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Using Hybrid optimization algorithms for nonlinear systems analysis is a novel approach. It is a powerful technique that uses the exploitation ability of one algorithm and the exploration ability of another algorithm, to find the best solution. Literature survey reveals that hybrid algorithms not only show quality response but also give faster convergence of error for nonlinear systems. In this paper, hybrid optimization techniques based proportional integral derivative (PID) controller is used for benchmark problems: Continuous stirred tank reactor (CSTR), Inverted pendulum and blood glucose system. Two recent hybrid algorithms: Particle swarm optimization‐Gravitational search algorithm (PSO‐GSA) and Particle swarm optimization‐Grey wolf algorithm (PSO‐GWO) are implemented to control the temperature and concentration of CSTR, pendulum angle of inverted pendulum, glucose concentration and insulin level of blood glucose system. In PID and PSOGWO algorithms, the exploration abilities of GSA and GWO combined with the exploitation ability of PSO have been used. The performance of these algorithms is then compared with individual PSO, GSA, and GWO algorithms proving their superiority. Stability is ensured using the Lyapunov approach while the robustness of the systems is checked using the parameter perturbation technique. Simulation results show substantial improvement in the performance of these systems by using these meta‐heuristic hybrid optimization techniques. A comparative analysis of these algorithms has also been done. [ABSTRACT FROM AUTHOR]

In today's world multiple type of datasets are available, they are mainly divided into two type namely classification and regression; based on the dataset type machine learning models are applied. There is a technique called bagging in which multiple models are trained simultaneously and produce results on testing dataset. These results are then clubbed to vote in the majority of the same outputs produced. This output is then considered as final output. The bagging technique used in this study is Random Forest (RF) algorithm, where the multiple models are replaced with decision trees. The RF algorithm requires multiple parameters to produce optimum output. These parameters are n_estimators, max_ samples, max_features etc. Though RF algorithm produces favourable results with default values of parameters, but to improve the efficiency of RF algorithm tuning of parameters is preferred. In this study, parameter n_estimators is tuned, on basis of the length of dataset to produce improved results as compared to the default parameters value. Further the proposed tuning method has been applied to RF algorithm and improvement of class prediction efficiency on different datasets is measured in terms of accuracy, precision, recall, F1-score. Also, implementation of ROC curves and auc is performed to depict the improvement of RF algorithm for datasets under consideration, for instance increase in auc values from 0.915 to 0.932 for Spine (2 Classes) dataset and 0.705 to 0.720 for Haberman's survival dataset. [ABSTRACT FROM AUTHOR]

Human identification may be difficult when there is no antemortem data available. A photograph of the deceased may be valuable in such cases. Digital advancement and inclusion in the lives of ordinary people makes it easier to retrieve clear, high-resolution photos from social media accounts and other places. This paper describes three cases of forensic dental identification from a US–Bangla plane crash in Nepal in which a charred body was positively identified from a smiling photograph provided by the deceased's family. Each case is unique and their identification rests on the availability of pre- and post-mortem information. Thus, the number of concordant points may vary from single to multiple; there is no defined criteria for minimum number of concordance for a positive dental identification. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT) era, an immense amount of sensing devices are obtained and produce various sensory data over time for a wide range of disciplines and applications. These devices will result in significant, fast, and real-time data streams based on the utilization characteristics. Utilizing analytics over such data streams to identify new information, model future insights, and make control decisions is a necessary process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. This paper presents a study of digital agriculture and its significance in terms of the application of an IoT-based device — a two-wheeled self-balancing robot — followed by a thorough procedural explanation of the development of the device, which begins with the mathematical modeling of the system through the Euler–Lagrange method to obtain the equations of motion for the same and linearize the equation to define the control method to be used to balance the robot structure, all based on the concept of the inverted pendulum. Then paper discusses the suitable and the most efficient control method, which is the linear quadratic regulator (LQR), for these robots. Then deep learning-based LQR (DL-LQR) method is implemented in the robots performing the algorithm to balance it successfully. [ABSTRACT FROM AUTHOR]

In today's world different data sets are available on which regression or classification algorithms of machine learning are applied. One of the classification algorithms is k-nearest neighbor (kNN) which computes distance amongst various rows in a dataset. The performance of kNN is evaluated based on K -value and distance metric used, where K is the total count of neighboring elements. Many different distance metrics have been used by researchers in literature, one of them is Canberra distance metric. In this paper the performance of kNN based on Canberra distance metric is measured on different datasets, further the proposed Canberra distance metric, namely, Modified Euclidean-Canberra Blend Distance (MECBD) metric has been applied to the kNN algorithm which led to improvement of class prediction efficiency on the same datasets measured in terms of accuracy, precision, recall, F1-score for different values of k. Further, this study depicts that MECBD metric use led to improvement in accuracy value 80.4% to 90.3%, 80.6% to 85.4% and 70.0% to 77.0% for various data sets used. Also, implementation of ROC curves and auc for k = 5 is done to show the improvement is kNN model prediction which showed increase in auc values for different data sets, for instance increase in auc values from 0.873 to 0.958 for Spine (2 Classes) dataset, 0.857 to 0.940, 0.983 to 0.983 (no change), 0.910 to 0.957 for DH, SL and NO class for Spine (3 Classes) data set and 0.651 to 0.742 for Haberman's data set. [ABSTRACT FROM AUTHOR]

The critical dependence of industrial smart grid systems on cutting-edge Internet of Things (IoT) technologies has made these systems more susceptible to a diverse array of assaults. This consequently puts at risk the integrity of energy data as well as the safety of energy management activities that depend on those data. This study offers a generative federated learning framework for semi-supervised threat detection in an IoT-assisted smart grid system. We refer to this framework as FSEI-Net. A unique semi-supervised edge intelligence network (SEI-Net) is presented in the FSEI-Net to enable semi-supervised training using labeled and unlabeled data in the edge tier. The design of SEI-Net is based on with bidirectional generative convolutional network that can intelligently capture the patterns of threat data from partially labeled smart grid data. We present federated training to enable remote edge servers to work together on training a semi-supervised detector without disclosing their own private local data. This is accomplished through cooperative training. To facilitate communication between cloud and edge layers that is both secure and respectful of users' privacy, a reputation-based block chain is introduced in the FSEI-Net. The outcomes from the practical applications demonstrate that the effectiveness of the proposed FSEI-Net over the most recent cutting-edge detection approaches are valid. [ABSTRACT FROM AUTHOR]

Digital image compression is the technique in digital image processing where special attention is provided in decreasing the number of bits required to represent a digital image. A wide range of techniques have been developed over the years, and novel approaches continue to emerge. This paper proposes a new technique for optimizing image compression using Fast Fourier Transform (FFT) and Intelligent Water Drop (IWD) algorithm. IWD-based FFT Compression is a emerging methodology, and we expect compression findings to be much better than the methods currently being applied in the domain. This work aims to enhance the degree of compression of the image while maintaining the features that contribute most. It optimizes the FFT threshold values using swarm-based optimization technique (IWD) and compares the results in terms of Structural Similarity Index Measure (SSIM). The criterion of structural similarity of image quality is based on the premise that the human visual system is highly adapted to obtain structural information from the scene, so a measure of structural similarity provides a reasonable estimate of the perceived image quality. [ABSTRACT FROM AUTHOR]

Two onboard crew members lost their lives in the fatal Makalu Air Cessna Grand Caravan 208B domestic cargo flight crash on May 16, 2018. The Disaster Victim Identification (DVI) procedure comprises external examination, photography, DNA collection, fingerprint collection, postmortem examination, antemortem information collection from the family members, and reconciliation. The major challenge of this operation was dealing with cognitive bias. The antemortem dental information of one of the deceased was revealed to the forensic experts just before the postmortem examination. This influenced the testing strategies. There was a tendency to neglect the complete dental examination presuming the identification was established. Later, during a thorough examination, the forensic odontologist realised that the initial decision was erroneous. Furthermore, there are few experience-based resources available to resolve cognitive bias issues. The authors begin by summarising complicated operations in which they have been involved, followed by a discussion of the key sources of cognitive bias along with the solution to resolve these issues in DVI preparedness planning. Discussion of Disaster Victim Identification experience by the involved team members Forensic odontologists discuss about the situation of bias during the operation This article highlights the importance of adhering to the best practices of disaster victim identification process irrespective of the size of the disaster [ABSTRACT FROM AUTHOR]

In today's world, video captioning is extensively used in various applications for specially-abled and, more specifically, visually abled persons. With advancements in technology for object detection and natural processing, there has been an instant surge infusing the above mainstream tasks. One such example of this fusion resulted in the generation of Image captions when an input image is fed to the system, and it gives a short description of what is present in the image. This fusion pertained to images and was further moved to be implemented on the Videos, with some tweaking in the current methods. This paper presents the survey of the state of art techniques of various video captioning methods. There have been many inputs provided by people worldwide in this domain; thus, there was a need to compile, study and analyze all the results and present that in a comprehensive study, which we have done in this paper. The comparison of various video captioning methods on the distinct dataset was evaluated on different parameters, which were most common and mainly used for image and video analysis. This review was done for methods used from the year 2015–2019 (year by year). The most commonly used dataset and evaluation method are also pictorially represented in a bar graph and scatter plot for each year for the respective evaluation parameter. Though a lot of analysis and research has been done on video captioning, our survey shows many problems. [ABSTRACT FROM AUTHOR]

This article describes the forensic odontological analysis of the events of the 2015 Nepal earthquake. It identifies the problems encountered in the aftermath, lessons learned, and prospective future advances aimed at reducing the subjectivity in disaster victim identification (DVI). During a crisis, dental practitioners, particularly forensic odontologists, can make a substantial contribution to DVI, as highlighted in this article. It also promotes best practices in forensic dentistry that may be used by anyone in situations with few resources or people to deal with comparable scenarios. [ABSTRACT FROM AUTHOR]

Goat (Capra hircus) is a small ruminant with hollow horn that is found throughout the world. A survey (N = 60) was carried out to evaluate the performance of locally adopted goats under farmers' management practices in Sundarbazar municipality of Lamjung district between February and August, 2017. The study focused on two breeds, Khari and Jamunaparias well as their crossbred. Individual goats' primary data, collected using a convenient sampling technique and pretested questionnaires with open and close ended questions, were analyzed using SPSS 20 and MS Excel 2010. Results revealed significant effects of a Doe's breed on age at first estrus (p < 0.001), age at first kidding (p < 0.001), gestation length (p < 0.001), postpartum estrus (p < 0.001), and kidding interval (p < 0.05). Age at first estrus and age at first kidding were lowest (252.3 days and 403.9 days, respectively) for Khari. Highly significant lowest kidding interval (214.7 days) and significantly shortest gestation length (148.2 days) were found for Khari. No significant effect of parity was observed on age at first estrus, age at first kidding, kidding interval, and postpartum estrus, but a significant effect (p < 0.05) was observed on gestation length with lowest values in 4th to 6th parity (149.5 days). Doe's breed showed a significant effect on litter size at birth (p < 0.05). However, no significant effect of the breed was found on litter birth weight and litter size at weaning (p < 0.05). The results demonstrated Khari as the desirable breed for the locality. [ABSTRACT FROM AUTHOR]

A novel real-time multimodal eye blink detection method using an amalgam of five unique weighted features extracted from the circle boundary formed from the eye landmarks is proposed. The five features, namely (Vertical Head Positioning, Orientation Factor, Proportional Ratio, Area of Intersection, and Upper Eyelid Radius), provide imperative gen (z score threshold) accurately predicting the eye status and thus the blinking status. An accurate and precise algorithm employing the five weighted features is proposed to predict eye status (open/close). One state-of-the-art dataset ZJU (eye-blink), is used to measure the performance of the method. Precision, recall, F1-score, and ROC curve measure the proposed method performance qualitatively and quantitatively. Increased accuracy (of around 97.2%) and precision (97.4%) are obtained compared to other existing unimodal approaches. The efficiency of the proposed method is shown to outperform the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In the era of modern technology, people may readily communicate through facial expressions, body language, and other means. As the use of the Internet evolves, it may be a boon to the medical fields. Recently, the Internet of Medical Things (IoMT) has provided a broader platform to handle difficulties linked to healthcare, including people's listening and hearing impairment. Although there are many translators that exist to help people of various linguistic backgrounds communicate more effectively. Using kinesics linguistics, one may assess or comprehend the communications of auditory and hearing-impaired persons who are standing next to each other. When looking at the present COVID-19 scenario, individuals are still linked in some way via online platforms; however, persons with disabilities have communication challenges with online platforms. The work provided in this research serves as a communication bridge inside the challenged community and the rest of the globe. The proposed work for Indian Sign Linguistic Recognition (ISLR) uses three-dimensional convolutional neural networks (3D-CNNs) and long short-term memory (LSTM) technique for analysis. A conventional hand gesture recognition system involves identifying the hand and its location or orientation, extracting certain essential features and applying an appropriate machine learning algorithm to recognise the completed action. In the calling interface of the web application, WebRTC has been implemented. A teleprompting technology is also used in the web app, which transforms sign language into audible sound. The proposed web app's average recognition rate is 97.21%. [ABSTRACT FROM AUTHOR]

The digital transformation (DT) is the acquiring the digital tool, techniques, approaches, mechanism etc. for the transformation of the business, applications, services and upgrading the manual process into the automation. The DT enable the efficacy of the system via automation, innovation, creativities. The another concept of DT in the engineering domain is to replace the manual and/or conventional process by means of automation to handle the big-data problems in an efficient way and harness the static/dynamic system information without knowing the system parameters. The DT represents the both opportunities and challenges to the developer and/or user in an organization, such as development and adaptation of new tool and technique in the system and society with respect to the various applications (i.e., digital twin, cybersecurity, condition monitoring and fault detection & diagnosis (FDD), forecasting and prediction, intelligent data analytics, healthcare monitoring, feature extraction and selection, intelligent manufacturing and production, future city, advanced construction, resilient infrastructure, greater sustainability etc.). Additionally, due to high impact of advanced artificial intelligent, machine learning and data analytics techniques, the harness of the profit of the DT is increased globally. Therefore, the integration of DT into all areas deliver a value to the both users as well as developer. In this editorial fifty-two different applications of DT of distinct engineering domains are presented, which includes its detailed information, state-of-the-art, methodology, proposed approach development, experimental and/or emulation-based performance demonstration and finally conclusive summary of the developed tool/technique along with the future scope. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]