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

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