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Model of ICT based blended education system: Productive implementation for sector skills development /
Patent Number: 202141055854, Applicant: Suplab Podder. -
Model of ICT based blended education system: Productive implementation for sector skills development /
Patent Number: 202141055854, Applicant: Suplab Podder. -
Model Selection Procedure in Alleviating Drawbacks of the Electronic Whiteboard
Deep learning has paved the way for critical and revolutionary applications in almost every field of life in general. Ranging from engineering to healthcare, machine learning and deep learning has left its mark as the state-of-the-art technology application which holds the epitome of a reasonable high benchmarked solution. Incorporating neural network architectures into applications has become a common part of any software development process. In this paper, we perform a comparative analysis on the different transfer learning approaches in the domain of hand-written digit recognition. We use two performance measures, loss and accuracy. We later visualize the different results for the training and validation datasets and reach to a unison conclusion. This paper aims to target the drawbacks of the electronic whiteboard with simultaneous focus on the suitable model selection procedure for the digit recognition problem. 2021 IEEE. -
Modeling a Logistic Regression based Sustained Approach for Cancer Detection
This assessment and treatment of cancer may be done using logistic regression. To properly forecast whether a tumour is malignant or benign, the likelihood of binary outcomes may be simulated based on input variables and taken into account for factors like volume, topology and texture. It aids in risk assessment by estimating an individual's likelihood of developing cancer using factors like age-group, relatives past data, life choices and gene based markers. Logistic regression plays an important role in early cancer detection and creating screening tools that identify high-risk individuals through patent characteristics, biomarkers, and medical imaging data. Prediction of the probability of survival based on age, tumor characteristics, treatment options and comorbidities is useful for survival analysis. In a comparative study, logistic regression achieved a high accuracy of 97.4%, along with random forest, in cancer detection and diagnosis. 2023 IEEE. -
Modeling and computational fluid dynamic analysis on a non-AC bus coach system
The main objective of this paper is to reduce the drag force and enhance the uniform airflow inside an existing non-air-conditioning bus coach system. The redesigning of an existing bus carried out by considering the forces that reduce the moment of the bus. Modeling and meshing was carried out using solid works and Hypermesh software, respectively. Finally, the problem is simulated using Ansys fluent software and analysis is carried out for different bus models. The noteworthy findings state that the air resistance of the vehicle is found to be 812.74 N and coefficient of drag is 0.67 are less as compared to existing bus model. 2020 Wiley Periodicals LLC -
Modeling Consumer Price Index: A Machine Learning Approach
The change in price of a group of goods and services is reflected in terms of consumer price index (CPI), making it one of the most important economic indicators. This is also the mostly used measure of inflation. Forecasted CPI values help the Government to take corrective measures to control the economic conditions of the country. This paper implements and examines two machine learning models such as artificial neural network (ANN) and ANN model optimized with particle swarm optimization (PSO) known as ANN-PSO to assess the accuracy in predictability of CPI. The data set for four groups such as food and beverages, housing, clothing, and footwear used for the calculation of all India CPI has been taken from the official website of the Government of India. The mean absolute percentage error (MAPE) has been used as the validator for model accuracy. The MAPE calculated for all experiments are less than 10% which indicates that the ANN-PSO models used are highly accurate for prediction of CPI of India. 2022 Wiley-VCH GmbH -
Modeling destination competitiveness: The unfamiliar shift for destination rebranding, restructuring, and repositioning with DMOs
Tourism is a tactical economic practice across the globe, but the urban and provincial transformations in the industry are strongly contemplated in the light of an unfamiliar shift in tourism business. This chapter discusses an integrated concept with a framework relating systematic approach of managing the destination and its competitiveness. An investigation on the impact on tourism and the recent narrative of national, regional, and local planning approach directs towards efficient destination management organizations (DMO) in practice for future development. This has proceeded by the formation of a competitive approach, emphasizing on the DMO roles and responsibilities helpful for a destination management during an unfamiliar business trend. Modeling destination competitiveness demands an absolute mechanism through destination rebranding, restructuring, and repositioning with DMOs for enabling competency. 2018, IGI Global. -
Modeling Environmentally Conscious Purchase Behavior: Examining the Role of Ethical Obligation and Green Self-Identity
Due to environmental degradation, using environment-friendly products has become necessary to reduce carbon emissions. However, the consumption of such products is still below expectations because these products are usually costlier than their traditional counterparts. The current study aims to investigate consumer behavior towards environment-friendly products using Ajzens theory of planned behavior as a theoretical model. The study seeks to examine the role of the key determinates of environmentally conscious purchase behavior, such as ethical obligation and green self-identity. A total of 386 responses were collected from consumers living in a few major cities of northern India using purposive sampling. The data were analyzed using structural equation modeling in Amos 22.0. The results demonstrated that attitudes towards environment-friendly products perceived behavioral control and green self-identity as the major determinants of green purchase intentions. In addition, attitude was reported to mediate the effect of ethical obligation on green purchase intentions and green self-identity was found to moderate the effect of attitude on green purchase intentions. Additionally, green self-identity was also reported to moderate the relationship between ethical obligation and attitude. The study adds value to the existing literature by signifying the role of green self-identity and ethical obligation in stimulating consumers green purchase intentions. The findings of the study are also meaningful for marketers and policymakers. 2023 by the authors. -
Modeling of Real Time Traffic Flow Monitoring System Using Deep Learning and Unmanned Aerial Vehicles
Recently, intelligent video surveillance technologies using unmanned aerial vehicles (UAVs) have been considerably increased in the transportation sector. Real time collection of traffic videos by the use of UAVs finds useful to monitor the traffic flow and road conditions. Since traffic jams have become common in urban areas, it is needed to design artificial intelligence (AI) based recognition techniques to attain effective traffic flow monitoring. Besides, the traffic flow monitoring system can assist the traffic managers to start efficient dispersal actions. Therefore, this study designs a real time traffic flow monitoring system using deep learning (DL) and UAVs, called RTTFM-DL. The proposed RTTFM-DL technique aims to detect vehicles, count vehicles, estimate speed and determine traffic flow. In addition, an efficient vehicle detection model is proposed by the use of Faster Regional Convolutional Neural Network (Faster RCNN) with Residual Network (ResNet). Also, a detection line based vehicle counting approach is designed, which is based on overlap ratio. Finally, traffic flow monitoring takes place based on the estimated vehicle count and vehicle speed. In order to guarantee the effectual performance of the RTTFM-DL technique, a series of experimental analyses take place and the results are examined under varying aspects. The experimental outcomes highlighted the betterment of the RTTFM-DL technique over the recent techniques. The RTTFM-DL technique has gained improved outcomes with a higher accuracy of 0.975. 2022 River Publishers. -
Modeling of the LiouvilleGreen method to approximate the mechanical waves in functionally graded and piezo material with a comparative study
The present research article studies and compares the surface waves transmission through the functionally graded piezoelectric material (FGPM) club between the piezomagnetic (PM) layer -and half-space, and for a comparative study, lower half-space is assumed to be piezoelectric material. The transmission of mechanical waves in a smart structure is analyzed by following the elastic wave theory of magneto-electro-elasticity. The Liouville-Green (LG) approximation technique is used to solve the differential equation in the FGPM stratum, where exponential variation is assumed in material gradients. It is noticed that the material gradients depend considerably on the angular frequency, which should be a crucial factor in regulating the dispersion characteristics of functionally graded materials (FGM) waveguides. In closed determinant form, the dispersion relation has been obtained for FGPM plate for electrically open and short cases. The profound effect of parameters, such as material gradient, a width of the layer on phase velocity, coupled electromechanical factor, and angular velocity, is observed and delineated graphically. Different parametric plots are sub-plotted into a single figure to increase the readability of the graphs. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Modeling requirements with diabetes using supervised machine learning techniques
Diabetes is characterized by either insufficient or inefficient insulin production by the body. High blood glucose levels result from this, which over time can harm a number of tissues and organs in the body. Diabetes can be brought on by a specific age, obesity, inactivity, insufficient physical activity, inherited diabetes, lifestyle, poor diet, hypertension, etc. This chapter explores modeling requirements with diabetes using supervised machine learning techniques. 2023, IGI Global. All rights reserved. -
Modeling the impact of political risk components on major macroeconomic variables
The risks of the political conditions prevailing in an economy are found to have a significant impact on its stock market. Such political risks can distort the entire economy. This study investigated the impact of political risk on major macroeconomic variables which are the indicators of growth in any economy by considering the various components of political risk as given by World Bank's worldwide governance indicators. Using a panel data approach, it modeled the major macroeconomic variables of eleven emerging and frontier Asian economies with various components of political risk. The study found that irrespective of the inter-linkages among different macroeconomic variables, they were not affected by the same political risk components. Most importantly, it revealed that GDP did not respond to any of the political risk components, whereas the exchange rate was found to be affected by all the political risk components. The study also found that FDI, inflation, and real interest rate were affected by one or more political risk components. 2019 AESS Publications. All Rights Reserved. -
Modeling the Intention to Use AI Healthcare Chabots in the Indian Context
Covid-19 has accelerated the need and use of artificial Intelligence-based healthcare Chabots. Penetration of the internet, smartphone, computational capability and machine learning technology brings healthcare services close to the patients. The penetration of AI healthcare Chatbot technology worldwide is on the rise. However, the healthcare ecosystem in India is unique and poses challenges in the adoption of healthcare chatbots. The demographic characteristics, economic conditions, diversity, belief systems on health-seeking, and alternative medical practices play a role in accepting and using chatbots. In this study, we attempt to model the factors influencing the intention and the purpose of using the chatbot. Through a literature review, we identify the variables related to the adoption of healthcare chatbots. We then focus on the more relevant concepts to the Indian context and develop a conceptual model. Through cases and literature, we frame the propositions of the study. We look at the awareness of chatbot features, perception towards the chatbot, trust and mistrust of the healthcare system, the doctors and the chatbots, health-seeking behavior, and the belief in traditional, complementary, and alternative medicine prevalent in India. This study contributes by developing an initial conceptual model for healthcare chatbots adoption in the Indian context. In the future, we plan to operationalize the study and test the propositions through an elaborate survey to validate the model empirically. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Modeling, optimizing and diagnosis of chiller systems using machine learning /
Patent Number: 202141035122, Applicant: Dr B S Praveen Kumar.
Modeling, Optimizing and diagnosis of Chiller Systems using Machine Learning The invention aims to create an energy use model for a chiller in heating, ventilation, and air conditioning system using the artificial neural network learning method. Input layers that included several input variables, quantity (percentage) of training data and number of neurons were measured for accuracy by the suggested chiller energy consumption model. -
Modelling and analysis of split parallel hybrid electric vehicle based on 14 degrees of freedom
The paper studies the scope, performs the modelling and validation for conversion of any Convetional Vehicle to a Split Parallel Hybrid Electric Vehicle. The introduction of a smart Energy Management System for sucha setup is also evaluated. The EMS enables load sharing between the IC Engine and the Traction motor based on the gradient of the road. The gradient analysis is performed using the GPS based road gradient database. For the accurate modelling and the dynamic analysis of the designed model the performance of the vehicles Degrees of Freedom (DoF) for the variation in steering angle is analyzed. 14 DoF parameters are considered and the designed vehicle is subjected to variation in steer angle followed by the analysis on the response of the DoF parameters. BEIESP. -
Modelling and CFD simulation of vortex bladeless wind turbine
When the forces act on a bluff body in the wind flow direction, vortices are formed. Vortex bladeless wind turbine oscillates as a result of the vortices generated due to VIV. When the vortex shedding frequency is nearer to the natural frequency of the structure, maximum amplitude of vibration occurs and coincidentally power is generated. 3D models are designed to stimulate flow at a Reynolds number of 50000. This paper focuses on modelling the bladeless wind turbine based on semi-vortex angle and also 1) to study the vortices pattern and vorticity of different models 2) to study the drag and lift coefficients. In this paper vortex turbine is designed with certain parameters of dimension in Solid Edge and CFD analysis is carried out in Simscale software. Different model performance parameters like power, natural frequency and coefficient of power are compared among different models to opt for the best vortex bladeless wind turbine design. 2022 Author(s). -
Modelling and optimization of Rhodamine B degradation over Bi2WO6Bi2O3 heterojunction using response surface methodology
The Bi2O3/Bi2WO6 heterostructures of various compositions are prepared via the surfactant-assisted solgel method, which exhibits enhanced and synergistic photocatalytic activity towards the degradation of Rhodamine B (Rh B) using visible light irradiation. Characterization of these heterostructures has been done using X-ray diffraction, microscopic and spectroscopic methods. The 50% tungstate in bismuth oxide (BWO) nanocomposites having band gap of 2.85eV and an average size of 4080nm shows maximum dye removal up to 87% in 4h compared to pure Bi2O3 and other heterostructures of Bi2O3/Bi2WO6. The reusability studies demonstrate the excellent retention of photocatalytic activity without much loss in activity, implying the stability and efficiency of the prepared catalyst. The degradation of the Rh B dye is modeled mathematically to analyze the interactive effects of the key parameters like the time, amount of catalyst, and dye concentration, and to determine the optimal setting of these parameters to optimize the degradation process using the face-centered Central Composite Design (FC-CCD) of the Response Surface Methodology (RSM) analysis. An accurate full quadratic model has been developed with R2 = 99.41%. The sensitivity of the degradation was evaluated at all levels of the key parameters. At 0.1g of catalyst amount, it was found that the increment of the catalyst amount would be suitable for improved degradation as compared to allowing more time for the degradation. The maximum degradation was obtained for a dye concentration of 5ppm, and 0.1g catalyst for 4h. 2022, King Abdulaziz City for Science and Technology. -
Modelling Climate, COVID-19, and Reliability Data: A New Continuous Lifetime Model under Different Methods of Estimation
In this article, a new continuous probability distribution called Arvind distribution is developed and studied. The proposed distribution has only one parameter but it exhibits a wide variety of shapes for density and hazard rate functions. A number of important distributional properties including mode, quantile function, moments, skewness, kurtosis, mean deviation, probability-weighted moments, stress-strength reliability, order statistics, reliability and hazard rate functions, Bonferroni Lorenz and Zenga curves, conditional moments, mean residual and mean past life functions, and stochastic ordering of the Arvind distribution are derived. For point estimation of the parameter of the proposed distribution, six estimation procedures including maximum likelihood, maximum product spacings, least squares, weighted least squares, Cram-von Mises, and Anderson-Darling estimators are used. The interval estimation of the unknown parameter has also been discussed using observed Fishers information. A vast simulation study has been conducted to examine the behaviour of different estimation procedures. Finally, the applicability of the proposed model is demonstrated by using three real-life datasets. The results of the real data analysis clearly announce that the Arvind distribution can be a better alternative to several existing models for modelling different types of data from various fields. 2024, Society of Statistics, Computer and Applications. All rights reserved. -
Modelling for working capital efficiency: integrating SBM-DEA and artificial neural networks in Indian manufacturing
Purpose: This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN). Design/methodology/approach: A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME. Findings: Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME. Originality/value: The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively. 2024, Emerald Publishing Limited. -
Modelling Networks withAttached Storage Using Perfect Italian Domination
Network-attached storage (NAS) is how data is stored and shared among hosts through a configured network. This is cheaper yet the best solution for sharing and using any huge unstructured data in an organization. Optimal distribution of NAS in a network of servers can be done using the concept of Perfect Italian Domination (PID). PID is a vertex labelling where the vertices of a graph G are labelled by 0, 1, 2 such that a vertex with label 0 should have a neighbourhood where the summation of the labels is exactly 2. The minimum possible sum of the labels obtained for graph G is its PID number. A network in an organization can have any structure. It can be highly interconnected, like a graph obtained from the Join of two graphs or the Corona product of two graphs. Hence, this paper discusses the PID of different graphs generated by the Join and the Corona products. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.