Authors: Alsaidan, Ibrahim | Rizwan, Mohammad | Alaraj, Muhannad

Article Type: Research Article

Abstract: The rapid advancements in the technology, increase in comfort levels, movement of population to urban areas, depletion of fossil fuels and increasing greenhouse gas emissions have invigorated the use of renewable energy resources for power generation in the last few years. The major renewable energy resources which have potential to fulfill the requirements includes solar energy, wind energy, small hydro and biomass etc. Among these major resources, solar energy-based technology is considered as one of the fastest growing technology because of its various advantages and ubiquitous availability of the resources. However, there are certain challenges in the utilization of solar …energy for power generation because of various uncertainties in the atmosphere. As a result, the power generated from solar based power plants is fluctuating in nature which is not desirable. Therefore, the utilities are adopting the smart grid approach which has ability to integrate the solar power plants efficiently and the solar energy forecasting is one of the essential tools for this new model. In this paper, AI based techniques are utilized to forecast solar energy using high quality measured solar irradiance data. The forecasting accuracy of the developed models is evaluated based on statistical indices such as absolute relative error and mean absolute percentage error. The results obtained from the developed models are compared to observe the forecasting ability and performance with the high-quality measured data and found accurate. Show more

Keywords: Artificial intelligence techniques, solar energy forecasting, smart energy management, intelligent systems, sustainable power generation

DOI: 10.3233/JIFS-189757

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 885-896, 2022

Authors: Bisht, Vimal Singh | Hasan, Mashhood | Malik, Hasmat | Sunori, Sandeep

Article Type: Research Article

Abstract: For estimation of the RUL (Remaining useful life) of Lithium ion battery we are required to do its health assessment using online facilities. For identifying the health of a battery its internal resistance and storage capacity plays the major role. However the estimation of both these parameters is not an easy job and requires lot of computational work to be done. So to overcome this constraint an easy alternate way is simulated in the paper through which we can estimate the RUL. For formation of a linear relationship between health index of the battery (HI) and its actual capacity used …of power transformation method is done and later on to validate the result a comparison study is done with Pearson & Spearman methods. Transformed value of Health Index is used for developing a neural network. The results demonstrated in the paper shows the feasibility of the proposed technique resulting in great saving of time Show more

Keywords: Remaining-useful-life, health indicator, lithium-Ion battery, Box-Cox, data-driven

DOI: 10.3233/JIFS-189758

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 897-907, 2022

Authors: Malik, Shaily | Bansal, Poonam

Article Type: Research Article

Abstract: The real-world data is multimodal and to classify them by machine learning algorithms, features of both modalities must be transformed into common latent space. The high dimensional common space transformation of features lose their locality information and susceptible to noise. This research article has dealt with this issue of a semantic autoencoder and presents a novel algorithm with distinct mapped features with locality preservation into a commonly hidden space. We call it discriminative regularized semantic autoencoder (DRSAE). It maintains the low dimensional features in the manifold to manage the inter and intra-modality of the data. The data has multi labels, …and these are transformed into an aware feature space. Conditional Principal label space transformation (CPLST) is used for it. With the two-fold proposed algorithm, we achieve a significant improvement in text retrieval form image query and image retrieval from the text query. Show more

Keywords: Semantic autoencoder, hypergraph, twofold validation, cross model retrieval

DOI: 10.3233/JIFS-189759

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 909-917, 2022

Authors: Srivastava, Vishal | Srivastava, Smriti

Article Type: Research Article

Abstract: Ball and beam is a popular benchmark problem in control engineering. Various control strategies have been proposed on ball & beam system in literature, In this paper, hybrid optimization algorithms have been implemented on PID controller to control ball position and beam angle. Hybrid algorithms combine exploration and exploitation ability of individual algorithm and find optimized value of performance index. In this paper, two hybrid algorithms namely PSO-GSA and PSO-GWO are used to tune controller parameters which in turn improve the system performance. Simulation results show effective and efficient improvement in system performance with these hybrid algorithms. To analyze the …performance of these algorithms, time domain parameters and mean square error (MSE) has been taken as performance index. A comparative study of these algorithms with that of individual algorithms namely PSO, GWO, GSA has also been done. Show more

Keywords: Ball and beam, particle swarm optimization (PSO), gravitational search algorithm (GSA), grey wolf optimization (GWO), mean square error (MSE), robustness

DOI: 10.3233/JIFS-189760

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 919-928, 2022

Authors: Gupta, Sangeeta | Varshney, Pragya | Srivastava, Smriti

Article Type: Research Article

Abstract: This paper proposes a scheme to synchronize fractional order chaotic systems employing fractional PID controller. The parameters of FOPID are tuned using Swarm based optimization techniques, viz.: Whale optimization algorithm and Particle swarm optimization techniques. To assert the complete synchronization, master-slave method has been implemented. Chaotic systems are highly dependent upon initial conditions and parameter perturbations. Therefore, taking these properties into consideration, synchronization of two identical fractional order financial chaotic systems is performed with distinct initial conditions. To show the efficacy of the proposed method, analysis is performed for orders between 0 to 1, and also for sensitivity to initial …conditions. Show more

Keywords: Fractional order chaotic system (FOCS), fractional order financial chaotic system (FOFCS), whale optimization algorithm (WOA), particle swarm optimization (PSO), proportional-integral-derivative (PID) controller, fractional order PID (FOPID) controller

DOI: 10.3233/JIFS-189761

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 929-942, 2022

Authors: Tyagi, Shikhar | Chawla, Bhavya | Jain, Rupav | Srivastava, Smriti

Article Type: Research Article

Abstract: Single biometric modalities like facial features and vein patterns despite being reliable characteristics show limitations that restrict them from offering high performance and robustness. Multimodal biometric systems have gained interest due to their ability to overcome the inherent limitations of the underlying single biometric modalities and generally have been shown to improve the overall performance for identification and recognition purposes. This paper proposes highly accurate and robust multimodal biometric identification as well as recognition systems based on fusion of face and finger vein modalities. The feature extraction for both face and finger vein is carried out by exploiting deep convolutional …neural networks. The fusion process involves combining the extracted relevant features from the two modalities at score level. The experimental results over all considered public databases show a significant improvement in terms of identification and recognition accuracy as well as equal error rates. Show more

Keywords: Multimodal biometrics, face, finger vein, convolutional neural network, score level fusion

DOI: 10.3233/JIFS-189762

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 943-955, 2022

Authors: Devarapalli, Ramesh | Venkateswara Rao, B. | Dey, Bishwajit | Vinod Kumar, K. | Malik, H. | García Márquez, Fausto Pedro

Article Type: Research Article

Abstract: Nowadays, improvement in power system performance is essential to obtaine economic and technical benifits. To achieve this, optimize the large number of parameters in the system based on optimal power flow(OPF). For solving OPF problem efficiently, it needs robust and fast optimization techniques. This paper proposes the application of a newly developed hybrid Whale and Sine Cosine optimization algorithm to solve the OPF. It has been implemented for optimization of the control variables. The reduction of true power generation cost, emission, true power losses, and voltage deviation are considered as different objectives. The hybrid Whale and Sine Cosine optimization is …validated by solving OPF problem with various intentions using IEEE30 bus system. To varidate the proposed technique, the results obtained from this are compared with other methods in the literature. The robustness achieved with the proposed algorithm has been analyzed for the considered OPF problem using statistical analysis and whisker plots. Show more

Keywords: Optimal power flow, sine cosine optimization, voltage deviation, whale optimization

DOI: 10.3233/JIFS-189763

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 957-967, 2022

Authors: Bhati, Nitesh Singh | Khari, Manju

Article Type: Research Article

Abstract: With the increase in the amount of data available today, the responsibility of keeping that data safe has also taken a more severe form. To prevent confidential data from getting in the hands of an attacker, some measures need to be taken. Here comes the need for an effective system, which can classify the traffic as an attack or normal. Intrusion Detection Systems can do this work with perfection. Many machine learning algorithms are used to develop efficient IDS. These IDS provide remarkable results. However, ensemble-based IDS using voting have been seen to outperform individual approaches (Support Vector Machine and …ExtraTree). Since the Voting methodology is able to work around both, theoretically similar and different classifiers and produce a single classifier based on the majority characteristics, it proved to be better than the other ensemble based techniques. In this paper, an ensemble IDS implementation is presented based on the voting ensemble method, using the two algorithms, Support Vector Machine (SVC) and ExtraTree. The experiment is performed on the KDDCup99 Dataset. The evaluation of the performance of the proposed method is based on the comparison with an unoptimized implementation of the same. The results based on performing the experiment in Python fetched an accuracy of 99.90%. Show more

Keywords: Security, intrusion detection system, network security, ensemble, voting, machine learning

DOI: 10.3233/JIFS-189764

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 969-979, 2022

Authors: Sarin, Sumit | Mittal, Antriksh | Chugh, Anirudh | Srivastava, Smriti

Article Type: Research Article

Abstract: Person identification using biometric features is an effective method for recognizing and authenticating the identity of a person. Multimodal biometric systems combine different biometric modalities in order to make better predictions as well as for achieving increased robustness. This paper proposes a touchless multimodal person identification model using deep learning techniques by combining the gait and speech modalities. Separate pipelines for both the modalities were developed using Convolutional Neural Networks. The paper also explores various fusion strategies for combining the two pipelines and shows how various metrics get affected with different fusion strategies. Results show that weighted average and product …fusion rules work best for the data used in the experiments. Show more

Keywords: Multimodal, touchless, biometric system, gait recognition, speech recognition

DOI: 10.3233/JIFS-189765

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 981-990, 2022

Authors: Meghana, Pulimamidi | Yammani, Chandrasekhar | Salkuti, Surender Reddy

Article Type: Research Article

Abstract: This paper proposes an energy scheduling mechanism among multiple microgrids (MGs) and also within the individual MGs. In this paper, electric vehicle (EV) energy scheduling is also considered and is integrated in the operation of the microgrid (MG). With the advancements in the battery technologies of EVs, the significance of Vehicle-to-Grid (V2G) is increasing tremendously. So, designing the strategies for energy management of electric vehicles (EVs) is of paramount importance. The battery degradation cost of an EV is also taken into account. Vickrey second price auction is used for truthful bidding. To enhance the security and trust, blockchain technology can …be incorporated. The market is shifted to decentralized state by using blockchain. To encourage the MGs to generate more, contribution index is allotted to each prosumer of a MG and to the MGs as a whole, depending on which priority is given during auction. The system was simulated using IEEE 118 bus feeder which consists of 5 MGs, which in turn contain EVs and prosumers. Show more

Keywords: Blockchain technologies, distributed generators, electric vehicles, green energy, microgrid, vehicle-to-grid

DOI: 10.3233/JIFS-189766

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 991-1002, 2022

Authors: Hasan, Mashhood | Alhazmi, Waleed Hassan | Zakri, Waleed

Article Type: Research Article

Abstract: In this paper, a solar photovoltaic model integrated with brushless DC motor via DC to DC zeta converter is controlled in two stage. In first stage, a fuzzy rule based maximum power point tracking (PPT) is proposed to generate the pulse for DC to DC zeta converter. It is efficient intelligent control approach to extract maximum power from the solar PV system and enhance the speed to track the maximum power. The basic three process of fuzzy logic controller (FLC) are fuzzifier, inference and defuzzifier where the defuzzification process is used center of gravity (COG) method to convert its original …value. The FLC to extract maximum PPT for solar PV based brushless DC motor can be examined the performance under transient and dynamic condition with different solar insolation. Moreover, in second stage a trapezoidal control approach based electronic commutation is chosen to generate the pulses of voltage source inverter (VSI) and it offers the smooth control of the brushless DC motor which can easily applicable for water pumping or irrigation purpose. A second stage, trapezoidal control approach is close loop control algorithm using sensorless drive. The performance of proposed fuzzy rule based control algorithm is shown using simulation results on MATLAB platform. Show more

Keywords: Center of gravity, fuzzy logic controller, fuzzy rule, membership function, solar photovoltaic

DOI: 10.3233/JIFS-189767

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1003-1014, 2022

Authors: Setiawan, Noor Akhmad | Nugroho, Hanung Adi | Persada, Anugerah Galang | Yuwono, Tito | Prasojo, Ipin | Rahmadi, Ridho | Wijaya, Adi

Article Type: Research Article

Abstract: Arrhythmia is an abnormality often encountered in patients with cardiac problems. The presence of arrhythmia can be detected by an electrocardiogram (ECG) test. Automatic observation based on machine learning has been developed for long time. Unfortunately, only few of them have capability of explaining the knowledge inside themselves. Thus, transparency is important to improve human understanding of knowledge. To achieve this goal, a method based on cascaded transparent classifier is proposed. Firstly, ECG signals were separated and every single signal was extracted using feature extraction method. Several of extracted feature’s attributes were selected, and the final step was classifying data …using cascade classifier which consists of decision tree and the rule based classifier. Classification performance was evaluated with publicly available dataset, the MIT-BIH Physionet Dataset. The methods were tested using 10-fold cross validation. The average of both accuracy and number of rules generated was considered. The best result using rule-based classifier achieves the accuracy and the number of rules 92.40% and 40, respectively. And the best result using cascade classifier achieves the accuracy and the number of rules 92.84% and 80, respectively. As a conclusion, transparent classifier shows a competitive performance with reasonable accuracy compared with previous research and promising in addressing the need for interpretability model. Show more

Keywords: Physionet, arrhythmia, cascade, transparent classifier

DOI: 10.3233/JIFS-189768

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1015-1025, 2022

Authors: Srikanth, Pullabhatla | Koley, Chiranjib

Article Type: Research Article

Abstract: In this work, different types of power system faults at various distances have been identified using a novel approach based on Discrete S-Transform clubbed with a Fuzzy decision box. The area under the maximum values of the dilated Gaussian windows in the time-frequency domain has been used as the critical input values to the fuzzy machine. In this work, IEEE-9 and IEEE-14 bus systems have been considered as the test systems for validating the proposed methodology for identification and localization of Power System Faults. The proposed algorithm can identify different power system faults like Asymmetrical Phase Faults, Asymmetrical Ground Faults, …and Symmetrical Phase faults, occurring at 20% to 80% of the transmission line. The study reveals that the variation in distance and type of fault creates a change in time-frequency magnitude in a unique pattern. The method can identify and locate the faulted bus with high accuracy in comparison to SVM. Show more

Keywords: Power system faults, localization, identification, fuzzy logic, signal processing

DOI: 10.3233/JIFS-189769

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1027-1039, 2022

Authors: Venkateswara Rao, B. | Devarapalli, Ramesh | Malik, Hasmat | Bali, Sravana Kumar | García Márquez, Fausto Pedro | Chiranjeevi, Tirumalasetty

Article Type: Research Article

Abstract: The trend of increasing demand creates a gap between generation and load in the field of electrical power systems. This is one of the significant problems for the science, where it require to add new generating units or use of novel automation technology for the better utilization of the existing generating units. The automation technology highly recommends the use of speedy and effective algorithms in optimal parameter adjustment for the system components. So newly developed nature inspired Bat Algorithm (BA) applied to discover the control parameters. In this scenario, this paper considers the minimization of real power generation cost with …emission as an objective. Further, to improve the power system performance and reduction in the emission, two of the thermal plants were replaced with wind power plants. In addition, to boost the voltage profile, Static VAR Compensator (SVC) has been integrated. The proposed case study, i.e., considering wind plant and SVC with BA, is applied on the IEEE30 bus system. Due to the incorporation of wind plants into the system, the emission output is reduced, and with the application of SVC voltage profile improved. Show more

Keywords: Bat algorithm, emission, optimal power flow, SVC, wind power

DOI: 10.3233/JIFS-189770

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1041-1049, 2022

Authors: Singh, Saumya | Srivastava, Smriti

Article Type: Research Article

Abstract: In the field of data analysis clustering is considered to be a major tool. Application of clustering in various field of science, has led to advancement in clustering algorithm. Traditional clustering algorithm have lot of defects, while these defects have been addressed but no clustering algorithm can be considered as superior. A new approach based on Kernel Fuzzy C-means clustering using teaching learning-based optimization algorithm (TLBO-KFCM) is proposed in this paper. Kernel function used in this algorithm improves separation and makes clustering more apprehensive. Teaching learning-based optimization algorithm discussed in the paper helps to improve clustering compactness. Simulation using five …data sets are performed and the results are compared with two other optimization algorithms (genetic algorithm GA and particle swam optimization PSO). Results show that the proposed clustering algorithm has better performance. Another simulation on same set of data is also performed, and clustering results of TLBO-KFCM are compared with teaching learning-based optimization algorithm with Fuzzy C- Means Clustering (TLBO-FCM). Show more

Keywords: Kernel fuzzy C means, TLBO, metaheuristic, multi-objective

DOI: 10.3233/JIFS-189771

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1051-1059, 2022

Authors: Suryakant, | Sreejeth, Mini | Singh, Madhusudan

Article Type: Research Article

Abstract: Detection of the rotor position is an important prerequisite for controlling the speed and developed torque in permanent magnet synchronous motor (PMSM). Even though use of incremental encoder and resolver is one of the popular schemes for sensing the rotor position in a PMSM drive, it increases the size and weight of the drive and reduces its reliability. Dynamic modeling of the motor and control algorithms are often used in sensor-less control of PMSM to estimate rotor position and motor speed. Most sensor-less control algorithms use machine parameters like torque constant, stator inductances and stator resistance for estimating the rotor …position and speed. However, with accuracy of such estimation and the performance of the motor degrades with variation in motor parameters. Model reference adaptive control (MRAC) provides a simple solution to this issue. An improved Adaptive neuro-fuzzy inference system (ANFIS) based MRAC observer for speed control of PMSM drive is presented in this paper. In the proposed method adaptive model and adaptive mechanism are replaced by an improved ANFIS controller, which neutralize the effect of parametric variation and results in improved performance of the drive. The modeling equations of PMSM are used to estimate the rotor position for speed and torque control of the drive. Simulation studies have been carried out under various operating condition using MATLAB/Simulink. In addition, a comparative analysis of the conventional MRAC based observer and improved ANFIS based MRAC observer is carried out. It is observed that the proposed method results in better performance of the PMSM drive. Show more

Keywords: PMSM, space vector PWM (SVPWM), model reference adaptive control, PI controller, adaptive neuro-fuzzy inference system

DOI: 10.3233/JIFS-189772

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1061-1073, 2022

Authors: Kumar, Indrajeet | Bhatt, Chandradeep | Vimal, Vrince | Qamar, Shamimul

Article Type: Research Article

Abstract: The white corpuscles nucleus segmentation from microscopic blood images is major steps to diagnose blood-related diseases. The perfect and speedy segmentation system assists the hematologists to identify the diseases and take appropriate decision for better treatment. Therefore, fully automated white corpuscles nucleus segmentation model using deep convolution neural network, is proposed in the present study. The proposed model uses the combination of ‘binary_cross_entropy’ and ‘adam’ for maintaining learning rate in each network weight. To validate the potential and capability of the above proposed solution, ALL-IDB2 dataset is used. The complete set of images is partitioned into training and testing set …and tedious experimentations have been performed. The best performing model is selected and the obtained training and testing accuracy of best performing model is reported as 98.69 % and 99.02 %, respectively. The staging analysis of proposed model is evaluated using sensitivity, specificity, Jaccard index, dice coefficient, accuracy and structure similarity index. The capability of proposed model is compared with performance of the region-based contour and fuzzy-based level-set method for same set of images and concluded that proposed model method is more accurate and effective for clinical purpose. Show more

Keywords: White corpuscles nucleus segmentation, region-based active contour, fuzzy-based level set method, U-Net model

DOI: 10.3233/JIFS-189773

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1075-1088, 2022

Authors: Gautam, Abhinav K. | Tariq, Mohd | Pandey, Jai Prakash | Verma, Kripa Shankar

Article Type: Research Article

Abstract: In this paper, the authors have addressed the modeling and design of the BLDC Motor-Driven E-Rickshaw based on hybrid energy storage system (HESS) for optimum power management using fuzzy logic. In Hybrid energy sources, solar power is used to charge a battery (primary source) that is effectively coupled to supercapacitor (ancillary source) for peak demand supplies. A power-split control strategy is proposed to control the power supply by using the HESS Fuzzy Logic in different engine operating modes. Projected power layering improves the battery life cycle with the proper use of the Supercapacitor. By providing a new switching algorithm, the …DC link voltage is boosted to effectively transfer power to the HESS unit. Fuzzy logic-based HESS provides better performance in electric vehicles, such as deep discharge protection of the battery, and faster acceleration. Also, there is a quick comparison of E-rickshaw solar power with traditional E-rickshaw. The planned design model was simulated by MATLAB® /Simulink environment. Show more

Keywords: Solar power, battery, optimal power management (OPM), BLDC, E-Rickshaw, fuzzy logic controller (FLC), Supercapacitor

DOI: 10.3233/JIFS-189774

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1089-1098, 2022

Authors: Malik, Hasmat | Alotaibi, Majed A. | Almutairi, Abdulaziz

Article Type: Research Article

Abstract: The electric load forecasting (ELF) is a key area of the modern power system (MPS) applications and also for the virtual power plant (VPP) analysis. The ELF is most prominent for the distinct applications of MPS and VPP such as real-time analysis of energy storage system, distributed energy resources, demand side management and electric vehicles etc. To manage the real-time challenges and map the stable power demand, in different time steps, the ELF is evaluated in yearly, monthly, weekly, daily, and hourly, etc. basis. In this study, an intelligent load predictor which is able to forecast the electric load for …next month or day or hour is proposed. The proposed approach is a hybrid model combining empirical mode decomposition (EMD) and neural network (NN) for multi-step ahead load forecasting. The model performance is demonstrated by suing historical dataset collected form GEFCom2012 and GEFCom2014. For the demonstration of the performance, three case studies are analyzed into two categories. The demonstrated results represents the higher acceptability of the proposed approach with respect to the standard value of MAPE (mean absolute percent error). Show more

Keywords: Feature extraction, decomposition, intelligent data analytics, short-term forecasting, power system planning

DOI: 10.3233/JIFS-189775

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1099-1114, 2022

Authors: Dhingra, Shefali | Bansal, Poonam

Article Type: Research Article

Abstract: Retrieving out the most comparable images from huge databases is the challenging task for image retrieval systems. So, there is a great need of constructing a capable and rigorous image retrieval system. In this implementation, an exclusive and competent Content based image retrieval (CBIR) system is schemed by the integration of Color moment (CM) and Local binary pattern (LBP). A hybrid feature vector is created by the combination of these two techniques through the process of normalization. This hybrid feature vector is given as the input to the intelligent classifiers i.e. Support vector machine (SVM) and Cascade forward back propagation …neural network (CFBPNN). After that, Relevance feedback (RF) technique is applied so as to get the high level information in order to reduce the semantic gap. So, here two Artificial Intelligent CBIR models are proposed, first one is (Hybrid+SVM+RF) and second is (Hybrid+CFBPNN+RF) and their performance parameters are compared. The implementations are performed on two benchmark dataset Corel-1K and Oxford flower dataset which contains 1000 and 1360 images respectively. Different parameters are figured such as accuracy, precision, average retrieval time, recall etc. The average precision obtained for the first model is 93% with Corel 1K database and 91% with Oxford flower database. And similarly for the second model, it is 97% and 94% respectively which is higher than the first model. This implemented technique is validated on both the datasets and the attained results outperforms with other related s approaches. Show more

Keywords: Support vector machine, local binary pattern, color moment, relevance feedback, cascade forward back propagation neural network

DOI: 10.3233/JIFS-189776

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1115-1126, 2022

Authors: Prasad, Dinanath | Kumar, Narendra | Sharma, Rakhi

Article Type: Research Article

Abstract: This paper bestows 3-phase grid interfaced solar-wind hybrid renewable energy system (RES), feeding three-phase loads. The proposed system includes solar photovoltaic, permanent magnet based synchronous generator (PMSG), DC-DC converter, maximum power point tracker (MPPT) based on incremental conductance, three phases IGBT based voltage source converter (VSC), with a third order generalized integrator (TOGI) control technique. This control technique bestows multifunctional capabilities as harmonic mitigations, load balancing, and reactive power compensation. A fundamental component of load current is extracted by TOGI based controller, and further it is utilized to provide switching pulses to VSC for power quality enrichment. The fuzzy logic-based …controller is used for loss computation of VSC as well as for maintaining DC link voltage. Moreover, fuzzy logic provides better dynamic performance compared to conventional PI controller. The results are presented in many aspects for linear and nonlinear loads such as, intermittent nature of solar and wind as well as disturbances in the system. A comparative analysis between proposed TOGI based controller and conventional control algorithm has been presented. Test results are performed by using MATLAB/ Simulink environment and demonstrate, AC-grid current is maintained within the IEEE-519 standard. Show more

Keywords: Third-order generalized integrator (TOGI), SPV array, MPPT, PMSG, fuzzy logic control (FLC)

DOI: 10.3233/JIFS-189777

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1127-1139, 2022

Authors: Sharma, Sachin | Kumar, Vineet | Rana, K.P.S.

Article Type: Research Article

Abstract: Generally, the process industry is affected by unwanted fluctuations in control loops arising due to external interference, components with inherent nonlinearities or aggressively tuned controllers. These oscillations lead to production of substandard products and thus affect the overall profitability of a plant. Hence, timely detection of oscillations is desired for ensuring safety and profitability of the plant. In order to achieve this, a control loop oscillation detection and quantification algorithm using Prony method of infinite impulse response (IIR) filter design and deep neural network (DNN) has been presented in this work. Denominator polynomial coefficients of the obtained IIR filter using …Prony method were used as the feature vector for DNN. Further, DNN is used to confirm the existence of oscillations in the process control loop data. Furthermore, amplitude and frequency of oscillations are also estimated with the help of cross-correlation values, computed between the original signal and estimated error signal. Experimental results confirm that the presented algorithm is capable of detecting the presence of single or multiple oscillations in the control loop data. The proposed algorithm is also able to estimate the frequency and amplitude of detected oscillations with high accuracy. The Proposed method is also compared with support vector machine (SVM) and empirical mode decomposition (EMD) based approach and it is found that proposed method is faster and more accurate than the later. Show more

Keywords: Oscillation detection, Prony method, EMD, IIR filter, deep neural network, cross-correlation, SVM

DOI: 10.3233/JIFS-189778

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1141-1154, 2022

Authors: Fatema, Nuzhat | Malik, Hasmat | Ahmad, Wakeel

Article Type: Research Article

Abstract: It is the need of today’s world, to deliver with quality health care services to meet the health needs of target populations. The healthcare system includes procedures of prevention and screening of all types of diseases, their treatment and diagnostics, recent research and development. These procedures must be maintained at a desired level of excellence, which comes under quality management. Quality management in healthcare incorporates with making of various quality policies, quality planning and assurance, quality control and quality improvement. Quality improvement (QI) is the scheme used for betterment of the services delivered to the patients, such as diagnosis and …treatment. If these schemes are recent and advanced technology based, services provided would be cost effective, accurate, less time consuming and hassle-free for both healthcare provider as well as patients. In this study we are applying artificial intelligent and machine learning techniques to enhance the diagnosis accuracy of the liver fibrosis which is caused by hepatitis C virus (HCV). Generally, the SLBs (serial liver biopsies) are utilized to diagnose the liver fibrosis levels (LFLs), which is the gold standard method in this domain. However, SLB has various impediment and not appropriate to the patients which leads to higher prognosis cost with invasive way. So, there is a big research gap in the medical field to find out the alternative non-invasive approach/method for SLB. The proposed data-driven intelligent model for identification of liver fibrosis using hybrid approach is designed and implemented to overcome the SLBs problems with higher diagnostic accuracy. The empirical mode decomposition (EMD) approach is used to extract the IMFs (intrinsic mode functions), which are used as input features to the ANN-J48 algorithm based intelligent classifiers. The proposed approach shows the evidence for utilization in a non-invasive way to diagnose the LFLs without high level clinical expert skills. Show more

Keywords: Quality management, data-driven, hybrid intelligent model, EMD, ANN, J48 algorithm, decision tree, machine learning, liver fibrosis, Hepatitis

DOI: 10.3233/JIFS-189779

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1155-1169, 2022

Authors: Srikanth, Pullabhatla | Koley, Chiranjib

Article Type: Research Article

Abstract: A convolution neural network (CNN) based deep learning method has been proposed for automatic classification and localization of nonlinear loads present in an interconnected power system. The identification of nonlinear loads has been previously dealt with the use of Nonlinear Auto Regression neural network with eXogenous inputs (NARX), Backpropagation Neural Network (BPNN), Probabilistic Neural Network (PNN), Artificial Neural Networks (ANN) and Fuzzy Logic (FL). However, these techniques had not explored the area of classification of industrial and domestic nonlinear loads in an interconnected power system. Also, a Deep learning-based solution for identification of the type of nonlinear load has not …been reported in the literature to date. Hence, to address these shortcomings, an IEEE-9 Bus system with industrial nonlinear loads has been used to obtain various current waveforms with distortions. The recorded current waveforms are transformed into a time-frequency (TF) domain plane, and the obtained images are then fed to the deep learning algorithm. The colored images of the TF plots of each type of nonlinear load in Red-Green-Blue (RGB) index provide the best visual features for extraction. The TF domain signatures of individual events are scaled to a standard size before feeding to the algorithm. Through these TF signatures, unique features were extracted with the deep learning algorithm, and then passed on to different stages of convolution and max-pooling with fully connected layers. The softmax classifier at the end classifies the input data into the type of nonlinear present in the power system. The algorithm, when run at different buses, also identifies the location of the nonlinear load. The proposed methodology avoids the usage of any additional fusion layer for obtaining unique features, reduces the training time and maintains the highest accuracy of 100%. Show more

Keywords: Nonlinear loads, localization, identification, deep learning, time-frequency representation

DOI: 10.3233/JIFS-189780

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1171-1184, 2022

Authors: Kumar, Neeraj | Tripathi, M.M.

Article Type: Research Article

Abstract:  Penetration of renewable energy resources into grid is necessary to meet the elevated demand of electricity. In view of this penetration of solar and wind power increasing immensely across the globe. Solar energy is widely expanding in terms of generation and capacity addition due its better predictability over wind energy. Electricity pricing is one of the important aspects for power system planning and it felicitates information for the electricity bidder for accurate electricity generation and resource allocation. The important task is to forecast the electricity price accurately in grid interactive environment. This task is tedious in renewable integrated market due …to intermittency issue. In this paper, investigation has been done on the effect of solar energy generation on electricity price forecasting. Different state of the art Machine learning (ML) models have been applied and compared with LSTM model for electricity price forecasting and the evaluation of the impact of solar energy generation on electricity price has been done. During the investigation it was found from the results that the LSTM model outperform all other models and impact of solar energy generation on electricity price is evaluated using forecasting metrics. The forecasted electricity price considering the factor of solar energy generation was lower as compared with the forecast without solar energy generation. The reliability test of the MAPE values has been performed by calculating confidence interval for proposed model. Show more

Keywords: Price forecasting, renewable energy, LSTM, LASSO, decision tree, random forest, XGBoost

DOI: 10.3233/JIFS-189781

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1185-1197, 2022

Authors: Ajith Kumar, S.P. | Banyal, Siddhant | Bhardwaj, Kartik Krishna | Thakur, Hardeo Kumar | Sharma, Deepak Kumar

Article Type: Research Article

Abstract:  Opportunistic IoT networks operate in an intermittent, mobile communication topology, employing peer-to-peer transmission hops on a store-carry-forward basis. Such a network suffers from intermittent connectivity, lack of end-to-end route definition, resource constraints and uncertainties arising from a dynamic topology, given the mobility of participating nodes. Machine learning is an instrumental tool for learning and many histories-based machine learning paradigms like MLPROPH, KNNR and GMMR have been proposed for digital transformations in the field with varying degrees of success. This paper explores the dynamic topology with a plethora of characteristics guiding the node interactions, and consequently, the routing decisions. Further, the …study ascertains the need for better representation of the versatility of node characteristics that guide their behavior. The proposed scheme Opportunistic Fuzzy Clustering Routing (OFCR) protocol employs a three-tiered intelligent fuzzy clustering-based paradigm that allows representation of multiple properties of a single entity and the degree of association of the entity with each property group that it is represented by. Such quantification of the extent of association allows OFCR a proper representation of multiple node characteristics, allowing a better judgement for message routing decisions based on these characteristics. OFCR performed 33.77%, 6.07%, 3.69%, 6.88% and 78.14% better than KNNR, GMMR, CAML, MLPRoPH and HBPR respectively across Message Delivery probability. OFCR, not only shows improved performance from the compared protocols but also shows relatively more consistency across the change in simulation time, message TTL and message generation interval across performance metrics. Show more

Keywords: Analytical models, clustering, fuzzy logic, Internet of Things, opportunistic networks, routing protocols, machine learning, ONE simulator

DOI: 10.3233/JIFS-189782

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1199-1211, 2022

Authors: Malik, Hasmat | Alotaibi, Majed A. | Almutairi, Abdulaziz

Article Type: Research Article

Abstract: Maintaining the reliable, efficient, secure and multifunctional IEC 61850 based substation is an extremely challenging task, especially in the ever-evolving cyberattacks domain. This challenge is also exacerbated with expending the modern power system (MPS) to meet the demand along with growing availability of hacking tools in the hacker community. Few of the most serious threats in the substation automation system (SAS) are DoS (Denial of Services), MS (Message Suppression) and DM (Data Manipulation) attacks, where DoS is due to flood bogus frames. In MS, hacker inject the GOOSE sequence (sqNum) and GOOSE status (stNum) number. In the DM attacks, attacker …modify current measurements reported by the merging units, inject modified boolean value of circuit breaker and replay a previously valid message. In this paper, an intelligent cyberattacks identification approach in IEC 61850 based SAS using PSVM (proximal support vector machine) is proposed. The performance of the proposed approach is demonstrated using experimental dataset of recorded signatures. The obtained results of the demonstrated study shows the effectiveness and high level of acceptability for real side implementation to protect the SAS from the cyberattacks in different scenarios. Show more

Keywords: False data injection, Man-In-The-Middle, intrusion detection system, GOOSE, MMS, SVM, information and communication technologies, substation automation system, telephone switching based remote control unit, digital communication network

DOI: 10.3233/JIFS-189783

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1213-1222, 2022

Authors: Virk, Jitender Singh | Singh, Mandeep | Singh, Mandeep | Panjwani, Usha | Ray, Koshik

Article Type: Research Article

Abstract:  Most of the people who do not take required sleep are prone to sleep-deprived mental fatigue. This mental fatigue due to sleep deprivation is very harmful to persons involved in critical jobs like Pilots, Surgeons, Air traffic controllers and others. The present research paper proposes an intelligent method based on re-enforced learning, followed by classification supported by the adaptive threshold. Moreover, the method proposed by us is non-intrusive, in which the subject is unaware of being monitored during the test; it helps prevent biased results. The novelty lies in the use of the Inter-frame interval of an open and close …eye for feature extraction that leads to the detection of “Alertness” or “Fatigue” based on the adaptive threshold. The proposed self-learning framework is real-time in nature and has a detection accuracy of 97.5 %. Since the method is self-learning, as the size of the data set increases, its accuracy and sensitivity are likely to increase further. Show more

Keywords: Alertness, computer vision, self-learning, visual cues

DOI: 10.3233/JIFS-189784

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1223-1233, 2022

Authors: Fatema, Nuzhat | Malik, Hasmat | Abd Halim, Mutia Sobihah

Article Type: Research Article

Abstract: This paper proposed a hybrid intelligent approach based on empirical mode decomposition (EMD), autoregressive integrated moving average (ARIMA) and Monte Carlo simulation (MCS) methods for multi-step ahead medical tourism (MT) forecasting using explanatory input variables based on two decade real-time recorded database. In the proposed hybrid model, these variables are 1st extracted then medical tourism is forecasted to perform the long term as well as the short term goal and planning in the nation. The multi-step ahead medical tourism is forecasted recursively, by utilizing the 1st forecasted value as the input variable to generate the next forecasting value and this …procedure is continued till third step ahead forecasted value. The proposed approach is firstly tested and validated by using international tourism arrival (ITA) dataset then proposed approach is implemented for forecasting of medical tourism arrival in nation. In order to validate the performance and accuracy of the proposed hybrid model, a comparative analysis is performed by using Monte Carlo method and the results are compared. Obtained results show that the proposed hybrid forecasting approach for medical tourism has outperforming characteristics. Show more

Keywords: ARIMA model, explanatory feature, multi-step ahead, medical tourism forecasting, Monte Carlo simulation, feature extraction

DOI: 10.3233/JIFS-189785

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1235-1251, 2022

Authors: Alzubi, Jafar A. | Jain, Rachna | Alzubi, Omar | Thareja, Anuj | Upadhyay, Yash

Article Type: Research Article

Abstract: The availability of techniques for driver distraction detection has been difficult to put to use because of delays caused due to lag in inferencing the model. Distractions caused due to handheld devices have been major causes of traffic accidents as they affect the decision-making capabilities of the driver and gives them less time to react to difficult situations. Often drivers try to multitask which reduces their reaction time leading to accidents, which can easily be avoided if they had been attentive. As such, problems related to the driver’s negligence towards safety a possible solution is to monitor the driver and …driving behavior and alerting them if they are distracted. In this paper, we propose a novel approach for detecting when a driver is distracted due to in hand electronic devices which is not only able to detect the distraction with high accuracy but also is energy and memory efficient. Our proposed compressed neural got an accuracy of 0.83 in comparison to 0.86 of heavyweight network. Show more

Keywords: Machine learning, deep learning, convolutional neural network, CNN, distraction detection, model compression, pruning, quantization, deep compression

DOI: 10.3233/JIFS-189786

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1253-1265, 2022

Article Type: Retraction

DOI: 10.3233/JIFS-219219

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1267-1267, 2022