This document presents material on simple linear regression models. It begins with definitions of key terms like response variable, predictor variables, and linear regression models. It then covers topics like estimation of model parameters, allocation of variation, confidence intervals for regression parameters and predictions, and visual tests for verifying regression assumptions. Examples are provided throughout to illustrate the concepts and calculations involved in simple linear regression.
An econometric model for Linear Regression using StatisticsIRJET Journal
This document discusses linear regression modeling using statistics. It begins by introducing linear regression and its assumptions. Both univariate and multivariate linear regression are covered. The coefficients are derived using statistics in matrix form. Properties of ordinary least squares estimators like their expected values and variances are proven. Hypothesis testing for multiple linear regression is presented in matrix form. The document emphasizes the importance of understanding linear regression for prediction and its application in fields like economics and social sciences. Rigorous statistical analysis is needed to ensure the validity of regression models.
DETECTION OF RELIABLE SOFTWARE USING SPRT ON TIME DOMAIN DATAIJCSEA Journal
In Classical Hypothesis testing volumes of data is to be collected and then the conclusions are drawn which may take more time. But, Sequential Analysis of statistical science could be adopted in order to decide upon the reliable / unreliable of the developed software very quickly. The procedure adopted for this is, Sequential Probability Ratio Test (SPRT). In the present paper we proposed the performance of SPRT on Time domain data using Weibull model and analyzed the results by applying on 5 data sets. The parameters are estimated using Maximum Likelihood Estimation.
This document discusses various statistical methods used in engineering. It covers topics like sample plans, capability studies, gauge R&R studies, comparative analysis, design of experiments (DOE), correlation, regression, reliability, and the DMAIC process in Six Sigma. DOE techniques like full factorial designs, fractional factorial designs, custom designs, evaluation of designs, response surface methods, and residuals are explained. The document provides examples and outlines the applications of these various statistical analysis methods.
Exponential software reliability using SPRT: MLEIOSR Journals
This document discusses using sequential probability ratio testing (SPRT) to analyze software reliability growth models (SRGMs). It proposes using SPRT on four datasets with an exponential SRGM to quickly determine if software is reliable or unreliable. The key steps are: 1) Define failure rate hypotheses for reliable (λ0) and unreliable (λ1) software; 2) Continuously monitor failure data and calculate the probability ratio of the data under each hypothesis; 3) Use SPRT decision rules to accept, reject, or continue testing based on if the ratio exceeds, is less than, or between thresholds. Maximum likelihood estimation is used to estimate SRGM parameters. SPRT allows decisions to be made much faster than traditional hypothesis testing by continuously
This document discusses autocorrelation, which occurs when there is a correlation between members of a series of observed data ordered over time or space. This violates an assumption of classical linear regression that error terms are uncorrelated. Causes of autocorrelation include inertia in macroeconomic data, specification bias from excluded or incorrectly specified variables, lags, data manipulation, and non-stationarity of time series data. Autocorrelation can be detected graphically or using the Durbin-Watson and Breusch-Godfrey tests. Remedial measures include first-difference transformation, generalized transformation, and using Newey-West standard errors.
This document discusses dummy variable models and testing for structural stability in regression analysis. It contains the following key points in 3 sentences:
The document defines dummy variable models, which allow for inclusion of qualitative independent variables by assigning numerical values, in regression analysis. It also discusses using dummy variables to account for seasonal or structural changes. Methods like Chow's test and including differential intercept and slope coefficients using dummy variables are presented to test for structural stability and identify if changes are in intercept and/or slope. The document concludes by defining limited dependent variable models which involve dependent variables that can only take on discrete values like binary choice models.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
A Theoretical Framework for Understanding Mutation-Based Testing MethodsDonghwan Shin
The document presents a theoretical framework for understanding mutation-based testing methods. It introduces a new testing factor called a "test differentiator" to formally describe the behavioral differences between programs and mutants. It extends the differentiator to a "d-vector" to represent differences over a set of tests. Positions in a multi-dimensional space are defined based on d-vectors to quantitatively analyze differences. A position deviance relation and position deviance lattice are defined as a graphical model to represent positions and their relationships. The framework can guide understanding of mutation-based testing and applications like identifying a minimal set of mutants.
An econometric model for Linear Regression using StatisticsIRJET Journal
This document discusses linear regression modeling using statistics. It begins by introducing linear regression and its assumptions. Both univariate and multivariate linear regression are covered. The coefficients are derived using statistics in matrix form. Properties of ordinary least squares estimators like their expected values and variances are proven. Hypothesis testing for multiple linear regression is presented in matrix form. The document emphasizes the importance of understanding linear regression for prediction and its application in fields like economics and social sciences. Rigorous statistical analysis is needed to ensure the validity of regression models.
DETECTION OF RELIABLE SOFTWARE USING SPRT ON TIME DOMAIN DATAIJCSEA Journal
In Classical Hypothesis testing volumes of data is to be collected and then the conclusions are drawn which may take more time. But, Sequential Analysis of statistical science could be adopted in order to decide upon the reliable / unreliable of the developed software very quickly. The procedure adopted for this is, Sequential Probability Ratio Test (SPRT). In the present paper we proposed the performance of SPRT on Time domain data using Weibull model and analyzed the results by applying on 5 data sets. The parameters are estimated using Maximum Likelihood Estimation.
This document discusses various statistical methods used in engineering. It covers topics like sample plans, capability studies, gauge R&R studies, comparative analysis, design of experiments (DOE), correlation, regression, reliability, and the DMAIC process in Six Sigma. DOE techniques like full factorial designs, fractional factorial designs, custom designs, evaluation of designs, response surface methods, and residuals are explained. The document provides examples and outlines the applications of these various statistical analysis methods.
Exponential software reliability using SPRT: MLEIOSR Journals
This document discusses using sequential probability ratio testing (SPRT) to analyze software reliability growth models (SRGMs). It proposes using SPRT on four datasets with an exponential SRGM to quickly determine if software is reliable or unreliable. The key steps are: 1) Define failure rate hypotheses for reliable (λ0) and unreliable (λ1) software; 2) Continuously monitor failure data and calculate the probability ratio of the data under each hypothesis; 3) Use SPRT decision rules to accept, reject, or continue testing based on if the ratio exceeds, is less than, or between thresholds. Maximum likelihood estimation is used to estimate SRGM parameters. SPRT allows decisions to be made much faster than traditional hypothesis testing by continuously
This document discusses autocorrelation, which occurs when there is a correlation between members of a series of observed data ordered over time or space. This violates an assumption of classical linear regression that error terms are uncorrelated. Causes of autocorrelation include inertia in macroeconomic data, specification bias from excluded or incorrectly specified variables, lags, data manipulation, and non-stationarity of time series data. Autocorrelation can be detected graphically or using the Durbin-Watson and Breusch-Godfrey tests. Remedial measures include first-difference transformation, generalized transformation, and using Newey-West standard errors.
This document discusses dummy variable models and testing for structural stability in regression analysis. It contains the following key points in 3 sentences:
The document defines dummy variable models, which allow for inclusion of qualitative independent variables by assigning numerical values, in regression analysis. It also discusses using dummy variables to account for seasonal or structural changes. Methods like Chow's test and including differential intercept and slope coefficients using dummy variables are presented to test for structural stability and identify if changes are in intercept and/or slope. The document concludes by defining limited dependent variable models which involve dependent variables that can only take on discrete values like binary choice models.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
A Theoretical Framework for Understanding Mutation-Based Testing MethodsDonghwan Shin
The document presents a theoretical framework for understanding mutation-based testing methods. It introduces a new testing factor called a "test differentiator" to formally describe the behavioral differences between programs and mutants. It extends the differentiator to a "d-vector" to represent differences over a set of tests. Positions in a multi-dimensional space are defined based on d-vectors to quantitatively analyze differences. A position deviance relation and position deviance lattice are defined as a graphical model to represent positions and their relationships. The framework can guide understanding of mutation-based testing and applications like identifying a minimal set of mutants.
State and disturbance estimation of a linear systems using proportional integ...Mustefa Jibril
This document discusses state and disturbance estimation for linear systems using proportional and proportional integral observers. It begins with an introduction to observer design and describes how proportional observers can estimate states but result in steady-state error in the presence of disturbances. Proportional integral observers are then presented as able to estimate states, disturbances, and outputs without steady-state error. The document provides mathematical models of plants, proportional observers, and proportional integral observers. It includes an illustrative example comparing state and output estimation using a proportional observer to state, output, and disturbance estimation using a proportional integral observer. Simulation results demonstrate the proportional observer has steady-state error after a disturbance, while the proportional integral observer accurately estimates states, disturbances, and outputs both with and without
IRJET- Stress Concentration of Plate with Rectangular CutoutIRJET Journal
This document discusses a study that analyzes stress concentration in plates with various shaped cutouts using finite element analysis software (ANSYS). The study compares stress concentration factors around circular, square, and rectangular cutouts in metallic plates made of materials like mild steel and aluminum. Experimental tensile tests are also conducted on plates with different cutout shapes and loaded in one direction. Results from the finite element analysis are validated by comparing with experimental test results, finding them to be relatively similar. The stress concentration is highest for plates with cutouts that have more oriented geometries compared to a baseline. Orientation is identified as an important factor in reducing stress concentration.
This document summarizes a research paper that proposes using a two-step sequential probability ratio test (SPRT) approach to analyze software reliability growth model (SRGM) data. Specifically, it applies the approach to the Half Logistic Software Reliability Growth Model (HLSRGM). The SPRT approach allows drawing conclusions about software reliability from sequential or continuous monitoring of failure data, potentially reaching conclusions more quickly than traditional hypothesis testing. Equations are provided for determining acceptance, rejection, and continuation regions based on comparing observed failure counts to lines derived from the HLSRGM mean value function. The approach is applied to five sets of existing software failure data to analyze results.
This document describes specification tests that can be used after estimating dynamic panel data models using the generalized method of moments (GMM) estimator. It presents GMM estimators for first-order autoregressive models with individual fixed effects that exploit moment restrictions from assuming serially uncorrelated errors. Monte Carlo simulations are used to evaluate the small-sample performance of tests of serial correlation based on GMM residuals, Sargan tests, and Hausman tests. The tests are also applied to estimated employment equations using an unbalanced panel of UK firms.
How to evaulate the unusualness (base rate) of WJ IV cluster or test score di...Kevin McGrew
The WJ IV provides two primary methods for comparing tests or cluster scores. One is based on a predictive model (the variation and comparison procedures) and the other allows comparisons of SEM confidence bands, which takes into account each measures reliability. A third method for comparing scores, one that takes into account the correlation between compared measures (ability cohesion model) is not provided, but is frequently used by assessment professionals. The three types of score comparison methods are described and new information, via a "rule of thumb" summary slide and nomograph, are provided to allow WJ IV users to evaluate scores via all three methods.
Short-term load forecasting with using multiple linear regression IJECEIAES
This document discusses short-term load forecasting using multiple linear regression. It summarizes the research method used, which involves developing a multiple linear regression model to predict electrical load based on variables like temperature, humidity, day of week, and previous load data. The model is trained on historical load and weather data from New York City over 9 years. Testing shows the model can predict load a day ahead with 5.15% mean absolute percentage error. Regression coefficients, t-statistics, and p-values indicate the trained model explains about 90% of the variation in load and the predictors are statistically significant. An example day-ahead hourly load forecast is provided for June 25, 2019.
20 ms-me-amd-06 (simple linear regression)HassanShah124
This document discusses simple linear regression. It defines simple linear regression as having one independent variable and a linear relationship between the independent and dependent variables. The simple linear regression model is presented as Yi = β0 + β1Xi + Ԑi, where β0 is the intercept and β1 is the slope. Formulas to estimate the regression line and calculate statistics like the F-test, t-test, and R-squared are also provided. An example is worked through to demonstrate how to apply simple linear regression to a real data set.
POSTERIOR RESOLUTION AND STRUCTURAL MODIFICATION FOR PARAMETER DETERMINATION ...IJCI JOURNAL
When only a few lower modes data are available to evaluate a large number of unknown parameters, it is
difficult to acquire information about all unknown parameters. The challenge in this kind of updation
problem is first to get confidence about the parameters that are evaluated correctly using the available
data and second to get information about the remaining parameters. In this work, the first issue is resolved
employing the sensitivity of the modal data used for updation. Once it is fixed that which parameters are
evaluated satisfactorily using the available modal data the remaining parameters are evaluated employing
modal data of a virtual structure. This virtual structure is created by adding or removing some known
stiffness to or from some of the stories of the original structure. A 12-story shear building is considered for
the numerical illustration of the approach. Results of the study show that the present approach is an
effective tool in system identification problem when only a few data is available for updation.
IRJET- Analysis of Chi-Square Independence Test for Naïve Bayes Feature Selec...IRJET Journal
This document analyzes using the Chi-Square Independence Test for feature selection in Naive Bayes classification. It uses a student performance dataset to test the Chi-Square Independence Test at different confidence intervals for feature selection. The Chi-Square Test is used to determine whether features are independent or associated with the classification attribute. Features with lower p-values have a stronger association. Naive Bayes models are then built using different feature sets selected at different confidence intervals and evaluated based on their accuracy in 2-class and 5-class classifications of student performance. The results show higher accuracy when using grade features and features selected at higher confidence intervals.
This document provides an overview of regression analysis, including what regression is, how it works, assumptions of regression, and how to assess the model fit and check assumptions. Regression allows us to predict a dependent variable from one or more independent variables. Key steps discussed include checking the normality, homoscedasticity and independence of residuals, identifying influential observations, and addressing issues like multicollinearity. Graphical methods like normal probability plots and scatter plots of residuals are presented as ways to check assumptions.
BPSO&1-NN algorithm-based variable selection for power system stability ident...IJAEMSJORNAL
Due to the very high nonlinearity of the power system, traditional analytical methods take a lot of time to solve, causing delay in decision-making. Therefore, quickly detecting power system instability helps the control system to make timely decisions become the key factor to ensure stable operation of the power system. Power system stability identification encounters large data set size problem. The need is to select representative variables as input variables for the identifier. This paper proposes to apply wrapper method to select variables. In which, Binary Particle Swarm Optimization (BPSO) algorithm combines with K-NN (K=1) identifier to search for good set of variables. It is named BPSO&1-NN. Test results on IEEE 39-bus diagram show that the proposed method achieves the goal of reducing variables with high accuracy.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
State and disturbance estimation of a linear systems using proportional integ...Mustefa Jibril
This document discusses state and disturbance estimation for linear systems using proportional and proportional integral observers. It begins with an introduction to observer design and describes how proportional observers can estimate states but result in steady-state error in the presence of disturbances. Proportional integral observers are then presented as able to estimate states, disturbances, and outputs without steady-state error. The document provides mathematical models of plants, proportional observers, and proportional integral observers. It includes an illustrative example comparing state and output estimation using a proportional observer to state, output, and disturbance estimation using a proportional integral observer. Simulation results demonstrate the proportional observer has steady-state error after a disturbance, while the proportional integral observer accurately estimates states, disturbances, and outputs both with and without
IRJET- Stress Concentration of Plate with Rectangular CutoutIRJET Journal
This document discusses a study that analyzes stress concentration in plates with various shaped cutouts using finite element analysis software (ANSYS). The study compares stress concentration factors around circular, square, and rectangular cutouts in metallic plates made of materials like mild steel and aluminum. Experimental tensile tests are also conducted on plates with different cutout shapes and loaded in one direction. Results from the finite element analysis are validated by comparing with experimental test results, finding them to be relatively similar. The stress concentration is highest for plates with cutouts that have more oriented geometries compared to a baseline. Orientation is identified as an important factor in reducing stress concentration.
This document summarizes a research paper that proposes using a two-step sequential probability ratio test (SPRT) approach to analyze software reliability growth model (SRGM) data. Specifically, it applies the approach to the Half Logistic Software Reliability Growth Model (HLSRGM). The SPRT approach allows drawing conclusions about software reliability from sequential or continuous monitoring of failure data, potentially reaching conclusions more quickly than traditional hypothesis testing. Equations are provided for determining acceptance, rejection, and continuation regions based on comparing observed failure counts to lines derived from the HLSRGM mean value function. The approach is applied to five sets of existing software failure data to analyze results.
This document describes specification tests that can be used after estimating dynamic panel data models using the generalized method of moments (GMM) estimator. It presents GMM estimators for first-order autoregressive models with individual fixed effects that exploit moment restrictions from assuming serially uncorrelated errors. Monte Carlo simulations are used to evaluate the small-sample performance of tests of serial correlation based on GMM residuals, Sargan tests, and Hausman tests. The tests are also applied to estimated employment equations using an unbalanced panel of UK firms.
How to evaulate the unusualness (base rate) of WJ IV cluster or test score di...Kevin McGrew
The WJ IV provides two primary methods for comparing tests or cluster scores. One is based on a predictive model (the variation and comparison procedures) and the other allows comparisons of SEM confidence bands, which takes into account each measures reliability. A third method for comparing scores, one that takes into account the correlation between compared measures (ability cohesion model) is not provided, but is frequently used by assessment professionals. The three types of score comparison methods are described and new information, via a "rule of thumb" summary slide and nomograph, are provided to allow WJ IV users to evaluate scores via all three methods.
Short-term load forecasting with using multiple linear regression IJECEIAES
This document discusses short-term load forecasting using multiple linear regression. It summarizes the research method used, which involves developing a multiple linear regression model to predict electrical load based on variables like temperature, humidity, day of week, and previous load data. The model is trained on historical load and weather data from New York City over 9 years. Testing shows the model can predict load a day ahead with 5.15% mean absolute percentage error. Regression coefficients, t-statistics, and p-values indicate the trained model explains about 90% of the variation in load and the predictors are statistically significant. An example day-ahead hourly load forecast is provided for June 25, 2019.
20 ms-me-amd-06 (simple linear regression)HassanShah124
This document discusses simple linear regression. It defines simple linear regression as having one independent variable and a linear relationship between the independent and dependent variables. The simple linear regression model is presented as Yi = β0 + β1Xi + Ԑi, where β0 is the intercept and β1 is the slope. Formulas to estimate the regression line and calculate statistics like the F-test, t-test, and R-squared are also provided. An example is worked through to demonstrate how to apply simple linear regression to a real data set.
POSTERIOR RESOLUTION AND STRUCTURAL MODIFICATION FOR PARAMETER DETERMINATION ...IJCI JOURNAL
When only a few lower modes data are available to evaluate a large number of unknown parameters, it is
difficult to acquire information about all unknown parameters. The challenge in this kind of updation
problem is first to get confidence about the parameters that are evaluated correctly using the available
data and second to get information about the remaining parameters. In this work, the first issue is resolved
employing the sensitivity of the modal data used for updation. Once it is fixed that which parameters are
evaluated satisfactorily using the available modal data the remaining parameters are evaluated employing
modal data of a virtual structure. This virtual structure is created by adding or removing some known
stiffness to or from some of the stories of the original structure. A 12-story shear building is considered for
the numerical illustration of the approach. Results of the study show that the present approach is an
effective tool in system identification problem when only a few data is available for updation.
IRJET- Analysis of Chi-Square Independence Test for Naïve Bayes Feature Selec...IRJET Journal
This document analyzes using the Chi-Square Independence Test for feature selection in Naive Bayes classification. It uses a student performance dataset to test the Chi-Square Independence Test at different confidence intervals for feature selection. The Chi-Square Test is used to determine whether features are independent or associated with the classification attribute. Features with lower p-values have a stronger association. Naive Bayes models are then built using different feature sets selected at different confidence intervals and evaluated based on their accuracy in 2-class and 5-class classifications of student performance. The results show higher accuracy when using grade features and features selected at higher confidence intervals.
This document provides an overview of regression analysis, including what regression is, how it works, assumptions of regression, and how to assess the model fit and check assumptions. Regression allows us to predict a dependent variable from one or more independent variables. Key steps discussed include checking the normality, homoscedasticity and independence of residuals, identifying influential observations, and addressing issues like multicollinearity. Graphical methods like normal probability plots and scatter plots of residuals are presented as ways to check assumptions.
BPSO&1-NN algorithm-based variable selection for power system stability ident...IJAEMSJORNAL
Due to the very high nonlinearity of the power system, traditional analytical methods take a lot of time to solve, causing delay in decision-making. Therefore, quickly detecting power system instability helps the control system to make timely decisions become the key factor to ensure stable operation of the power system. Power system stability identification encounters large data set size problem. The need is to select representative variables as input variables for the identifier. This paper proposes to apply wrapper method to select variables. In which, Binary Particle Swarm Optimization (BPSO) algorithm combines with K-NN (K=1) identifier to search for good set of variables. It is named BPSO&1-NN. Test results on IEEE 39-bus diagram show that the proposed method achieves the goal of reducing variables with high accuracy.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.