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Improved Bald Eagle Search for Optimal Allocation of D-STATCOM in Modern Electrical Distribution Networks with Emerging Loads
Currently, modern electrical distribution networks (EDNs) are experiencing high demand with emerging electric vehicle loads and are being planned for specific load requirements such as agricultural loads. In this connection, characterization and optimization of their performance become essential in planning studies. In this paper, optimal reactive power compensation using a distribution-static synchronous compensator (D-STATCOM) is proposed with the aim of loss reduction, voltage profile improvement and voltage stability enhancement different types of loads including agricultural and electric vehicle loads. A recent efficient meta-heuristic approach, improved bald eagle search (IBES), is implemented for solving the proposed optimization problem considering different operational and planning constraints. The simulation results are performed on IEEE 33-bus for different types of load modelling. The computational efficiency of IBES is compared with basic BES and other literature works. From the results, IBES has shown superior computational characteristics than all compared works. On the other hand, the optimal location and size of D-STATCOM caused significant loss reduction, voltage profile improvement and voltage stability enhancement for kinds of loads as experiencing in the modern EDNs 2022,International Journal of Intelligent Engineering and Systems.All Rights Reserved -
Green Synthesized ZnO Nanoparticles as Biodiesel Blends and their Effect on the Performance and Emission of Greenhouse Gases
Pollution and global warming are a few of the many reasons for environmental problems, due to industrial wastes and greenhouse gases, hence there are efforts to bring down such emissions to reduce pollution and combat global warming. In the present study, zinc oxide nanoparticles are green synthesized using cow dung as fuel, through combustion. Synthesized material was characterized by FTIR, XRD, UV, and FESEM. The as-prepared ZnO-GS NPs were employed as a transesterification catalyst for the preparation of biodiesel from discarded cooking oil. The biodiesel obtained is termed D-COME (discarded cooking oil methyl ester), which is blended with 20% commercial diesel (B20). Additionally, this blend, i.e., B20, is further blended with varying amounts of as-prepared ZnO-GS NPs, in order to ascertain its effects on the quality of emissions of various greenhouse gases such as hydrocarbons, COx, NOx. Moreover, the brake thermal efficiency (BTHE) and brake specific fuel consumption (BSFC) were studied for their blends. The blend (B20) with 30 mg of ZnO-GS, i.e., B20-30, displays the best performance and reduced emissions. Comparative studies revealed that the ZnO-GS NPs are as efficient as the ZnO-C NPs, indicating that the green synthetic approach employed does not affect the efficiency of the ZnO NPs. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Investigations on the electronic properties and effect of chitosan capping on the structural and optical properties of zinc aluminate quantum dots
Quantum confined uncapped and chitosan capped nanoparticles of ZnAl2O4 synthesized by microwave-assisted solgel method were investigated by structural analysis and optical techniques. X-ray diffraction studies confirmed the formation of cubic spinels with crystallite size 4.5 nm and 3.4 nm, respectively for uncapped and chitosan capped ZnAl2O4 nanoparticles. Chitosan capping produced a blue shift in bandgap energy (3.84 to 3.94 eV) agreeing with size effects according to the Brus equation. A blue shift in emission peak and enhancement in photoluminescence intensity was also observed upon chitosan capping. The electronic band structure and the density of states of the bulk spinel were also calculated using density functional theory. The effective masses of electrons and holes estimated based on the band structure were used to extract the excitonic Bohr radius. 2021 Elsevier B.V. -
Electro fabrication of molecularly imprinted sensor based on Pd nanoparticles decorated poly-(3 thiophene acetic acid) for progesterone detection
In recent years, scientific community has witnessed substantial interest in the design and engineering of electrodes as sensing platforms towards sensitive and selective detection of hormones. An electrochemical strategy for the detection of progesterone was proposed by generating a composite film comprising of palladium nanoparticles with 3-thiophene acetic acid (3-TAA) coupled with molecular imprinting technology. Progesterone molecule was employed as the template while generating molecular imprints by electropolymerization on the surface of the Carbon Fibre Paper (CFP) electrode. Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry were used to analyse the various modified working electrodes (CV). Characterization methods included field emission scanning microscopy, energy dispersive X-ray spectrometry, optical profilometry, and X-ray photon electron spectroscopy. Pd nanoparticles resulted in enhanced sensitivity and molecular imprinting technology contributed to its specificity. Because of the molecular cavities created on the removal of the template molecule, Nyquist plots data showed that the MIP/Pd/CFP electrode had the lowest charge transfer resistance compared to other control electrodes. 2022 Elsevier Ltd -
Multifaceted Destination Personality Traits: A Short Communication on Understanding from Tourists Perspective
This short communication is an extract from a major research work on destination branding, and this cull out of analysis focused on the multifaceted destination personality traits that the destinations possess and perhaps how such perceptions of tourists differ based on the selected personal factors. Though there are many studies in the destination branding literature, the evidence regarding the personality traits is still at the stage of progression, and approaches referring to multifaceted personality traits are unseen. After the pilot testing, a structured questionnaire was floated to 400 tourists who visited the selected destinations a district in Tamil Nadu, India, between June 2019 and February 2020, where 327 responses were finalized. The questionnaire had statements measuring the destinations personality traits and other questions on tourists characteristics. Combined mean calculation and multivariate results revealed that two personality traits, welcoming and friendly, were emphasized by the tourists and perceived in common. Also, personality traits such as spiritual and charming were found to be commonly perceived. The mean values also indicated the existence of multifaceted destination personality traits some inherent and some perceived. Marketers and others thereof have been recommended on the branding and advertising strategies based on the outcome of this communication. The limitations and scope of this research have been indicated. 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Nitrogen-rich dual linker MOF catalyst for room temperature fixation of CO2 via cyclic carbonate synthesis: DFT assisted mechanistic study
The benign synthesis of a novel Zn based Lewis acid-base bifunctional metal-organic framework (ITH-1) and its room temperature catalytic ability for the chemical fixation of carbon dioxide via cyclic carbonate synthesis is reported herein. ITH-1 is characterized by the presence of mono coordinated pendant imidazole groups throughout the framework inducing Lewis basicity. The synthesized material is crystallized in the monoclinic space group as revealed by the Single Crystal X-ray Diffraction Analysis and possesses a 2 D non-planar interdigitated network wherein the neighbouring sheets are connected via strong hydrogen bonding (1.947 . ITH-1 was characterized thoroughly via various physicochemical analyses such as XRD, FT-IR, Raman, FE-SEM, CHN, ICP, TGA and was found thermally stable up to 300 ?C. The co-existence of accessible and active Lewis acid (Zn) Lewis base (imidazole) moieties rendered ITH-1 the potential to catalyse the cycloaddition of CO2 with propylene oxide under solvent and co-catalyst free conditions (~95% conversion) at moderate temperatures with remarkable reusable performance (over 5 times). ITH-1 manifested excellent CO2 conversion even under room temperature and 1 bar pressure in the presence of a co-catalyst. Density Functional Theory (DFT) calculations utilizing M06 functional were exercised to envisage the mechanism behind the successful CO2 conversion by ITH-1 at room temperature and were found to be in clear agreement with the experimental results. 2022 Elsevier Ltd -
Dimensionally engineered ternary nanocomposite of reduced graphene oxide/multiwalled carbon nanotubes/zirconium oxide for supercapacitors
Three dimensional (3D) hybrid nanoarchitecture of two-dimensional (2D) reduced graphene oxide/one dimensional (1D) multiwalled carbon nanotube and zero-dimensional (0D) zirconium oxide (ZrO2) nanoparticles (rGO/MWCNT/ZrO2) was synthesised by a simple hydrolysis method for high performance supercapacitors. To unlock the properties of individual materials to the maximum, binaries of ZrO2 with GO and MWCNT were also synthesised. The increased wettability, integrated structure, and the synergistic effect of rGO, MWCNT, and ZrO2 in rGO/MWCNT/ZrO2 (GMZ) offer a capacitance of 357 F g?1 at 1 A g?1 with excellent capacitance retention of 98% across 5000 cycles. 1D structure of MWCNT creates an exceptional conductive network with rGO due to the confinement of electrons and ions without disturbing its electronic structure. The intriguing supercapacitor performance of differently dimensioned framework with ZrO2 emphasises the engineered orientation and tuning of a designed environment for its appropriateness, uniqueness, and sensitivity to push up enhanced performance. 2021 Elsevier B.V. -
UV-Promoted Metal- and Photocatalyst-Free Direct Conversion of Aromatic Aldehydes to Nitriles
Abstract: An efficient, simple, and catalyst-free UV-induced functional group transformation of aromatic aldehydes to nitriles has been reported. The developed strategy delivers various functionalized aromatic nitriles with high yields and purity. The UV irradiation activates the carbonyl group of aldehydes and leads to the formation of aldoxime intermediate, further resulting in the generation of nitriles. The striking highlights of the reported methodology are simple reaction conditions, good yields, UV-promoted transformation, and catalyst-free synthesis. Due to the above-mentioned advantages, the methodology provides a whip hand toward environmentally friendly chemical synthesis. 2022, Pleiades Publishing, Ltd. -
An Effective Strategy and Mathematical Model to Predict the Sustainable Evolution of the Impact of the Pandemic Lockdown
There have been considerable losses in terms of human and economic resources due to the current coronavirus pandemic. This work, which contributes to the prevention and control of COVID-19, proposes a novel modified epidemiological model that predicts the epidemics evolution over time in India. A mathematical model was proposed to analyze the spread of COVID-19 in India during the lockdowns implemented by the government of India during the first and second waves. What makes this study unique, however, is that it develops a conceptual model with time-dependent characteristics, which is peculiar to Indias diverse and homogeneous societies. The results demonstrate that governmental control policies and suitable public perception of risk in terms of social distancing and public health safety measures are required to control the spread of COVID-19 in India. The results also show that Indias two strict consecutive lockdowns (21 days and 19 days, respectively) successfully helped delay the spread of the disease, buying time to pump up healthcare capacities and management skills during the first wave of COVID-19 in India. In addition, the second waves severe lockdown put a lot of pressure on the sustainability of many Indian cities. Therefore, the data show that timely implementation of government control laws combined with a high risk perception among the Indian population will help to ensure sustainability. The proposed model is an effective strategy for constructing healthy cities and sustainable societies in India, which will help prevent such a crisis in the future. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Explaining the intention to uptake COVID-19 vaccination using the behavioral and social drivers of vaccination (BeSD) model
Background: The World Health Organization (WHO) has proposed a tool to measure behavioral and social drivers (BeSD) of vaccination uptake intentions of people across all countries. This study tests BeSD model to predict people's intentions to uptake COVID-19 vaccination in rural India. Methods: An online cross-sectional survey was developed for the purpose based on the components of the BeSD model, i.e., confidence, motivation, and behavioral intention. A convenient sampling technique was used to collect samples, amounting to a total of 625, from rural Bengaluru, in the Karnataka state of India. Structural equation modelling (SEM) was applied to examine the proposed model. All respondents for the survey were in the age category of 1868 years with a mean age of 35 years. Findings: The results showed that 85% of COVID-19 vaccine uptake intentions can directly or indirectly be attributed to the government's vaccine communication strategy, perceived threats about the vaccine, and their trust in the healthcare sector. The dimensions of the vaccine acceptance scale (motivation factors) act as a mediator between these factors and COVID-19 vaccination uptake (the behavioral factor). Conclusion: The study demonstrates that the BeSD framework is an efficient model for predicting the COVID-19 vaccination uptake in India. 2022 The Authors -
Positive side effects of the Covid-19 pandemic on environmental sustainability: evidence from the quadrilateral security dialogue countries
Purpose: The eruption of coronavirus disease 2019 (COVID-19) has pointedly subdued global economic growth and producing significant impact on environment. As a medicine or a treatment is yet available at mass level, social distancing and lockdown is expected the key way to avert it. Some outcome advocates that lockdown strategies considered to reduce air pollution by curtailing the carbon emission. Current investigation strives to affirm the impact of lockdown and social distancing policy due to covid-19 outbreak on environmental pollution in the QUAD nations. Design/methodology/approach: To calibrate the social movement of public, six indicators such residential mobility, transit mobility, workplace mobility, grocery and pharmacy mobility, retail and recreation mobility and park mobility have been deliberated. The data of human mobility have been gathered from the Google mobility database. To achieve the relevant objectives, current pragmatic analysis exerts a panel autoregressive distributed lag model (ARDL)-based framework using the pooled mean-group (PMG) estimator, proposed by Pesaran and Shin (1999), Pesaran and Smith (1995). Findings: The outcome reveals that in the long-run public mobility change significantly impact the pollutants such as PM2.5 and nitrogen dioxide; however, it does not lead to any changes on ozone level. As per as short run outcome is concerned, the consequence unearths country wise heterogeneous impact of different indicators of public mobility on the air pollution. Research limitations/implications: The ultimate inferences of the above findings have been made merely on the basis of examination of QUAD economies; however, comprehensive studies can be performed by considering modern economies simultaneously. Additionally, finding could be constraint in terms of data; for instance, Google data used may not suitably signify real public mobility changes. Originality/value: A considerable amount of investigation explores the impact of covid-19 on environmental consequences by taking carbon emission as a relevant indicator of environmental pollution. Hence, the present pragmatic investigation attempts to advance the present discernment of the above subject in two inventive ways. Primarily, by investigating other components of environmental pollution such as nitrogen dioxide, PM2.5 and ozone, to reveal the impact of covid-19 outbreak on environmental pollution, as disregarded by the all preceding studies. Additionally, it makes a methodological contribution before integrating supplementary variables accompanying with ecological air pollution. Finally, the current research article provides an alternative and creative approach of modeling the impact of public mobility on environmental sustainability. 2021, Emerald Publishing Limited. -
Earthquake and flood resilience through spatial Planning in the complex urban system
Urban Communities are exposed to different disaster risks. The paper aims at understanding the interrelation of spatial planning and the resilience of the urban communities for earthquakes and floods. Various spatial planning components were used to evaluate the community resilience to earthquake and flood in the city of Pune of Maharashtra state in India. It has been identified that spatial planning contributes to a greater extent in determining community resilience. Spatial planning results in differential resilience among communities. In the study area, economically weaker households are found to be more vulnerable to disaster risk due to their spatial locations and limited accessibility to share the resources. These factors are found to be contributing to reduced resilience in the city. 2022 The Authors -
Towards a theory of well-being in digital sports viewing behavior
Purpose: Social television (Social TV) viewing of live sports events is an emerging trend. The realm of transformative service research (TSR) envisions that every service consumption experience must lead to consumer well-being. Currently, a full appreciation of the well-being factors obtained through Social TV viewing is lacking. This study aims to gain a holistic understanding of the concept of digital sports well-being obtained through live Social TV viewing of sports events. Design/methodology/approach: Focus group interviews were used to collect data from the 40 regular sports viewers, and the qualitative data obtained is analyzed thematically using NVivo 12. A post hoc verification of the identified themes is done to narrow down the most critical themes. Findings: The exploration helped understand the concept of digital sports well-being (DSW) obtained through live Social TV sports spectating and identified five critical themes that constitute its formation. The themes that emerged were virtual connectedness, vividness, uncertainty reduction, online disinhibition and perceived autonomy. This study defines the concept and develops a conceptual model for DSW. Research limitations/implications: This study adds to the body of knowledge in TSR, transformative sport service research, digital customer engagement, value co-creation in digital platforms, self-determination theory and flow theory. The qualitative study is exploratory, with participants views based on a single match in one particular sport, and as such, its findings are restrained by the small sample size and the specific sport. To extend this studys implications, empirical research involving a larger and more diversified sample involving multiple sports Social TV viewing experiences would help better understand the DSW concept. Practical implications: The research provides insights to Social TV live streamers of sporting events and digital media marketers about the DSW construct and identifies the valued DSW dimensions that could provide a competitive advantage. Originality/value: To the best of the authors knowledge, the exploration is the first attempt to describe the concept of DSW and identify associated themes. 2021, Emerald Publishing Limited. -
Cloud security based attack detection using transductive learning integrated with Hidden Markov Model
In recent years, organizations and enterprises put huge attention on their network security. The attackers were able to influence vulnerabilities for the configuration of the network through the network. Zero-day (0-day) is defined as vulnerable software or application that is either defined by the vendor or not patched by any vendor of organization. When zero-day attack is identified within the network there is no proper mechanism when observed. To mitigate challenges related to the zero-day attack, this paper presented HMM_TDL, a deep learning model for detection and prevention of attack in the cloud platform. The presented model is carried out in three phases like at first, Hidden Markov Model (HMM) is incorporated for the detection of attacks. With the derived HMM model, hyper alerts are transmitted to the database for attack prevention. In the second stage, a transductive deep learning model with k-medoids clustering is adopted for attack identification. With k-medoids clustering, soft labels are assigned for attack and data and update to the database. In the last phase, with computed HMM_TDL database is updated with computed trust value for attack prevention within the cloud. 2022 -
Theory of planned behavior in predicting the construction of eco-friendly houses
Purpose: The present study aimed to explore the applicability of theory of planned behavior in construction of eco-friendly houses. Design/methodology/approach: Study utilized cross-sectional correlational research design, collected data from 269 adult house owners of Kerala, India, with the help of a self-report measures namely, attitude towards eco-friendly house construction, subjective norm, perceived behavioral control, behavioral intention to build eco-friendly houses, check list of eco-friendly house and socio-demographic data sheet. Descriptive statistics, Karl Pearson product moment correlation, confirmatory factor analysis and mediation analysis with the help of AMOS were used to describe the distribution of study variables and to test the research hypotheses and proposed model. Findings: Study revealed that behavioral intention to build eco-friendly house was the immediate and strongest predictor of actual behavior of constructing an eco-friendly house. Behavioral intention mediated the relationship of attitudinal variables, normative variables and control variables with the behavior of constructing eco-friendly houses. Research limitations/implications: The results vouched the applicability of theory of planned behavior as a comprehensive model in explaining the behavior of eco-friendly house construction. Practical implications: Results of the study iterates the utility of attitudinal, normative and control factors in enhancing the choice of constructing eco-friendly houses. The results can be applied to develop a marketing tool to enhance the behavior of choosing or constructing eco-friendly houses in the population. Originality/value: Role of conventional concrete construction in climate crisis is unquestioned, and adopting eco-friendly architecture is a potential solution to the impending doom of climate crisis. Behavioral changes play a significant role in the success of global actions to curb the climate crisis. Present study discusses the role of psychological variables in constructing eco-friendly houses. 2022, Emerald Publishing Limited. -
Convolutional neural network for stock trading using technical indicators
Stock market prediction is a very hot topic in financial world. Successful prediction of stock market movement may promise high profits. However, an accurate prediction of stock movement is a highly complicated and very difficult task because there are many factors that may affect the stock price such as global economy, politics, investor expectation and others. Several non-linear models such as Artificial Neural Network, fuzzy systems and hybrid models are being used for forecasting stock market. These models have limitations like slow convergence and overfitting problem. To solve the aforementioned issues, this paper intends to develop a robust stock trading model using deep learning network. In this paper, a stock trading model by integrating Technical Indicators and Convolutional Neural Network (TI-CNN) is developed and implemented. The stock data investigated in this work were collected from publicly available sources. Ten technical indicators are extracted from the historical data and taken as feature vectors. Subsequently, feature vectors are converted into an image using Gramian Angular Field and fed as an input to the CNN. Closing price of stock data are manually labelled as sell, buy, and hold points by determining the top and bottom points in a sliding window. The duration considered over a period from January 2009 to December 2018. Prediction ability of the developed TI-CNN model is tested on NASDAQ and NYSE data. Performance indicators such as accuracy and F1 score are calculated and compared to prove effectiveness of the proposed stock trading model. Experimental results demonstrate that the proposed TI-CNN achieves high prediction accuracy than that of the earlier models considered for comparison. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies
The evolution of the coronavirus (COVID-19) disease took a toll on the social, healthcare, economic, and psychological prosperity of human beings. In the past couple of months, many organizations, individuals, and governments have adopted Twitter to convey their sentiments on COVID-19, the lockdown, the pandemic, and hashtags. This paper aims to analyze the psychological reactions and discourse of Twitter users related to COVID-19. In this experiment, Latent Dirichlet Allocation (LDA) has been used for topic modeling. In addition, a Bidirectional Long Short-Term Memory (BiLSTM) model and various classification techniques such as random forest, support vector machine, logistic regression, naive Bayes, decision tree, logistic regression with stochastic gradient descent optimizer, and majority voting classifier have been adapted for analyzing the polarity of sentiment. The effectiveness of the aforesaid approaches along with LDA modeling has been tested, validated, and compared with several benchmark datasets and on a newly generated dataset for analysis. To achieve better results, a dual dataset approach has been incorporated to determine the frequency of positive and negative tweets and word clouds, which helps to identify the most effective model for analyzing the corpora. The experimental result shows that the BiLSTM approach outperforms the other approaches with an accuracy of 96.7%. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Effects of variable viscosity and rotation modulation on ferroconvection
We theoretically explore the dynamics of a ferrofluid with temperature and magnetic field-dependent viscosity, which is in a RayleighBard situation and is subjected to rotation. The problem considers both sinusoidal and non-sinusoidal time-periodic variations of rotation to study the onset and post-onset regimes of RayleighBard ferroconvection. We perform a weakly nonlinear stability analysis using a truncated Fourier series representation and arrive at the third-order Lorenz system for ferrofluid convection with variable viscosity. By using the linearized form of the Lorenz system for ferrofluid convection with variable viscosity, we arrive at the critical Rayleigh number to study the onset of rotating ferroconvection. The heat transport is quantified in terms of the time-averaged Nusselt number and the effects of various parameters on it are studied. The effect of modulated rotation is found to have a stabilizing effect on the onset of ferroconvection while that of variable viscosity has a destabilizing effect. The effects of magnetorheological and thermorheological effects are antagonistic in nature. It is found that the square waveform modulation facilitates maximum heat transport in the system due to advanced onset of ferroconvection. 2021, Akadiai Kiad Budapest, Hungary. -
Search and analysis of giant radio galaxies with associated nuclei (SAGAN): III. New insights into giant radio quasars
Giant radio quasars (GRQs) are radio-loud active galactic nuclei (AGN) that propel megaparsec-scale jets. In order to understand GRQs and their properties, we have compiled all known GRQs (the GRQ catalogue) and a subset of small (size < 700 kpc) radio quasars (SRQs) from the literature. In the process, we have found ten new Fanaroff-Riley type-II GRQs in the redshift range of 0.66 < z < 1.72, which we include in the GRQ catalogue. Using the above samples, we have carried out a systematic comparative study of GRQs and SRQs using optical and radio data. Our results show that the GRQs and SRQs statistically have similar spectral index and black hole mass distributions. However, SRQs have a higher radio core power, core dominance factor, total radio power, jet kinetic power, and Eddington ratio compared to GRQs. On the other hand, when compared to giant radio galaxies (GRGs), GRQs have a higher black hole mass and Eddington ratio. The high core dominance factor of SRQs is an indicator of them lying closer to the line of sight than GRQs. We also find a correlation between the accretion disc luminosity and the radio core and jet power of GRQs, which provides evidence for disc-jet coupling. Lastly, we find the distributions of Eddington ratios of GRGs and GRQs to be bi-modal, similar to that found in small radio galaxies (SRGs) and SRQs, which indicates that size is not strongly dependent on the accretion state. Using all of this, we provide a basic model for the growth of SRQs to GRQs. ESO 2022. -
Smart Affect Recognition System for Real-Time Biometric Surveillance Using Hybrid Features and Multilayered Binary Structured Support Vector Machine
Human affect recognition (HAR) using images of facial expression and electrocardiogram (ECG) signal plays an important role in predicting human intention. This system improves the performance of the system in applications like the security system, learning technologies and health care systems. The primary goal of our work is to recognize individual affect states automatically using the multilayered binary structured support vector machine (MBSVM), which efficiently classify the input into one of the four affect classes, relax, happy, sad and angry. The classification is performed efficiently by designing an efficient support vector machine (SVM) classifier in multilayer mode operation. The classifier is trained using the 8-fold cross-validation method, which improves the learning of the classifier, thus increasing its efficiency. The classification and recognition accuracy is enhanced and also overcomes the drawback of 'facial mimicry' by using hybrid features that are extracted from both facial images (visual elements) and physiological signal ECG (signal features). The reliability of the input database is improved by acquiring the face images and ECG signals experimentally and by inducing emotions through image stimuli. The performance of the affect recognition system is evaluated using the confusion matrix, obtaining the classification accuracy of 96.88%. 2020 The British Computer Society 2020. All rights reserved.