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

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