This document summarizes a study comparing different classification models for identifying liver disease types using patient data. It describes applying four classification algorithms - First Order Inductive Learner (FOIL), Classification Based on Association (CBA), Classification based on Multiple Association Rules (CMAR), and Classification based on Predictive Association Rules (CPAR) - to data on liver function tests, other health factors, and diagnosed disease for each patient. Dimensionality reduction was used as a preprocessing step to remove ambiguous attributes. The models were trained on full patient data and tested on replicated data, with results showing accuracy and training time for each classifier. Analysis focused on using the algorithms to identify viral, alcoholic, and non-alcoholic liver diseases.
IRJET-Performance Analysis of Liver Disease Prediction using Machine Learning...IRJET Journal
This document discusses using machine learning algorithms to predict liver disease based on patient dataset features. It first describes the liver and common liver disorders like fatty liver, hepatitis, and cirrhosis. It then discusses prior research applying classification algorithms like Naive Bayes, decision trees, and neural networks to liver disease prediction. The current study uses an Indian liver patient dataset from a UCI repository containing 583 records described by 7 attributes. It applies feature selection and classification algorithms like J48, SVM, MLP and Bayesian networks to build prediction models and compares their performance, finding J48 achieved the highest accuracy of 95.04% for predicting liver disease when used with feature selection.
This study compared measurements of muscle oxygen saturation (SmO2) from a novel wearable wireless device to a benchtop fiber-based system during exercise. 17 athletes performed an incremental cycling test while SmO2 was measured in the quadriceps muscle of both legs simultaneously using the two systems. The wearable device provided real-time estimates of lactate threshold power, which were evaluated against blood lactate measurements. The study found high correlations between SmO2 measurements from the two systems and that the wearable device estimated lactate threshold power within half an increment of the cycling test results, suggesting this novel device may provide a useful physiological indicator of exertion for athletes.
Raised Lipid Profile In Rheumatoid Arthritis- A Risk For CVDiosrjce
IOSR Journal of Biotechnology and Biochemistry (IOSR-JBB) covers studies of the chemical processes in living organisms, structure and function of cellular components such as proteins, carbohydrates, lipids, nucleic acids and other biomolecules, chemical properties of important biological molecules, like proteins, in particular the chemistry of enzyme-catalyzed reactions, genetic code (DNA, RNA), protein synthesis, cell membrane transport, and signal transduction. IOSR-JBB is privileged to focus on a wide range of biotechnology as well as high quality articles on genetic engineering, cell and tissue culture technologies, genetics, microbiology, molecular biology, biochemistry, embryology, cell biology, chemical engineering, bioprocess engineering, information technology, biorobotics.
This document summarizes new methods for analyzing the distribution of EQ-5D health state observations. The objectives are to describe the distribution of health profiles within EQ-5D data, demonstrate differences between the 3L and 5L versions, and compare new methods to existing ones like the Shannon Index. The results show that the 5L version has less clustered profiles than the 3L. Rare profiles are more important after medical interventions, shown by increased inequality in 3L data after surgery versus before. The new measures can help report and understand the distribution of EQ-5D profile data.
This document provides a summary of key findings from a confidential lifestyle and salary survey of American Association of Equine Practitioners (AAEP) members. The average salary among all survey respondents was $111,340. Salaries increased substantially with age and years of experience, with those over 60 earning over $155,000 on average. Recent graduates earned significantly less, with a average of $40,540 for those who graduated in 2006. The document compares salaries by various demographic and employment factors.
Estimating the Survival Function of HIV AIDS Patients using Weibull Modelijtsrd
This work provides information on the survival times of a cohort of infected individuals. The mean survival time was obtained as 22.579 months from the resultant estimate of the shape parameter =1.156 and scale parameter =0.0256 from Weibull 7 simulation of n = 500. Confidence intervals were also obtained for the two parameters at = 0.05 and it was found that the estimates are highly reliable. R. A. Adeleke | O. D. Ogunwale "Estimating the Survival Function of HIV/AIDS Patients using Weibull Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30636.pdf Paper Url :https://www.ijtsrd.com/mathemetics/statistics/30636/estimating-the-survival-function-of-hivaids-patients-using-weibull-model/r-a-adeleke
A great deal is happening in lupus-related research. This presentation will update participants on recent research developments and their impact on those affected by lupus. Dr. Petri will provide an overview of current lupus research and the prospects for the future of lupus treatments. Learn how to better manage your lupus and make knowledgeable decisions regarding your treatment plan.
IRJET-Performance Analysis of Liver Disease Prediction using Machine Learning...IRJET Journal
This document discusses using machine learning algorithms to predict liver disease based on patient dataset features. It first describes the liver and common liver disorders like fatty liver, hepatitis, and cirrhosis. It then discusses prior research applying classification algorithms like Naive Bayes, decision trees, and neural networks to liver disease prediction. The current study uses an Indian liver patient dataset from a UCI repository containing 583 records described by 7 attributes. It applies feature selection and classification algorithms like J48, SVM, MLP and Bayesian networks to build prediction models and compares their performance, finding J48 achieved the highest accuracy of 95.04% for predicting liver disease when used with feature selection.
This study compared measurements of muscle oxygen saturation (SmO2) from a novel wearable wireless device to a benchtop fiber-based system during exercise. 17 athletes performed an incremental cycling test while SmO2 was measured in the quadriceps muscle of both legs simultaneously using the two systems. The wearable device provided real-time estimates of lactate threshold power, which were evaluated against blood lactate measurements. The study found high correlations between SmO2 measurements from the two systems and that the wearable device estimated lactate threshold power within half an increment of the cycling test results, suggesting this novel device may provide a useful physiological indicator of exertion for athletes.
Raised Lipid Profile In Rheumatoid Arthritis- A Risk For CVDiosrjce
IOSR Journal of Biotechnology and Biochemistry (IOSR-JBB) covers studies of the chemical processes in living organisms, structure and function of cellular components such as proteins, carbohydrates, lipids, nucleic acids and other biomolecules, chemical properties of important biological molecules, like proteins, in particular the chemistry of enzyme-catalyzed reactions, genetic code (DNA, RNA), protein synthesis, cell membrane transport, and signal transduction. IOSR-JBB is privileged to focus on a wide range of biotechnology as well as high quality articles on genetic engineering, cell and tissue culture technologies, genetics, microbiology, molecular biology, biochemistry, embryology, cell biology, chemical engineering, bioprocess engineering, information technology, biorobotics.
This document summarizes new methods for analyzing the distribution of EQ-5D health state observations. The objectives are to describe the distribution of health profiles within EQ-5D data, demonstrate differences between the 3L and 5L versions, and compare new methods to existing ones like the Shannon Index. The results show that the 5L version has less clustered profiles than the 3L. Rare profiles are more important after medical interventions, shown by increased inequality in 3L data after surgery versus before. The new measures can help report and understand the distribution of EQ-5D profile data.
This document provides a summary of key findings from a confidential lifestyle and salary survey of American Association of Equine Practitioners (AAEP) members. The average salary among all survey respondents was $111,340. Salaries increased substantially with age and years of experience, with those over 60 earning over $155,000 on average. Recent graduates earned significantly less, with a average of $40,540 for those who graduated in 2006. The document compares salaries by various demographic and employment factors.
Estimating the Survival Function of HIV AIDS Patients using Weibull Modelijtsrd
This work provides information on the survival times of a cohort of infected individuals. The mean survival time was obtained as 22.579 months from the resultant estimate of the shape parameter =1.156 and scale parameter =0.0256 from Weibull 7 simulation of n = 500. Confidence intervals were also obtained for the two parameters at = 0.05 and it was found that the estimates are highly reliable. R. A. Adeleke | O. D. Ogunwale "Estimating the Survival Function of HIV/AIDS Patients using Weibull Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30636.pdf Paper Url :https://www.ijtsrd.com/mathemetics/statistics/30636/estimating-the-survival-function-of-hivaids-patients-using-weibull-model/r-a-adeleke
A great deal is happening in lupus-related research. This presentation will update participants on recent research developments and their impact on those affected by lupus. Dr. Petri will provide an overview of current lupus research and the prospects for the future of lupus treatments. Learn how to better manage your lupus and make knowledgeable decisions regarding your treatment plan.
IRJET- Comparison of Techniques for Diabetes Detection in Females using Machi...IRJET Journal
This document discusses and compares different machine learning techniques for detecting diabetes in females using various factors. It analyzes logistic regression and decision tree algorithms on a dataset containing factors like pregnancies, glucose levels, blood pressure, skin thickness, BMI, age and outcomes. Logistic regression is used to predict the binary outcome of having diabetes. Decision trees divide the problem into smaller subsets and use entropy and information gain to predict the most distinguishing parameters. The techniques are compared to prior studies on diabetes detection using machine learning.
PREVENTION OF HEART PROBLEM USING ARTIFICIAL INTELLIGENCEijaia
This document discusses building a machine learning model to predict the probability of patients experiencing heart problems based on their medical data. It analyzes data from 1000 patients across India on risk factors like family history, smoking, hypertension, cholesterol levels, blood sugar, obesity, lifestyle, previous bypass surgery, and iron levels. The model aims to help doctors make treatment decisions and minimize false negatives, where the model predicts no problem when one exists. It finds certain risk factors like family history, age over 50, and being male are correlated with higher heart problem rates. The model will be trained on this data to predict new patients' heart problem probability.
Screening of Promising Lead Molecules against Two Drug Targets in Ebola Virus...iosrjce
Ebola virus belongs to Filoviridae family. Recently, Ebola outbreaks have appeared drastically in
West Africa. The 2014 Ebola epidemic was lethal which was found to be affecting multiple countries in West
Africa. Till date there is no reported host for Ebola infection but it is most likely spread through bats. Still,
mankind is struggling to combat this pandemic infection. There are no reported drug targets in Ebola virus.
Therefore there are no reported inhibitors for the same. In this present work, two drug targets have been
proposed based on its essentiality to Ebola infection and non-homology to human proteome. Further, docking
and ADMET analysis have been done to report promising lead molecules for these two drug targets.
The Relationship between the usage of Drugs and Sport Performanceijtsrd
The main challenges for sports psychologist and coaches in this century would be producing strong performance among the nondrug addict athletes. Relatively widespread use of such drugs as namely anabolic steroids to enhance performance dates back at least to the Olympic of the 1960's, although broad public awareness of such drugs use seems relatively recent. The easy availability of drugs, through illegal, contributes to the rise of drug addictions. This research explores some rationales of regulating drug use by athletes in order to determine the level of sport performance. The sample consisted of 97 athletes, drawn from athletes who competed in sports between Universities. Drug Usage Questionnaire and Sport Performance were used to collect the data. The correlation coefficient of 0.712 was noted respectively between the usage of drugs and sports performance. Even though the usage of drugs benefits the sports performance, but it is considered as cheating and unfair, harm, perversion of sports, unnaturalness and dehumanization. The government, sport psychology, counselors, coaches and sports bodies should play an important role to face the challenges to overcomes the evil, drug enhances to protect athletes' health and to achieve fair level playing field. Vincent Parnabas | Julinamary Parnabas | Antoinette Mary Parnabas "The Relationship between the usage of Drugs and Sport Performance" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26405.pdfPaper URL: https://www.ijtsrd.com/other-scientific-research-area/other/26405/the-relationship-between-the-usage-of-drugs-and-sport-performance/vincent-parnabas
The learning speed of the feed forward neural
network takes a lot of time to be trained which is a major
drawback in their applications since the past decades. The
key reasons behind may be due to the slow gradient-based
learning algorithms which are extensively used to train the
neural networks or due to the parameters in the networks
which are tuned iteratively using some learning algorithms.
Thus, in order to eradicate the above pitfalls, a new learning
algorithm was proposed known as Extreme Learning Machines
(ELM). This algorithm tries to compute Hidden-layer-output
matrix that is made of randomly assigned input layer and
hidden layer weights and randomly assigned biases. Unlike the
other feedforward networks, ELM has the access of the whole
training dataset before going into the computation part. Here,
we have devised a new two-layer-feedforward network (TFFN)
for ELM in a new manner with randomly assigning the weights
and biases in both the hidden layers, which then calculates the
output-hidden layer weights using the Moore-Penrose generalized
inverse. TFFN doesn’t restricts the algorithm to fix the number
of hidden neurons that the algorithm should have. Rather it
searches the space which gives an optimized result in the neurons
combination in both the hidden layers. This algorithm provides a
good generalization capability than the parent Extreme Learning
Machines at an extremely fast learning speed. Here, we have
experimented the algorithm on various types of datasets and
various popular algorithm to find the performances and report
a comparison.
This document describes a study that used business analytics software and statistical analysis to establish new reference intervals for 12 common metabolic analytes using a large dataset of patient results from the laboratory. Over 500 patient results were used for each analyte to calculate the central 95th percentile, far exceeding the recommended minimum of 120 samples. The established reference intervals were compared to package insert and current ranges. The new methodology provided robust reference intervals truly representative of the laboratory's patient population in an efficient manner without relying on traditional, more limited approaches or IT resources.
system dynamics simulation model for cardiovascular heart disease riskIJAEMSJORNAL
Detecting diseases at early stage can help to overcome and treat them accurately. Identifying the appropriate treatment depends on the method that is used in diagnosing the diseases. The incidence of cardiovascular heart disease (CVD) has been increasing steadily and so too its associated mortality. System Dynamics is appropriate methodology for Modelling and Simulation. The Expert knowledge about risk factors for CVD was elicited through interview and literature search. Two CVD risk factors Smoking and Alcohol Intake were analyzed by the proposed decision support system developed with System Dynamics Simulation software (iThink V9.0.2 ),used for the design, implementation and evaluation of the system. The proposed framework would be particularly useful for researchers in the field but also for medical practitioners and developers of medical decision support systems.
This document summarizes a teleconference discussing the cost-effectiveness of various cardiovascular disease therapies. It provides cost-effectiveness ratios for therapies such as statins, clopidogrel, and eplerenone. It also discusses the high costs of post-MI heart failure and the benefits and cost-effectiveness of eplerenone in reducing mortality and hospitalization in MI patients with left ventricular dysfunction.
This paper presents a novel non-invasive method for glucose monitoring using impedance spectroscopy. The experimental setup involves measuring the impedance of Ringer's solution and human blood using a four-electrode system at varying glucose concentrations and frequencies. Results show voltage peaks occurring within a consistent frequency band for both Ringer's solution and human subjects, and that voltage increases with higher glucose levels. This establishes a relationship between blood glucose concentration and impedance. Further refinement is needed to account for other factors influencing impedance readings and improve accuracy for a full non-invasive glucose monitoring device.
Multi Institutional Cohort to Facilitate Cardiovascular Disease Biomarker Val...HMO Research Network
This pilot study aims to incorporate emerging biomarkers into cardiovascular disease risk prediction models using existing biobank samples. The study retrospectively analyzed over 18,000 patients from two healthcare systems, identifying over 800 cases of acute myocardial infarction. Risk factors like age, sex, smoking history, blood pressure, diabetes and lipids were significant predictors of AMI. A simulated biomarker improved the predictive performance of risk models, correctly reclassifying hundreds of cases. An initial screen of genetic variants identified APOE and 9p21 as potentially useful biomarkers after validation in larger samples.
Presentation to CCG - Capita Health Freakononics v3Mike Thorogood
This document discusses using econometric modeling and statistical analysis to understand factors that influence weight gain and loss. It presents an initial model that links weight to food consumption. The model is then developed to also account for exercise and different activities. The document outlines testing the model by examining the overall fit and significance of individual variables. It also discusses checking for issues like collinearity between variables and establishing causality. Further tests are described to identify patterns in the residuals and improve model specification. Applications of similar modeling for targeted health interventions and estimating cost savings are briefly mentioned.
Estimation of bitlength of transformed quantized residueIAEME Publication
This document proposes a method to estimate the bitlength of transformed and quantized residue coefficients and syntax elements for mode decision in H.264 baseline encoding. It aims to reduce the computational complexity of calculating rate-distortion cost (RD-Cost) by estimating bitlengths without fully encoding bitstreams. The key aspects are:
1) It classifies residue coefficients and estimates bitlength for coefficient types like Luma_4x4, Luma DC, Luma AC, Chroma DC, and Chroma AC based on coding tables and context like neighboring coefficients.
2) It estimates bitlengths for syntax elements like macroblock type, prediction modes, and motion vectors based on Exp-Golomb coding tables
Electromagnetic studies on nano sized magnesium ferriteIAEME Publication
The document summarizes research on the electromagnetic properties of nano-sized magnesium ferrite synthesized using microwave techniques. Key findings include:
1) Magnetic properties were measured using VSM which showed the material has a high coercivity of 785.12 Oe, classifying it as a hard magnetic material.
2) Dielectric measurements found the ac conductivity and dielectric constant decreased with increasing frequency. Both increased with temperature initially before decreasing.
3) The dielectric loss showed expected dispersion behavior, decreasing with frequency and generally increasing with temperature.
4) A high quality factor of 150 was obtained, higher than for bulk ferrites, indicating potential applications in microwave devices.
Study of model predictive control using ni lab viewIAEME Publication
This document discusses the implementation of model predictive control (MPC) using National Instruments LabVIEW software. It begins with introductions to MPC and LabVIEW. It then covers constructing state space and transfer function models in LabVIEW. Simulation results are presented for MPC applied to first order systems with and without time delay. MPC performance is compared to PID control, showing MPC can handle constraints and optimize process operation while PID cannot. The document concludes MPC simulation using LabVIEW is successful and simulation results are useful for control system design.
This document discusses Kaizen, a philosophy of continuous improvement, and its implementation in an Indian petrochemical plant. [1] It provides background on total quality management (TQM) and defines Kaizen as continuous, gradual improvements involving everyone. [2] The principles of Kaizen emphasize that employees are a company's most important asset and that success comes from consistent, incremental changes rather than occasional radical changes. [3] Kaizen aims to improve all aspects of operations through activities like quality circles, process management, and eliminating waste.
This document summarizes a study that analyzed Indian scientific literature in veterinary sciences from 1999-2011. Some key findings include:
- A total of 5,468 publications were analyzed, with the majority (99.09%) being journal articles.
- Research output grew steadily from 1999-2008 but declined in 2009-2010.
- The most common authorship patterns were papers with 3 or 4 authors, indicating collaborative work is prevalent.
- The author with the most publications was Kumar, A. from Punjab Agricultural University with 94 papers.
Instruction level parallelism using ppm branch predictionIAEME Publication
This document summarizes an approach to instruction level parallelism using prediction by partial matching (PPM) branch prediction. It proposes a hybrid PPM-based branch predictor that uses both local and global branch histories. The two predictors are combined using a neural network. Key aspects of the implementation include:
1. Using local and global history PPM predictors and combining their predictions with a neural network.
2. Enhancements to the basic PPM approach like program counter tagging, efficient history encoding using run-length encoding, tracking pattern bias, and dynamic pattern length selection.
3. Details of the global history PPM predictor including the use of tables and linked lists to store patterns of different lengths and handle collisions
Numerical computation of eigenenergy and transmission coefficient of symmetri...IAEME Publication
This document summarizes a study on numerically computing the eigenenergy and transmission coefficient of a symmetric quantum double barrier structure with variable effective mass under an applied electric field. The study uses the transfer matrix method to solve Schrodinger's equation for a GaAs/AlxGa1-xAs material system. It finds that eigenenergy decreases nonlinearly with increasing electric field. Transmission coefficient decreases with increasing barrier thickness or height but can occur at lower energies with increasing well thickness. The existence of higher quasi-bound states is also observed.
Spatial and temporal study of a mechanical and harmonic vibration by high spe...IAEME Publication
The document summarizes a study that uses high-speed optical interferometry to analyze the spatial and temporal evolution of vibrations in a mechanically excited rectangular metal plate. A high-speed CMOS camera captures 4000 frames per second of the plate's free vibration. An algorithm is used to extract phase maps from the interferograms, showing the plate's deformation over time and allowing reconstruction of the vibration cycle. Simulated results demonstrate the technique's ability to measure an unknown vibration using 12 sample interferograms without synchronization requirements.
A new approach for design of cmos based cascode current mirror for asp applic...IAEME Publication
This document discusses a new approach for designing a CMOS-based cascode current mirror circuit for analog signal processing applications. It begins by introducing current mirrors and their importance as core structures in analog, digital, and mixed-signal circuits. It then reviews different configurations of basic current mirror circuits and discusses how cascode configurations can improve performance by maintaining constant voltages. The document proposes an innovative cascode current mirror circuit and evaluates its performance through simulation using a 0.13 micron CMOS technology.
Comparison and analysis of combining techniques for spatial multiplexing spac...IAEME Publication
This document compares different combining techniques for space-time block coded systems in Rayleigh fading channels. It finds that maximum ratio combining outperforms other techniques like equal gain combining and selection combining for any space-time block code configuration, providing the best bit error rate. The document provides background on space-time block codes, describes the Alamouti space-time code, and discusses various receive diversity combining techniques.
Survey on data mining techniques in heart disease predictionSivagowry Shathesh
This document describes a study on applying data mining techniques to analyze and predict heart disease. It discusses how data mining can extract valuable knowledge from healthcare data. The study uses several data mining techniques like decision trees, naive Bayes classification, clustering, and association rule mining on heart disease datasets from UC Irvine to predict heart disease. Experimental results show that multilayer neural networks and classification techniques like naive Bayes had higher prediction accuracy compared to other methods.
IRJET- Comparison of Techniques for Diabetes Detection in Females using Machi...IRJET Journal
This document discusses and compares different machine learning techniques for detecting diabetes in females using various factors. It analyzes logistic regression and decision tree algorithms on a dataset containing factors like pregnancies, glucose levels, blood pressure, skin thickness, BMI, age and outcomes. Logistic regression is used to predict the binary outcome of having diabetes. Decision trees divide the problem into smaller subsets and use entropy and information gain to predict the most distinguishing parameters. The techniques are compared to prior studies on diabetes detection using machine learning.
PREVENTION OF HEART PROBLEM USING ARTIFICIAL INTELLIGENCEijaia
This document discusses building a machine learning model to predict the probability of patients experiencing heart problems based on their medical data. It analyzes data from 1000 patients across India on risk factors like family history, smoking, hypertension, cholesterol levels, blood sugar, obesity, lifestyle, previous bypass surgery, and iron levels. The model aims to help doctors make treatment decisions and minimize false negatives, where the model predicts no problem when one exists. It finds certain risk factors like family history, age over 50, and being male are correlated with higher heart problem rates. The model will be trained on this data to predict new patients' heart problem probability.
Screening of Promising Lead Molecules against Two Drug Targets in Ebola Virus...iosrjce
Ebola virus belongs to Filoviridae family. Recently, Ebola outbreaks have appeared drastically in
West Africa. The 2014 Ebola epidemic was lethal which was found to be affecting multiple countries in West
Africa. Till date there is no reported host for Ebola infection but it is most likely spread through bats. Still,
mankind is struggling to combat this pandemic infection. There are no reported drug targets in Ebola virus.
Therefore there are no reported inhibitors for the same. In this present work, two drug targets have been
proposed based on its essentiality to Ebola infection and non-homology to human proteome. Further, docking
and ADMET analysis have been done to report promising lead molecules for these two drug targets.
The Relationship between the usage of Drugs and Sport Performanceijtsrd
The main challenges for sports psychologist and coaches in this century would be producing strong performance among the nondrug addict athletes. Relatively widespread use of such drugs as namely anabolic steroids to enhance performance dates back at least to the Olympic of the 1960's, although broad public awareness of such drugs use seems relatively recent. The easy availability of drugs, through illegal, contributes to the rise of drug addictions. This research explores some rationales of regulating drug use by athletes in order to determine the level of sport performance. The sample consisted of 97 athletes, drawn from athletes who competed in sports between Universities. Drug Usage Questionnaire and Sport Performance were used to collect the data. The correlation coefficient of 0.712 was noted respectively between the usage of drugs and sports performance. Even though the usage of drugs benefits the sports performance, but it is considered as cheating and unfair, harm, perversion of sports, unnaturalness and dehumanization. The government, sport psychology, counselors, coaches and sports bodies should play an important role to face the challenges to overcomes the evil, drug enhances to protect athletes' health and to achieve fair level playing field. Vincent Parnabas | Julinamary Parnabas | Antoinette Mary Parnabas "The Relationship between the usage of Drugs and Sport Performance" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26405.pdfPaper URL: https://www.ijtsrd.com/other-scientific-research-area/other/26405/the-relationship-between-the-usage-of-drugs-and-sport-performance/vincent-parnabas
The learning speed of the feed forward neural
network takes a lot of time to be trained which is a major
drawback in their applications since the past decades. The
key reasons behind may be due to the slow gradient-based
learning algorithms which are extensively used to train the
neural networks or due to the parameters in the networks
which are tuned iteratively using some learning algorithms.
Thus, in order to eradicate the above pitfalls, a new learning
algorithm was proposed known as Extreme Learning Machines
(ELM). This algorithm tries to compute Hidden-layer-output
matrix that is made of randomly assigned input layer and
hidden layer weights and randomly assigned biases. Unlike the
other feedforward networks, ELM has the access of the whole
training dataset before going into the computation part. Here,
we have devised a new two-layer-feedforward network (TFFN)
for ELM in a new manner with randomly assigning the weights
and biases in both the hidden layers, which then calculates the
output-hidden layer weights using the Moore-Penrose generalized
inverse. TFFN doesn’t restricts the algorithm to fix the number
of hidden neurons that the algorithm should have. Rather it
searches the space which gives an optimized result in the neurons
combination in both the hidden layers. This algorithm provides a
good generalization capability than the parent Extreme Learning
Machines at an extremely fast learning speed. Here, we have
experimented the algorithm on various types of datasets and
various popular algorithm to find the performances and report
a comparison.
This document describes a study that used business analytics software and statistical analysis to establish new reference intervals for 12 common metabolic analytes using a large dataset of patient results from the laboratory. Over 500 patient results were used for each analyte to calculate the central 95th percentile, far exceeding the recommended minimum of 120 samples. The established reference intervals were compared to package insert and current ranges. The new methodology provided robust reference intervals truly representative of the laboratory's patient population in an efficient manner without relying on traditional, more limited approaches or IT resources.
system dynamics simulation model for cardiovascular heart disease riskIJAEMSJORNAL
Detecting diseases at early stage can help to overcome and treat them accurately. Identifying the appropriate treatment depends on the method that is used in diagnosing the diseases. The incidence of cardiovascular heart disease (CVD) has been increasing steadily and so too its associated mortality. System Dynamics is appropriate methodology for Modelling and Simulation. The Expert knowledge about risk factors for CVD was elicited through interview and literature search. Two CVD risk factors Smoking and Alcohol Intake were analyzed by the proposed decision support system developed with System Dynamics Simulation software (iThink V9.0.2 ),used for the design, implementation and evaluation of the system. The proposed framework would be particularly useful for researchers in the field but also for medical practitioners and developers of medical decision support systems.
This document summarizes a teleconference discussing the cost-effectiveness of various cardiovascular disease therapies. It provides cost-effectiveness ratios for therapies such as statins, clopidogrel, and eplerenone. It also discusses the high costs of post-MI heart failure and the benefits and cost-effectiveness of eplerenone in reducing mortality and hospitalization in MI patients with left ventricular dysfunction.
This paper presents a novel non-invasive method for glucose monitoring using impedance spectroscopy. The experimental setup involves measuring the impedance of Ringer's solution and human blood using a four-electrode system at varying glucose concentrations and frequencies. Results show voltage peaks occurring within a consistent frequency band for both Ringer's solution and human subjects, and that voltage increases with higher glucose levels. This establishes a relationship between blood glucose concentration and impedance. Further refinement is needed to account for other factors influencing impedance readings and improve accuracy for a full non-invasive glucose monitoring device.
Multi Institutional Cohort to Facilitate Cardiovascular Disease Biomarker Val...HMO Research Network
This pilot study aims to incorporate emerging biomarkers into cardiovascular disease risk prediction models using existing biobank samples. The study retrospectively analyzed over 18,000 patients from two healthcare systems, identifying over 800 cases of acute myocardial infarction. Risk factors like age, sex, smoking history, blood pressure, diabetes and lipids were significant predictors of AMI. A simulated biomarker improved the predictive performance of risk models, correctly reclassifying hundreds of cases. An initial screen of genetic variants identified APOE and 9p21 as potentially useful biomarkers after validation in larger samples.
Presentation to CCG - Capita Health Freakononics v3Mike Thorogood
This document discusses using econometric modeling and statistical analysis to understand factors that influence weight gain and loss. It presents an initial model that links weight to food consumption. The model is then developed to also account for exercise and different activities. The document outlines testing the model by examining the overall fit and significance of individual variables. It also discusses checking for issues like collinearity between variables and establishing causality. Further tests are described to identify patterns in the residuals and improve model specification. Applications of similar modeling for targeted health interventions and estimating cost savings are briefly mentioned.
Estimation of bitlength of transformed quantized residueIAEME Publication
This document proposes a method to estimate the bitlength of transformed and quantized residue coefficients and syntax elements for mode decision in H.264 baseline encoding. It aims to reduce the computational complexity of calculating rate-distortion cost (RD-Cost) by estimating bitlengths without fully encoding bitstreams. The key aspects are:
1) It classifies residue coefficients and estimates bitlength for coefficient types like Luma_4x4, Luma DC, Luma AC, Chroma DC, and Chroma AC based on coding tables and context like neighboring coefficients.
2) It estimates bitlengths for syntax elements like macroblock type, prediction modes, and motion vectors based on Exp-Golomb coding tables
Electromagnetic studies on nano sized magnesium ferriteIAEME Publication
The document summarizes research on the electromagnetic properties of nano-sized magnesium ferrite synthesized using microwave techniques. Key findings include:
1) Magnetic properties were measured using VSM which showed the material has a high coercivity of 785.12 Oe, classifying it as a hard magnetic material.
2) Dielectric measurements found the ac conductivity and dielectric constant decreased with increasing frequency. Both increased with temperature initially before decreasing.
3) The dielectric loss showed expected dispersion behavior, decreasing with frequency and generally increasing with temperature.
4) A high quality factor of 150 was obtained, higher than for bulk ferrites, indicating potential applications in microwave devices.
Study of model predictive control using ni lab viewIAEME Publication
This document discusses the implementation of model predictive control (MPC) using National Instruments LabVIEW software. It begins with introductions to MPC and LabVIEW. It then covers constructing state space and transfer function models in LabVIEW. Simulation results are presented for MPC applied to first order systems with and without time delay. MPC performance is compared to PID control, showing MPC can handle constraints and optimize process operation while PID cannot. The document concludes MPC simulation using LabVIEW is successful and simulation results are useful for control system design.
This document discusses Kaizen, a philosophy of continuous improvement, and its implementation in an Indian petrochemical plant. [1] It provides background on total quality management (TQM) and defines Kaizen as continuous, gradual improvements involving everyone. [2] The principles of Kaizen emphasize that employees are a company's most important asset and that success comes from consistent, incremental changes rather than occasional radical changes. [3] Kaizen aims to improve all aspects of operations through activities like quality circles, process management, and eliminating waste.
This document summarizes a study that analyzed Indian scientific literature in veterinary sciences from 1999-2011. Some key findings include:
- A total of 5,468 publications were analyzed, with the majority (99.09%) being journal articles.
- Research output grew steadily from 1999-2008 but declined in 2009-2010.
- The most common authorship patterns were papers with 3 or 4 authors, indicating collaborative work is prevalent.
- The author with the most publications was Kumar, A. from Punjab Agricultural University with 94 papers.
Instruction level parallelism using ppm branch predictionIAEME Publication
This document summarizes an approach to instruction level parallelism using prediction by partial matching (PPM) branch prediction. It proposes a hybrid PPM-based branch predictor that uses both local and global branch histories. The two predictors are combined using a neural network. Key aspects of the implementation include:
1. Using local and global history PPM predictors and combining their predictions with a neural network.
2. Enhancements to the basic PPM approach like program counter tagging, efficient history encoding using run-length encoding, tracking pattern bias, and dynamic pattern length selection.
3. Details of the global history PPM predictor including the use of tables and linked lists to store patterns of different lengths and handle collisions
Numerical computation of eigenenergy and transmission coefficient of symmetri...IAEME Publication
This document summarizes a study on numerically computing the eigenenergy and transmission coefficient of a symmetric quantum double barrier structure with variable effective mass under an applied electric field. The study uses the transfer matrix method to solve Schrodinger's equation for a GaAs/AlxGa1-xAs material system. It finds that eigenenergy decreases nonlinearly with increasing electric field. Transmission coefficient decreases with increasing barrier thickness or height but can occur at lower energies with increasing well thickness. The existence of higher quasi-bound states is also observed.
Spatial and temporal study of a mechanical and harmonic vibration by high spe...IAEME Publication
The document summarizes a study that uses high-speed optical interferometry to analyze the spatial and temporal evolution of vibrations in a mechanically excited rectangular metal plate. A high-speed CMOS camera captures 4000 frames per second of the plate's free vibration. An algorithm is used to extract phase maps from the interferograms, showing the plate's deformation over time and allowing reconstruction of the vibration cycle. Simulated results demonstrate the technique's ability to measure an unknown vibration using 12 sample interferograms without synchronization requirements.
A new approach for design of cmos based cascode current mirror for asp applic...IAEME Publication
This document discusses a new approach for designing a CMOS-based cascode current mirror circuit for analog signal processing applications. It begins by introducing current mirrors and their importance as core structures in analog, digital, and mixed-signal circuits. It then reviews different configurations of basic current mirror circuits and discusses how cascode configurations can improve performance by maintaining constant voltages. The document proposes an innovative cascode current mirror circuit and evaluates its performance through simulation using a 0.13 micron CMOS technology.
Comparison and analysis of combining techniques for spatial multiplexing spac...IAEME Publication
This document compares different combining techniques for space-time block coded systems in Rayleigh fading channels. It finds that maximum ratio combining outperforms other techniques like equal gain combining and selection combining for any space-time block code configuration, providing the best bit error rate. The document provides background on space-time block codes, describes the Alamouti space-time code, and discusses various receive diversity combining techniques.
Survey on data mining techniques in heart disease predictionSivagowry Shathesh
This document describes a study on applying data mining techniques to analyze and predict heart disease. It discusses how data mining can extract valuable knowledge from healthcare data. The study uses several data mining techniques like decision trees, naive Bayes classification, clustering, and association rule mining on heart disease datasets from UC Irvine to predict heart disease. Experimental results show that multilayer neural networks and classification techniques like naive Bayes had higher prediction accuracy compared to other methods.
LIVER DISEASE PREDICTION BY USING DIFFERENT DECISION TREE TECHNIQUESIJDKP
Early prediction of liver disease is very important to save human life and take proper steps to control the
disease. Decision Tree algorithms have been successfully applied in various fields especially in medical
science. This research work explores the early prediction of liver disease using various decision tree
techniques. The liver disease dataset which is select for this study is consisting of attributes like total
bilirubin, direct bilirubin, age, gender, total proteins, albumin and globulin ratio. The main purpose of this
work is to calculate the performance of various decision tree techniques and compare their performance.
The decision tree techniques used in this study are J48, LMT, Random Forest, Random tree, REPTree,
Decision Stump, and Hoeffding Tree. The analysis proves that Decision Stump provides the highest
accuracy than other techniques
A Tentative analysis of Liver Disorder using Data Mining Algorithms J48, Deci...MangaiK4
Abstract — Nowadays healthcare field has additional data mining process became a crucial role to use for disease prediction. Data mining is that the process of investigate up info from the huge information sets. The medical information is extremely voluminous. Therefore the investigator is extremely difficult to predict the disease is challenging. To overcome this issue the researchers use data mining processing technique like classification, clustering, association rules so on. The most objective of this analysis work is to predict disease supported common attributes intake of alcohol, smoking, obesity, diabetes, consumption of contaminated food, case history of liver disease using classification algorithm. The algorithms employed in this analysis work are J48, Naive Bayes. These classification algorithms are compared base on the performance factors accuracy and execution time. The investigational results could be a improved classifier for predict the liver disease.
IRJET - Deep Multiple Instance Learning for Automatic Detection of Diabetic R...IRJET Journal
This document describes a proposed method for using deep multiple instance learning to automatically detect diabetic retinopathy in retinal images. Diabetic retinopathy is a complication of diabetes that can cause vision loss or blindness. The proposed method treats retinal images as "bags" containing "instances" of image patches. A deep learning model is trained using only image-level labels to both detect diabetic retinopathy images and identify lesions within images. The model first preprocesses images to normalize factors like scale and illumination. It then segments lesions and extracts features before classifying images using convolutional neural networks. The goal is to provide explicit locations of lesions to aid clinicians while leveraging large datasets typically required for deep learning.
This document discusses using machine learning techniques to predict diabetes. Specifically:
- The authors build several prediction models using machine learning algorithms like logistic regression, KNN, decision trees on a diabetes dataset to classify patients as having diabetes or not.
- They evaluate the performance of the different models using metrics like accuracy, and find that KNN achieved the highest accuracy of 78% on the test data.
- The document also reviews several other studies applying techniques like random forests, support vector machines, convolutional neural networks to the same diabetes prediction task and Pima Indian diabetes dataset.
- The authors conduct their own experiments applying algorithms like logistic regression, KNN, decision trees, random forest, XGBoost to the
1 springer format chronic changed edit iqbal qcIAESIJEECS
In the present generation, majority of the people are highly affected by kidney diseases. Among them, chronic kidney is the most common life threatening disease which can be prevented by early detection.Histological grade in chronic kidney disease provides clinically important prognostic information. Therefore, machine learning techniques are applied on the information collected from previously diagnosed patients in order to discover the knowledge and patterns for making precise predictions.A large number of features exist in the raw data in which some may cause low information and error; hence feature selection techniques can be used to retrieve useful subset of features and to improve the computation performance. In this manuscript we use a set of Filter, Wrapper methods followed by Bagging and Boosting models with parameter tuning technique to classify chronic kidney disease.Capability of Bagging and Boosting classifiers are compared and the best ensemble classifier which attains high stability with better promising results is identified.
Genetically Optimized Neural Network for Heart Disease ClassificationIRJET Journal
This document describes a study that uses a genetically optimized neural network to classify heart disease based on patient risk factors. The study collects data on 12 risk factors from 50 patients and encodes the values for use as input to a neural network. The neural network is initially trained using backpropagation, then genetic algorithms are used to optimize the network weights and biases to improve accuracy. Confusion matrices are plotted to evaluate the accuracy of the optimized neural network at classifying patients as having heart disease or not. The approach achieves a classification accuracy of 90% on the test data.
Diabetes Prediction by Supervised and Unsupervised Approaches with Feature Se...IJARIIT
Two approaches to building models for prediction of the onset of Type diabetes mellitus in juvenile subjects were examined. A set of tests performed immediately before diagnosis was used to build classifiers to predict whether the subject would be diagnosed with juvenile diabetes. A modified training set consisting of differences between test results taken at different times was also used to build classifiers to predict whether a subject would be diagnosed with juvenile diabetes. Supervised were compared with decision trees and unsupervised of both types of classifiers. In this study, the system and the test most likely to confirm a diagnosis based on the pre-test probability computed from the patient's information including symptoms and the results of previous tests. If the patient's disease post-test probability is higher than the treatment threshold, a diagnostic decision will be made, and vice versa. Otherwise, the patient needs more tests to help make a decision. The system will then recommend the next optimal test and repeat the same process. In this thesis find out which approach is better on diabetes dataset in weka framework. Also use feature selection techniques which reduce the features and complexities of process
Improving the performance of k nearest neighbor algorithm for the classificat...IAEME Publication
The document discusses improving the performance of the k-nearest neighbor (kNN) algorithm for classifying diabetes datasets with missing values. It first provides background on diabetes and challenges with missing data. It then describes various data preprocessing techniques used to handle missing values, including mean imputation. The document outlines the kNN classification algorithm and metrics like accuracy and error rate to evaluate performance. It applies these techniques to the Pima Indian diabetes dataset and finds that imputing missing values along with suitable preprocessing like normalization increases classification accuracy compared to ignoring missing values or imputation alone.
A CONCEPTUAL APPROACH TO ENHANCE PREDICTION OF DIABETES USING ALTERNATE FEATU...IAEMEPublication
Machine learning algorithms play a vital role in prediction of many diseases such as heart disease, diabetes, cancer, lung disease etc. The applicability of machine learning algorithms to healthcare domain relieves the burden of physicians as it is impractical to scan manually all the data collected over a period of time in order to arrive at some valuable information. Machine learning algorithms learn from the training dataset and they become capable of thinking like a human. Once the algorithm completes it learning with training dataset, it can automatically predict the target output label of any unseen data. In this work, predicting diabetes using machine learning algorithms has been taken up. A conceptual architecture has been proposed based on big data architecture.
A CONCEPTUAL APPROACH TO ENHANCE PREDICTION OF DIABETES USING ALTERNATE FEATU...IAEME Publication
Machine learning algorithms play a vital role in prediction of many diseases such as heart disease, diabetes, cancer, lung disease etc. The applicability of machine learning algorithms to healthcare domain relieves the burden of physicians as it is impractical to scan manually all the data collected over a period of time in order to arrive at some valuable information. Machine learning algorithms learn from the training dataset and they become capable of thinking like a human. Once the algorithm completes it learning with training dataset, it can automatically predict the target output label of any unseen data. In this work, predicting diabetes using machine learning algorithms has been taken up. A conceptual architecture has been proposed based on big data architecture.
IRJET- Disease Analysis and Giving Remedies through an Android ApplicationIRJET Journal
The document describes a proposed Android application that uses decision trees to analyze symptoms and predict diseases. User-reported symptoms would be input to predict the disease and provide herbal remedies. The proposed system aims to overcome limitations of prior work by covering more diseases and their home remedies without side effects. It was developed using Android Studio and stores data in Firebase. The system uses a decision tree algorithm to predict disease based on symptom probability and scans a database to match remedies.
Performance Evaluation of Data Mining Algorithm on Electronic Health Record o...BRNSSPublicationHubI
This document discusses the performance evaluation of various data mining algorithms on an electronic health record database of diabetic patients. It first provides background on data mining and its applications in healthcare, particularly for diabetes. It then describes the methodology used, which involved preprocessing the data and applying several classification algorithms (decision stump, J48, random forest, neural network, Zero R, One R) to predict diabetes status. The results of each algorithm are evaluated based on accuracy, precision, recall, and error rate. Overall, the document aims to compare the performance of these algorithms on an electronic health record database for diabetes prediction.
PREDICTION OF DIABETES MELLITUS USING MACHINE LEARNING TECHNIQUESIAEME Publication
Diabetes mellitus is a common disease caused by a set of metabolic ailments
where the sugar stages over drawn-out period is very high. It touches diverse organs
of the human body which therefore harm a huge number of the body's system, in
precise the blood strains and nerves. Early prediction in such disease can be exact
and save human life. To achieve the goal, this research work mainly discovers
numerous factors associated to this disease using machine learning techniques.
Machine learning methods provide effectual outcome to extract knowledge by building
predicting models from diagnostic medical datasets together from the diabetic
patients. Quarrying knowledge from such data can be valuable to predict diabetic
patients. In this research, six popular used machine learning techniques, namely
Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), C4.5 Decision
Tree (DT), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) are
compared in order to get outstanding machine learning techniques to forecast diabetic
mellitus. Our new outcome shows that Support Vector Machine (SVM) achieved
higher accuracy compared to other machine learning techniques.
Diagnosis Of Chronic Kidney Disease Using Machine LearningIRJET Journal
This document discusses a study that used machine learning techniques to diagnose chronic kidney disease (CKD). The study analyzed a dataset of 400 patients and 24 features related to CKD. Missing data was imputed using statistical methods. Recursive feature elimination was used to select important features. Four classification algorithms were tested - support vector machine, k-nearest neighbors, decision tree, and random forest. The random forest algorithm achieved the highest accuracy, precision and other performance measures at 100% for CKD diagnosis. The study aims to help doctors make early diagnoses of CKD to prevent kidney failure through the use of artificial intelligence techniques.
THE APPLICATION OF EXTENSIVE FEATURE EXTRACTION AS A COST STRATEGY IN CLINICA...IJDKP
The document describes a study that uses principal component analysis (PCA) for feature extraction to reduce the number of clinical markers needed for disease classification. PCA was performed on prostate cancer and diabetes datasets to extract the most relevant features. For prostate cancer, PCA extracted 3 features from 4 original markers, and for diabetes it extracted 4 features from 5 original markers. When the reduced feature sets were used in a neural network, it yielded classification accuracies of 80% for prostate cancer and 75% for diabetes. The feature extraction approach aims to lower the cost of clinical decision support systems by reducing the number of tests required while maintaining accuracy.
THE APPLICATION OF EXTENSIVE FEATURE EXTRACTION AS A COST STRATEGY IN CLINICA...IJDKP
Patients waste great deal of resources in the cause of identification of pathogens that caused their ailments; this calls for concern, hence the need to develop a veritable tool for minimizing the cost involved in classification of disease pathogens without compromising accuracy. In this paper, we developed a feature extraction model which reduces the clinical markers for prostate cancer and diabetes. The feature extraction, in the form of principal component analysis (PCA), was used to extract relevant components from prostate cancer and diabetes datasets.The simulation and experiment of the system were done with matlab.The system was able to extract 3 relevant features out of 4 prostate cancer clinical markers and 4 relevant features out of 5 diabetes clinical markers.The result showed that when trained in a multilayer neural network it yielded better classification accuracy with the extracted relevant features with 80% and 75% component analysis in prostate cancer and diabetes datasets respectively.
THE APPLICATION OF EXTENSIVE FEATURE EXTRACTION AS A COST STRATEGY IN CLINICA...IJDKP
Patients waste great deal of resources in the cause of identification of pathogens that caused their
ailments; this calls for concern, hence the need to develop a veritable tool for minimizing the cost involved
in classification of disease pathogens without compromising accuracy. In this paper, we developed a
feature extraction model which reduces the clinical markers for prostate cancer and diabetes. The feature
extraction, in the form of principal component analysis (PCA), was used to extract relevant components
from prostate cancer and diabetes datasets. The simulation and experiment of the system were done with
matlab. The system was able to extract 3 relevant features out of 4 prostate cancer clinical markers and 4
relevant features out of 5 diabetes clinical markers. The result showed that when trained in a multilayer
neural network it yielded better classification accuracy with the extracted relevant features with 80% and
75% component analysis in prostate cancer and diabetes datasets respective
IRJET- Chronic Kidney Disease Prediction based on Naive Bayes TechniqueIRJET Journal
This document discusses using a Naive Bayes technique to predict chronic kidney disease (CKD) based on patient data. It begins by introducing data mining and its applications in healthcare to extract useful information from large datasets. It then reviews literature on using classification algorithms like Naive Bayes for disease detection. Next, it describes the limitations of existing manual CKD prediction systems. The proposed system would automate CKD prediction using a Naive Bayes classifier to help doctors diagnose the disease which affects many worldwide. The methodology involves collecting clinical data, pre-processing it, then applying the Naive Bayes technique to extract patterns and predict CKD.
Standardization and wider use of Electronic Health records (EHR) creates opportunities for
better understanding patterns of illness and care within and across medical systems. In the healthcare
systems, hidden event signatures allow taking decision for patient’s diagnosis, prognosis, and
management. Temporal history of event codes embedded in patients' records, investigates frequently
occurring sequences of event codes across patients. There is a framework that enables the
representation, retrieval, and mining of high order latent event structure and relationships within
single and multiple event sequences. There is a wealth of hidden information present in the large
databases. Different data mining techniques can be used for retrieving data. A classifier approach for
detection of diabetes is presented in this paper and shows how Naive Bayes can be used for
classification purpose. In this system, medical data is categories into five categories namely low,
average, high and very high and critical, treatment is given as per the predicted category. The system
will predict the class label of unknown sample. Hence two basic functions namely classification
(training) and prediction (testing) will be performed. An algorithm and database used affects the
accuracy of the system. It can answer complex queries for diagnosing diabetes disease and thus assist
healthcare practitioners to make intelligent clinical decisions which traditional decision support
systems cannot.Over the last decade, so many information visualization techniques have been
developed to support the exploration of large data sets. There are various interactive visual data
mining tools available for visual data analysis. It is possible to perform clinical assessment for visual
interactive knowledge discovery in large electronic health record databases. In this paper, we
proposed that it is possible to develop a tool for data visualization for interactive knowledge
discovery.
Similar to Significance of integrated taxonomy approach in (20)
Submission Deadline: 30th September 2022
Acceptance Notification: Within Three Days’ time period
Online Publication: Within 24 Hrs. time Period
Expected Date of Dispatch of Printed Journal: 5th October 2022
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...IAEME Publication
White layer thickness (WLT) formed and surface roughness in wire electric discharge turning (WEDT) of tungsten carbide composite has been made to model through response surface methodology (RSM). A Taguchi’s standard Design of experiments involving five input variables with three levels has been employed to establish a mathematical model between input parameters and responses. Percentage of cobalt content, spindle speed, Pulse on-time, wire feed and pulse off-time were changed during the experimental tests based on the Taguchi’s orthogonal array L27 (3^13). Analysis of variance (ANOVA) revealed that the mathematical models obtained can adequately describe performance within the parameters of the factors considered. There was a good agreement between the experimental and predicted values in this study.
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURSIAEME Publication
The study explores the reasons for a transgender to become entrepreneurs. In this study transgender entrepreneur was taken as independent variable and reasons to become as dependent variable. Data were collected through a structured questionnaire containing a five point Likert Scale. The study examined the data of 30 transgender entrepreneurs in Salem Municipal Corporation of Tamil Nadu State, India. Simple Random sampling technique was used. Garrett Ranking Technique (Percentile Position, Mean Scores) was used as the analysis for the present study to identify the top 13 stimulus factors for establishment of trans entrepreneurial venture. Economic advancement of a nation is governed upon the upshot of a resolute entrepreneurial doings. The conception of entrepreneurship has stretched and materialized to the socially deflated uncharted sections of transgender community. Presently transgenders have smashed their stereotypes and are making recent headlines of achievements in various fields of our Indian society. The trans-community is gradually being observed in a new light and has been trying to achieve prospective growth in entrepreneurship. The findings of the research revealed that the optimistic changes are taking place to change affirmative societal outlook of the transgender for entrepreneurial ventureship. It also laid emphasis on other transgenders to renovate their traditional living. The paper also highlights that legislators, supervisory body should endorse an impartial canons and reforms in Tamil Nadu Transgender Welfare Board Association.
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURSIAEME Publication
Since ages gender difference is always a debatable theme whether caused by nature, evolution or environment. The birth of a transgender is dreadful not only for the child but also for their parents. The pain of living in the wrong physique and treated as second class victimized citizen is outrageous and fully harboured with vicious baseless negative scruples. For so long, social exclusion had perpetuated inequality and deprivation experiencing ingrained malign stigma and besieged victims of crime or violence across their life spans. They are pushed into the murky way of life with a source of eternal disgust, bereft sexual potency and perennial fear. Although they are highly visible but very little is known about them. The common public needs to comprehend the ravaged arrogance on these insensitive souls and assist in integrating them into the mainstream by offering equal opportunity, treat with humanity and respect their dignity. Entrepreneurship in the current age is endorsing the gender fairness movement. Unstable careers and economic inadequacy had inclined one of the gender variant people called Transgender to become entrepreneurs. These tiny budding entrepreneurs resulted in economic transition by means of employment, free from the clutches of stereotype jobs, raised standard of living and handful of financial empowerment. Besides all these inhibitions, they were able to witness a platform for skill set development that ignited them to enter into entrepreneurial domain. This paper epitomizes skill sets involved in trans-entrepreneurs of Thoothukudi Municipal Corporation of Tamil Nadu State and is a groundbreaking determination to sightsee various skills incorporated and the impact on entrepreneurship.
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONSIAEME Publication
The banking and financial services industries are experiencing increased technology penetration. Among them, the banking industry has made technological advancements to better serve the general populace. The economy focused on transforming the banking sector's system into a cashless, paperless, and faceless one. The researcher wants to evaluate the user's intention for utilising a mobile banking application. The study also examines the variables affecting the user's behaviour intention when selecting specific applications for financial transactions. The researcher employed a well-structured questionnaire and a descriptive study methodology to gather the respondents' primary data utilising the snowball sampling technique. The study includes variables like performance expectations, effort expectations, social impact, enabling circumstances, and perceived risk. Each of the aforementioned variables has a major impact on how users utilise mobile banking applications. The outcome will assist the service provider in comprehending the user's history with mobile banking applications.
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONSIAEME Publication
Technology upgradation in banking sector took the economy to view that payment mode towards online transactions using mobile applications. This system enabled connectivity between banks, Merchant and user in a convenient mode. there are various applications used for online transactions such as Google pay, Paytm, freecharge, mobikiwi, oxygen, phonepe and so on and it also includes mobile banking applications. The study aimed at evaluating the predilection of the user in adopting digital transaction. The study is descriptive in nature. The researcher used random sample techniques to collect the data. The findings reveal that mobile applications differ with the quality of service rendered by Gpay and Phonepe. The researcher suggest the Phonepe application should focus on implementing the application should be user friendly interface and Gpay on motivating the users to feel the importance of request for money and modes of payments in the application.
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINOIAEME Publication
The prototype of a voice-based ATM for visually impaired using Arduino is to help people who are blind. This uses RFID cards which contain users fingerprint encrypted on it and interacts with the users through voice commands. ATM operates when sensor detects the presence of one person in the cabin. After scanning the RFID card, it will ask to select the mode like –normal or blind. User can select the respective mode through voice input, if blind mode is selected the balance check or cash withdraw can be done through voice input. Normal mode procedure is same as the existing ATM.
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...IAEME Publication
There is increasing acceptability of emotional intelligence as a major factor in personality assessment and effective human resource management. Emotional intelligence as the ability to build capacity, empathize, co-operate, motivate and develop others cannot be divorced from both effective performance and human resource management systems. The human person is crucial in defining organizational leadership and fortunes in terms of challenges and opportunities and walking across both multinational and bilateral relationships. The growing complexity of the business world requires a great deal of self-confidence, integrity, communication, conflict and diversity management to keep the global enterprise within the paths of productivity and sustainability. Using the exploratory research design and 255 participants the result of this original study indicates strong positive correlation between emotional intelligence and effective human resource management. The paper offers suggestions on further studies between emotional intelligence and human capital development and recommends for conflict management as an integral part of effective human resource management.
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMYIAEME Publication
Our life journey, in general, is closely defined by the way we understand the meaning of why we coexist and deal with its challenges. As we develop the "inspiration economy", we could say that nearly all of the challenges we have faced are opportunities that help us to discover the rest of our journey. In this note paper, we explore how being faced with the opportunity of being a close carer for an aging parent with dementia brought intangible discoveries that changed our insight of the meaning of the rest of our life journey.
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...IAEME Publication
The main objective of this study is to analyze the impact of aspects of Organizational Culture on the Effectiveness of the Performance Management System (PMS) in the Health Care Organization at Thanjavur. Organizational Culture and PMS play a crucial role in present-day organizations in achieving their objectives. PMS needs employees’ cooperation to achieve its intended objectives. Employees' cooperation depends upon the organization’s culture. The present study uses exploratory research to examine the relationship between the Organization's culture and the Effectiveness of the Performance Management System. The study uses a Structured Questionnaire to collect the primary data. For this study, Thirty-six non-clinical employees were selected from twelve randomly selected Health Care organizations at Thanjavur. Thirty-two fully completed questionnaires were received.
Living in 21st century in itself reminds all of us the necessity of police and its administration. As more and more we are entering into the modern society and culture, the more we require the services of the so called ‘Khaki Worthy’ men i.e., the police personnel. Whether we talk of Indian police or the other nation’s police, they all have the same recognition as they have in India. But as already mentioned, their services and requirements are different after the like 26th November, 2008 incidents, where they without saving their own lives has sacrificed themselves without any hitch and without caring about their respective family members and wards. In other words, they are like our heroes and mentors who can guide us from the darkness of fear, militancy, corruption and other dark sides of life and so on. Now the question arises, if Gandhi would have been alive today, what would have been his reaction/opinion to the police and its functioning? Would he have some thing different in his mind now what he had been in his mind before the partition or would he be going to start some Satyagraha in the form of some improvement in the functioning of the police administration? Really these questions or rather night mares can come to any one’s mind, when there is too much confusion is prevailing in our minds, when there is too much corruption in the society and when the polices working is also in the questioning because of one or the other case throughout the India. It is matter of great concern that we have to thing over our administration and our practical approach because the police personals are also like us, they are part and parcel of our society and among one of us, so why we all are pin pointing towards them.
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...IAEME Publication
The goal of this study was to see how talent management affected employee retention in the selected IT organizations in Chennai. The fundamental issue was the difficulty to attract, hire, and retain talented personnel who perform well and the gap between supply and demand of talent acquisition and retaining them within the firms. The study's main goals were to determine the impact of talent management on employee retention in IT companies in Chennai, investigate talent management strategies that IT companies could use to improve talent acquisition, performance management, career planning and formulate retention strategies that the IT firms could use. The respondents were given a structured close-ended questionnaire with the 5 Point Likert Scale as part of the study's quantitative research design. The target population consisted of 289 IT professionals. The questionnaires were distributed and collected by the researcher directly. The Statistical Package for Social Sciences (SPSS) was used to collect and analyse the questionnaire responses. Hypotheses that were formulated for the various areas of the study were tested using a variety of statistical tests. The key findings of the study suggested that talent management had an impact on employee retention. The studies also found that there is a clear link between the implementation of talent management and retention measures. Management should provide enough training and development for employees, clarify job responsibilities, provide adequate remuneration packages, and recognise employees for exceptional performance.
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...IAEME Publication
Globally, Millions of dollars were spent by the organizations for employing skilled Information Technology (IT) professionals. It is costly to replace unskilled employees with IT professionals possessing technical skills and competencies that aid in interconnecting the business processes. The organization’s employment tactics were forced to alter by globalization along with technological innovations as they consistently diminish to remain lean, outsource to concentrate on core competencies along with restructuring/reallocate personnel to gather efficiency. As other jobs, organizations or professions have become reasonably more appropriate in a shifting employment landscape, the above alterations trigger both involuntary as well as voluntary turnover. The employee view on jobs is also afflicted by the COVID-19 pandemic along with the employee-driven labour market. So, having effective strategies is necessary to tackle the withdrawal rate of employees. By associating Emotional Intelligence (EI) along with Talent Management (TM) in the IT industry, the rise in attrition rate was analyzed in this study. Only 303 respondents were collected out of 350 participants to whom questionnaires were distributed. From the employees of IT organizations located in Bangalore (India), the data were congregated. A simple random sampling methodology was employed to congregate data as of the respondents. Generating the hypothesis along with testing is eventuated. The effect of EI and TM along with regression analysis between TM and EI was analyzed. The outcomes indicated that employee and Organizational Performance (OP) were elevated by effective EI along with TM.
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...IAEME Publication
By implementing talent management strategy, organizations would have the option to retain their skilled professionals while additionally working on their overall performance. It is the course of appropriately utilizing the ideal individuals, setting them up for future top positions, exploring and dealing with their performance, and holding them back from leaving the organization. It is employee performance that determines the success of every organization. The firm quickly obtains an upper hand over its rivals in the event that its employees having particular skills that cannot be duplicated by the competitors. Thus, firms are centred on creating successful talent management practices and processes to deal with the unique human resources. Firms are additionally endeavouring to keep their top/key staff since on the off chance that they leave; the whole store of information leaves the firm's hands. The study's objective was to determine the impact of talent management on organizational performance among the selected IT organizations in Chennai. The study recommends that talent management limitedly affects performance. On the off chance that this talent is appropriately management and implemented properly, organizations might benefit as much as possible from their maintained assets to support development and productivity, both monetarily and non-monetarily.
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...IAEME Publication
Banking regulations act of India, 1949 defines banking as “acceptance of deposits for the purpose of lending or investment from the public, repayment on demand or otherwise and withdrawable through cheques, drafts order or otherwise”, the major participants of the Indian financial system are commercial banks, the financial institution encompassing term lending institutions. Investments institutions, specialized financial institution and the state level development banks, non banking financial companies (NBFC) and other market intermediaries such has the stock brokers and money lenders are among the oldest of the certain variants of NBFC and the oldest market participants. The asset quality of banks is one of the most important indicators of their financial health. The Indian banking sector has been facing severe problems of increasing Non- Performing Assets (NPAs). The NPAs growth directly and indirectly affects the quality of assets and profitability of banks. It also shows the efficiency of banks credit risk management and the recovery effectiveness. NPA do not generate any income, whereas, the bank is required to make provisions for such as assets that why is a double edge weapon. This paper outlines the concept of quality of bank loans of different types like Housing, Agriculture and MSME loans in state Haryana of selected public and private sector banks. This study is highlighting problems associated with the role of commercial bank in financing Small and Medium Scale Enterprises (SME). The overall objective of the research was to assess the effect of the financing provisions existing for the setting up and operations of MSMEs in the country and to generate recommendations for more robust financing mechanisms for successful operation of the MSMEs, in turn understanding the impact of MSME loans on financial institutions due to NPA. There are many research conducted on the topic of Non- Performing Assets (NPA) Management, concerning particular bank, comparative study of public and private banks etc. In this paper the researcher is considering the aggregate data of selected public sector and private sector banks and attempts to compare the NPA of Housing, Agriculture and MSME loans in state Haryana of public and private sector banks. The tools used in the study are average and Anova test and variance. The findings reveal that NPA is common problem for both public and private sector banks and is associated with all types of loans either that is housing loans, agriculture loans and loans to SMES. NPAs of both public and private sector banks show the increasing trend. In 2010-11 GNPA of public and private sector were at same level it was 2% but after 2010-11 it increased in many fold and at present there is GNPA in some more than 15%. It shows the dark area of Indian banking sector.
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...IAEME Publication
An experiment conducted in this study found that BaSO4 changed Nylon 6's mechanical properties. By changing the weight ratios, BaSO4 was used to make Nylon 6. This Researcher looked into how hard Nylon-6/BaSO4 composites are and how well they wear. Experiments were done based on Taguchi design L9. Nylon-6/BaSO4 composites can be tested for their hardness number using a Rockwell hardness testing apparatus. On Nylon/BaSO4, the wear behavior was measured by a wear monitor, pinon-disc friction by varying reinforcement, sliding speed, and sliding distance, and the microstructure of the crack surfaces was observed by SEM. This study provides significant contributions to ultimate strength by increasing BaSO4 content up to 16% in the composites, and sliding speed contributes 72.45% to the wear rate
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...IAEME Publication
The majority of the population in India lives in villages. The village is the back bone of the country. Village or rural industries play an important role in the national economy, particularly in the rural development. Developing the rural economy is one of the key indicators towards a country’s success. Whether it be the need to look after the welfare of the farmers or invest in rural infrastructure, Governments have to ensure that rural development isn’t compromised. The economic development of our country largely depends on the progress of rural areas and the standard of living of rural masses. Village or rural industries play an important role in the national economy, particularly in the rural development. Rural entrepreneurship is based on stimulating local entrepreneurial talent and the subsequent growth of indigenous enterprises. It recognizes opportunity in the rural areas and accelerates a unique blend of resources either inside or outside of agriculture. Rural entrepreneurship brings an economic value to the rural sector by creating new methods of production, new markets, new products and generate employment opportunities thereby ensuring continuous rural development. Social Entrepreneurship has the direct and primary objective of serving the society along with the earning profits. So, social entrepreneurship is different from the economic entrepreneurship as its basic objective is not to earn profits but for providing innovative solutions to meet the society needs which are not taken care by majority of the entrepreneurs as they are in the business for profit making as a sole objective. So, the Social Entrepreneurs have the huge growth potential particularly in the developing countries like India where we have huge societal disparities in terms of the financial positions of the population. Still 22 percent of the Indian population is below the poverty line and also there is disparity among the rural & urban population in terms of families living under BPL. 25.7 percent of the rural population & 13.7 percent of the urban population is under BPL which clearly shows the disparity of the poor people in the rural and urban areas. The need to develop social entrepreneurship in agriculture is dictated by a large number of social problems. Such problems include low living standards, unemployment, and social tension. The reasons that led to the emergence of the practice of social entrepreneurship are the above factors. The research problem lays upon disclosing the importance of role of social entrepreneurship in rural development of India. The paper the tendencies of social entrepreneurship in India, to present successful examples of such business for providing recommendations how to improve situation in rural areas in terms of social entrepreneurship development. Indian government has made some steps towards development of social enterprises, social entrepreneurship, and social in- novation, but a lot remains to be improved.
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...IAEME Publication
Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case.
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...IAEME Publication
Manufacturing industries have witnessed an outburst in productivity. For productivity improvement manufacturing industries are taking various initiatives by using lean tools and techniques. However, in different manufacturing industries, frugal approach is applied in product design and services as a tool for improvement. Frugal approach contributed to prove less is more and seems indirectly contributing to improve productivity. Hence, there is need to understand status of frugal approach application in manufacturing industries. All manufacturing industries are trying hard and putting continuous efforts for competitive existence. For productivity improvements, manufacturing industries are coming up with different effective and efficient solutions in manufacturing processes and operations. To overcome current challenges, manufacturing industries have started using frugal approach in product design and services. For this study, methodology adopted with both primary and secondary sources of data. For primary source interview and observation technique is used and for secondary source review has done based on available literatures in website, printed magazines, manual etc. An attempt has made for understanding application of frugal approach with the study of manufacturing industry project. Manufacturing industry selected for this project study is Mahindra and Mahindra Ltd. This paper will help researcher to find the connections between the two concepts productivity improvement and frugal approach. This paper will help to understand significance of frugal approach for productivity improvement in manufacturing industry. This will also help to understand current scenario of frugal approach in manufacturing industry. In manufacturing industries various process are involved to deliver the final product. In the process of converting input in to output through manufacturing process productivity plays very critical role. Hence this study will help to evolve status of frugal approach in productivity improvement programme. The notion of frugal can be viewed as an approach towards productivity improvement in manufacturing industries.
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENTIAEME Publication
In this paper, we investigated a queuing model of fuzzy environment-based a multiple channel queuing model (M/M/C) ( /FCFS) and study its performance under realistic conditions. It applies a nonagonal fuzzy number to analyse the relevant performance of a multiple channel queuing model (M/M/C) ( /FCFS). Based on the sub interval average ranking method for nonagonal fuzzy number, we convert fuzzy number to crisp one. Numerical results reveal that the efficiency of this method. Intuitively, the fuzzy environment adapts well to a multiple channel queuing models (M/M/C) ( /FCFS) are very well.