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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]

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]

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]

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]