In recent years, there has been rapid development in Artificial Intelligence (AI), with a particular focus on image captioning, which has garnered significant interest among scientists. This field involves automatical...
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The paper provides an explicit outlook on the development of a comprehensive sentiment analysis systems for online social media by targeting user-generated text and images on platforms such as Twitter, Facebook, and I...
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Autonomous vehicles face significant challenges in accurately identifying vehicles, objects, and traffic signals under adverse weather conditions and poor lighting. To address these issues, we introduce a novel detect...
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Twitter is a radiant platform with a quick and effective technique to analyze users’perceptions of activities on social *** researchers and industry experts show their attention to Twitter sentiment analysis to recog...
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Twitter is a radiant platform with a quick and effective technique to analyze users’perceptions of activities on social *** researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder *** sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning *** assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine(SVM).This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare,behaviour estimation,*** addition,the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive,negative and neutral *** this work,we obligated Twitter Application programming Interface(API)account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account *** distinguish the results in terms of the performance evaluation,an error analysis investigates the features of various stakeholders comprising social media analytics researchers,Natural Language Processing(NLP)developers,engineering managers and experts involved to have a decision-making approach.
Mastery Learning is a pedagogical strategy that allows students to demonstrate mastery of the skills acquired in a course over multiple attempts. Failed attempts are used to provide feedback and are not factored in th...
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The fundamental objective of this paper is to study the effectiveness of magnetic field and gravity on an isotropic homogeneous thermoelastic structure based on four theories of generalized *** another meaning,the mod...
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The fundamental objective of this paper is to study the effectiveness of magnetic field and gravity on an isotropic homogeneous thermoelastic structure based on four theories of generalized *** another meaning,the models of coupled dynamic theory(CDT),Lord-Shulman(LS),Green-Lindsay(GL)as well as Green-Naghdi(GN II)will be taken in the ***,applying the harmonic method(normal mode technique),the solution of the governing equations and the expressions for the components of the displacement,temperature and(Mechanical and Maxwell’s)stresses is taken into account and calculated *** impacts of the gravity and magnetic field are illustrated graphically which are pronounced on the different physical ***,the results of some research that others have previously obtained may be found some or all of them as special cases from this study.
This research investigates the use of machine learning techniques, specifically Multiple Linear Regression (MLR) and Adaptive Neuro-Fuzzy Inference System (ANFIS), to predict sea level rise in the Coral Sea region. Cl...
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The difficulty with dynamic and heterogeneous natured edge computing environments is resource provisioning. Reinforcement Learning (RL) can be promising to solve the problems of resource allocation under conditions of...
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Electroencephalography (EEG) is a non-intrusive method used to capture electrical potential generated by brain neurons, which is crucial for diagnosing neurological disorders like epilepsy, sleep disorders, brain tumo...
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Traditional yield curve models, such as the Nelson–Siegel parsimonious model, excel in accurately depicting the interest rate term structure when applied to smoothly evolving yield curve data. Nelson–Siegel's mo...
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Traditional yield curve models, such as the Nelson–Siegel parsimonious model, excel in accurately depicting the interest rate term structure when applied to smoothly evolving yield curve data. Nelson–Siegel's model has garnered widespread international adoption due to the meaningful economic interpretations it provides for its parameters. In the context of developed capital markets, where abrupt economic fluctuations are infrequent, the smoothness of yield curve data presents no significant hurdles in the modeling of interest rate curves. However, Asian Frontier countries that include Sri Lanka, Pakistan, Bangladesh, and Vietnam used to face economic downturns frequently and the impact of such conditions influenced the country substantially. Adopting the most flexible and interpretable yield curve model such as the aynamic Nelson–Siegel (NS) model is a challenge under such conditions. The yield curve data from January 2010 to 2022 was examined and clustered into steady-state and non-steady-state data based on inflation and exchange rate movement for each country. The accuracy of the Nelson–Siegel model was observed to decline in non-steady-state conditions compared to steady-state scenarios, as indicated by lower R-squared values and higher mean absolute deviation (MAD). Furthermore, the data exhibited varying degrees of smoothness between these two states, with a higher degree of smoothness observed in steady-state conditions based on the autocorrelation function. Several smoothing techniques, including spline smoothing, super smoothing, and LOWESS smoothing, were applied to both steady-state and non-steady-state datasets. LOWESS smoothing and spline smoothing appear to excel in achieving smoother results for steady-state data, whereas super smoothing and spline smoothing prove more effective in non-steady-state situations. Notably, spline smoothing outperformed other smoothing techniques in model accuracy tests, such as R-squared and MAD, suggesting that smoothing
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