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Analysis and Actions Planned for Programme Outcomes in Outcome Based Education for a Particular Course
In India many of the technical institutions are NBA (National Board of Accreditation) accredited and the accreditation is a way to maintain quality of education. The outcome-based education (OBE) plays an important role in technical education across the world. So, in this research we will show how we can implement the attainment process related to OBE for a particular course. In this paper we will discuss how the course outcome and mapping of course outcome with program outcome can be defined. Then we will discuss the process to calculate the attainment. Finally, the program gaps were identified for that course and actions were suggested. 2024 IEEE. -
Analysis and dynamics of the Ivancevic option pricing model with a novel fractional calculus approach
The aim of the current study is to capture the complex behavior of the Ivancevic option pricing (IOP) model using the (Formula presented.) -homotopy analysis transform method ((Formula presented.) -HATM) with novel fractional operator. The generalization of the Black-Scholes model with the nonlinear Schringer equation plays a pivotal role in financial mathematics in studying the option-pricing wave function associated with two parameters. Based on adaptive market potential and volatility constant with distinct initial situations, we hired three distinct cases to exemplify the ability of (Formula presented.) -HATM. The considered method is elegant unification of the (Formula presented.) -homotopy analysis and Laplace transform algorithms. The derivative of fractional order is projected with the Atangana-Baleanu (AB) operator. The fixed-point theorem is used to present the existence and uniqueness of the attained result for the considered model, and we hire five distinct initial conditions. The hired scheme is highly methodical and exact to analyze the insights of the complex system with integer and fractional order exemplifying associated areas of science, which can be observed using plots and table. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Analysis and Forecasting of Area Under Cultivation of Rice in India: Univariate Time Series Approach
This study uses three distinct models to analyse a univariate time series of data: Holt's exponential smoothing model, the autoregressive integrated moving average (ARIMA) model, and the neural network autoregression (NNAR) model. The effectiveness of each model is assessed using in-sample forecasts and accuracy metrics, including mean absolute percentage error, mean absolute square error, and root mean square log error. The area under cultivation in India for the following 5years is predicted using the model whose fitted values are most like the observed values. This is determined by performing a residual analysis. The time series data used for the study was initially found to be non-stationary. It is then transformed into stationary data using differencing before the models can be used for analysis and prediction. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Analysis and Forecasting of Crude Oil Price Based on Univariate and Multivariate Time Series Approaches
This paper discusses the notion of multivariate and univariate analysis for the prediction of crude oil price in India. The study also looks at the long-term relationship between the crude oil prices and its petroleum products price such as diesel, gasoline, and natural gas in India. Both univariate and multivariate time series analyses are used to predict the relationship between crude oil price and other petroleum products. The Johansen cointegration test, EngleGranger test, vector error correction (VEC) model, and vector auto regressive (VAR) model are used in this study to assess the long- and short-run dynamics between crude oil prices and other petroleum products. Prediction of crude oil price has also been modeled with respect to the univariate time series models such as autoregressive integrated moving average (ARIMA) model, Holt exponential smoothing, and generalized autoregressive conditional heteroskedasticity (GARCH). The cointegration test indicated that diesel prices and crude oil prices have a long-run link. The Granger causality test revealed a bidirectional relationship between the price of diesel and the price of gasoline, as well as a unidirectional association between the price of diesel and the price of crude oil. Based on in-sample forecasts, accuracy metrics such as root mean square logarithmic error (RMSLE), mean absolute percentage error (MAPE), and mean absolute square error (MASE) were derived, and it was discovered that VECM and ARIMA models can efficiently predict crude oil prices. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Analysis and measurement of supply chain flexibility
Supply chain flexibility is a strategic and tactical necessity for sustenance and progress of business enterprises. Measurement of flexibility is therefore necessary for its monitoring, control and communication. The study proposes a framework and a methodology for flexibility performance measurement of supply chains. The framework identifies flexibility objectives and its contributing attributes at four levels of the supply chain and suggests taxonomy of flexibility performance measures. A methodology to prioritise the contribution of each performance attribute to achieve the desired flexibility objective using analytic hierarchy process (AHP) has also been proposed and demonstrated in this study. The research is based on detailed literature-based study and analysis of existing frameworks of flexibility performance measurement in supply chain and expert opinion. The proposed framework is suitable for measurement, monitoring and controlling flexibility in a supply chain in addition to prioritising contributing attributes of flexibility. The research does not test the model but suggests a platform for further development. Copyright 2015 Inderscience Enterprises Ltd. -
Analysis and optimization of uplink spectral efficiency in massive multiple-input and multiple-output
Fifth Generation (5G) specifications aims for data rate of 1 Gbps in high mobility and 10 Gbps in low mobility conditions, 15-30 bps/Hz of spectral efficiency with less than 1 milli second (ms) latency reduction. Massive multiple-input and multiple-output (Massive MIMO) is one of the promising technologies in 5G standard which offers a high spectral efficiency improvement. This work focus on the uplink scenario spectral efficiency in a Massive MIMO simulation network based on third generation partnership project (3GPP) and long term evolution (LTE) document of 5G. This work analyzes the spectral efficiency metric by simulating the 5G Massive MIMO network. Then, the research identified major constraint parameters; number of user antennas, K, number of base station antennas, M, transmission power, P, channel bandwidth, B, and coherence time, Tau_C and pilot time Tau_P which plays a significant role in varying this metric. The authors focus on improving the spectral efficiency by passing these constraint parameters through different meta-heurestic optimization algorithms, such as, convex optimization solver, White shark optimization (WSO) and Particle swarm optimization (PSO). The results show an overall, 1-10 percent of improvement of the parameter wnen compared with other research articles. The maximum value achieved is 49.84 bps/Hz, which is three times higher as per to the 3GPP and International Telecommunication Unioin (ITU) release document. 2022 Institute of Advanced Engineering and Science. All rights reserved. -
Analysis and prediction of Indian stock market: a machine-learning approach
Prediction of financial stock market is a challenging task because of its volatile and non- linear nature. The presence of different factors like psychological, sentimental state, rational or irrational behaviour of investors make the stock market more dynamic. With the inculcation of algorithms based on artificial intelligence, deep learning algorithms, the prediction of movement of financial stock market is revolutionized in the recent years. The purpose of using these algorithms is to help the investors for taking decisions related to the Stock Pricing. A model has been proposed to predict the direction of movement of Indian stock market in the near future. This model makes use of historical Indian stock data of companies in nifty 50 since they came existence along with some financial and social indicators like financial news and tweets related to stocks. After pre-processing and normalization various machine learning algorithms like LSTM, support vector machines, KNearest neighbour, random forest, gradient boosting regressor are applied on this time series data to produce better accuracy and to minimize the RMSE error. This model has the ability to reduce major losses to the investors who invest in stock market. The social indicators will give an insight for predicting the direction of stock market. The LSTM network will make use of historical closing prices, tweets and trading volume. 2023, The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden. -
Analysis and prediction of seed quality using machine learning
The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed dataset. Using data that can be used to forecast the future, this model is used to learn about whether the seeds are of premium quality, standard quality, or regular quality. While testing data are employed in the algorithms predictive analytics, training data and validation data are used for categorization reasons. Thus, by examining the training accuracy of the convolution neural network (CNN) model and the prediction accuracy of the algorithm, the projects primary goal is to develop the best method for the more accurate prediction of seed quality. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Analysis and Prediction of Suitable Model for Coconut Production Estimates in South Indian States
The study attempts to forecast coconut production in major coconut-producing states in India. The future projections on coconut production have been calculated based on yearly data for 73 years (194950 to 202122) accessed from the database of Indiastat (2022). We have used prominent forecasting techniques for the purpose and a suitable model has been chosen based on the lowest results of MAPE. The damped linear trend has been chosen for forecasting coconut production in Karnataka whereas Differenced first-order Auto Regressive model with drift has been adopted for Kerala and Karnataka. This study has considered a large dataset compared to other existing works and has chosen states that produce coconut on a large scale in India. Along with this, this study also attempts to find which state will produce more nuts for the Indian coconut industry, which can help the concerned stakeholders to take necessary decisions. Future projections depict that Kerala will continue to be the largest producer of coconut and Karnataka will show remarkable performance in coconut production during the upcoming four years post-study period. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Analysis and predictions of spread, recovery, and death caused by COVID-19 in India
The novel coronavirus outbreak was first reported in late December 2019 and more than 7 million people were infected with this disease and over 0.40 million worldwide lost their lives. The first case was diagnosed on 30 January 2020 in India and the figure crossed 0.24 million as of 6 June 2020. This paper presents a detailed study of recently developed forecasting models and predicts the number of confirmed, recovered, and death cases in India caused by COVID-19. The correlation coefficients and multiple linear regression applied for prediction and autocorrelation and autoregression have been used to improve the accuracy. The predicted number of cases shows a good agreement with 0.9992 R-squared score to the actual values. The finding suggests that lockdown and social distancing are two important factors that can help to suppress the increasing spread rate of COVID-19. 2018 Tsinghua University Press. -
Analysis of a Fractional Stage-Structured Model With CrowleyMartin Type Functional Response by Lagrange Polynomial Based Method
The dynamics of a stage-structured predator-prey system that replicates interactions between two densities of prey and predator populations were investigated in this work. The adult predator population and the juvenile predator are the two compartments that make up the predator population in the model. The predator relies on both prey and juvenile predator, which is another element of the paradigm that can be termed cannibalism. CrowleyMartin type functional denotes the nature of the interaction between prey and adult predators, while Holling type-I functional response denotes the nature of contact between juvenile and adult predators. The concept of memory is introduced in the form of the Caputo fractional derivative to reflect the complicated dynamics of interaction among the species. As a result, the model is able to incorporate all relevant historical information about the occurrence, from its inception to the desired time, into its calculations. We have also investigated the boundedness and existence and uniqueness of solutions to the proposed model. The condition of existence and stability of various points of equilibrium are investigated. The numerical simulations are performed by using the Lagrange polynomial-based method which is novel in the field of mathematical biology. Simulations have been accomplished to examine the significance of parameters related to cannibalism, the conversion rate from prey to adult predator, harvesting of an adult predator, and growth rate of juvenile predators on the overall behavior of the system. The noteworthy performance of the fractional operator on the anticipated predator-prey models dynamical behavior is well demonstrated by numerical results. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Analysis of a magnetic field and Hall effects in nanoliquid flow under insertion of dust particles
In this study, the two-phase hydromagnetic flow of a viscous liquid through a suspension of dust and nanoparticles is considered. The influence of the Hall current is also taken into account. The similarity variables are utilized to transform the problem into one independent variable. The obtained expressions in one independent variable are solved through the RungeKuttaFehlberg scheme connected with the shooting procedure. The computed results are sketched for employing multiple values of physical constraints on the temperature and velocity of the nanofluid and dust phase. The characterization of various nanoparticles like Cu, Al2O3, TiO2, and Ag on velocities and temperatures of both phases is made through plots. A comparative analysis in the limiting approach is presented to justify the present solution methodology. The range of emerging parameters is taken as 0 ? l ? 3, 0.1 ? ?t ? 3, 0 ? m ? 2.5, 0 ? M2 ? 2, 0.1 ? ?v ? 3, 0 ? ? ? 0.4, and ?0.8 ? ? ? 0.8. From the study, it is revealed that ?t has theopposite effect on the temperature of dust and nanofluid phases. The Hall parameter mraisesthe profiles of velocities in the nanoliquid and dust phases. Also, it is found that the transverse velocities h(?) and H((?) andtemperatures ?(?) and ?p(?) rise for larger ?. 2020 Wiley Periodicals, Inc. -
Analysis of a mathematical model of the aggregation process of cellular slime mold within the frame of fractional calculus
The pivotal aim of the present study is to find the solution for a nonlinear system describing the aggregation process of cellular slime mold by using (Formula presented.) -homotopy analysis transform method. The coupled system is considered within the frame of the Caputo fractional operator. We examine three different cases with distinct values of sensitivity function (Formula presented.), (Formula presented.) and (Formula presented.) to exemplify the efficiency and applicability of the considered scheme. We capture the nature of the obtained results with respect to the fractional order, with distinct initial conditions, and illustrate them using 2D and 3D plots for particular values of the parameters. The considered scheme offers parameters, which help to adjust the convergence region, and we plotted the ?-curves to dissipate the effect in the present framework. Moreover, some simulating and important behavior of the considered model using attained results shows the prominence of the hired operator while analyzing the coupled equations and confirms the competence of the projected scheme. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
Analysis of an Existing Method for Detecting Adversarial Attacks on Deep Neural Networks
Analyzes the existing method of detecting adversarial attacks on deep neural networks, proposed by researchers from Carnegie Mellon University and the Korean Institute of Advanced Technologies (KAIST) Ko, G. and Lim, G in 2021. Examines adversarial attacks, as well as the history of research on the topic. The paper considers the concepts of interpreted and not interpreted neural networks and features of methods of protection of the types of neural networks considered. The method for protecting against adversarial attacks is also considered to be applicable to both types of neural networks. An example of an attack simulation is given, which makes it possible to identify a sign showing that an attack has been committed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Analysis of attention deficit hyperactivity disorder using various classifiers
Attention Deficit Hyperactivity Disorder (ADHD) is a neurobehavioral childhood impairment that wipes away the beauty of the individual from a very young age. Data mining classification techniques which are becoming a very important field in every sector play a vital role in the analysis and identification of these disorders. The objective of this paper is to analyze and evaluate ADHD by applying different classifiers like Nae Bayes, Bayes Net, Sequential Minimal Optimization, J48 decision tree, Random Forest, and Logistic Model Tree. The dataset employed in this paper is the first publicly obtainable dataset ADHD-200 and the instances of the dataset are classified into low, moderate, and high ADHD. The analysis of the performance metrics and therefore the results show that the Random Forest classifier offers the highest accuracy on ADHD dataset compared to alternative classifiers. With the current need to provide proper evaluation and management of this hyperactive disorder, this research would create awareness about the influence of ADHD and can help ensure the proper and timely treatment of the affected ones. Springer Nature Singapore Pte Ltd 2021. -
Analysis of benchmark image pre-processing techniques for coronary angiogram images
Coronary Artery supplies oxygenated blood and nutrients to the heart muscles. It can be narrow by the plaque deposited on the artery wall. Cardiologists and radiologists diagnose the disease through visual inspection based on x-ray images. It is a challenging part for them to identify the plaque in the artery in the given imagery. By using image processing and pattern recognition techniques, a narrowed artery can be identified. In this paper, pre-processing methods of image processing are discussed with respect to coronary angiogram image(s). In general the angiogram images are affected by device generated noise / artifacts; pre-processing techniques help to reduce the noise in the image and to enhance the quality of the image so that the region of interest is sensed. The main objective of the medical image analysis is to localize the region of interest by removing the noise. It is essential to find the structure of the artery in the angiogram image, for that preprocessing is useful. 2021 IEEE. -
Analysis of Cardiovascular Diseases Prediction Using Machine Learning Classification Algorithms
Worldwide healthcare systems have faced enormous hurdles because of the COVID-19 pandemic, especially when it comes to treating individuals who already have pre-existing disorders such as cardiovascular diseases (CVDs). Prioritizing medical therapies and resources for COVID-19 patients who are at increased risk of mortality from underlying CVDs requires early identification. In this work, we investigate how well three machine learning algorithms-, Random Forest, XGBoost, and Logistic Regression-predict death in COVID-19 patients who already have cardiovascular disease. We performed grid search and cross-validation using a dataset of clinical and demographic features of COVID-19 patients with and without CVDs to reduce overfitting and maximize model performance. Our findings show that among patients with CVDs, Logistic Regression had the best accuracy in predicting COVID-19 fatality, followed by Random Forest and Decision Tree coming in a close second. These results highlight how machine learning algorithms can help clinical professionals detect high-risk COVID-19 patients who have underlying cardiovascular diseases (CVDs), enable prompt interventions, and enhance patient outcomes. 2024 IEEE. -
Analysis of Challenges Experienced by Students with Online Classes During the COVID-19 Pandemic
In the current context of the COVID-19 pandemic, due to restrictions in mobility and the closure of schools, people had to shift to work from home. India has the worlds second-largest pool of internet users, yet half its population lacks internet access or knowledge to use digital services. The shift to online mediums for education has exposed the stark digital divide in the education system. The digitization of education proved to be a significant challenge for students who lacked the devices, internet facility, and infrastructure to support the online mode of education or lacked the training to use these devices. These challenges raise concerns about the effectiveness of the future of education, as teachers and students find it challenging to communicate, connect, and assess meaningful learning. This study was conducted at one of the universities in India using a purposive sampling method to understand the challenges faced by the students during the online study and their satisfaction level. This paper aims to draw insight from the survey into the concerns raised by students from different backgrounds while learning from their homes and the decline in the effectiveness of education. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Analysis of chromosomal aberrations and micronuclei in type 2 diabetes mellitus patients
Introduction: Type 2 diabetes mellitus is a metabolic disorder characterized by insulin resistance and disrupted insulin secretion. It is often linked to injuries, malfunction and failure of several organs in the long term. The elevated chromosomal disruptions and genetic complications in diabetic patients are due to the increased production of reactive oxygen species. Materials and Methods: The current study used chromosomal aberration assay and micronucleus assay to analyze the extent of abnormalities in the subjects. Results: The results showed increase in frequency of chromosomal aberrations in diabetic patients when compared to the control group (2.761.65 and 0.470.75 respectively). They also showed higher levels of micronuclei formation than the control participants (13.288.63 and 4.128.89 respectively). The correlation analysis indicated positive relationship between total aberrations and duration of diabetes. Conclusion: These results indicate that diabetes is associated with genomic instability and studies at a genetic level can be employed for early detection. 2020, West Asia Organization for Cancer Prevention. -
Analysis of club convergence for economies: identification and testing using development indices
This paper attempts to identify club convergence using the procedure suggested by Phillips and Sul (Phillips and Sul, Econometrica 75:17711855, 2007, Phillips and Sul, J Appl Economet 24:11531185, 2009) based on GDP per capita for 102 countries across the globe for the time period 1996 through 2015. The results indicate the presence of five clubs with four countries belonging to the non- convergent group. After identifying the clubs, the study analyzed the transitional behaviors among the clubs. Finally, to understand the determinant of the club membership, we used the ordered logit model by considering the initial level of GDP, gross capital formation, growth rate of population, and four indices, namely social, governance, sustainability, and globalization as the explanatory variables. The results suggest that the initial level of GDP per capita, gross capital formation, social, governance, sustainability, and globalization are the major factors for determining the club. 2021, The Japan Section of the Regional Science Association International.