This document discusses using machine learning to predict the risk factor of patients with hepatocellular carcinoma (HCC or liver cancer) based on medical test results. It involves collecting patient data, preprocessing the data, feature selection to identify key predictive features, and using machine learning algorithms like support vector machines (SVM) and random forests. The best model achieved 95% accuracy using SVM with 5 selected features to classify patients as high or low risk, where high risk means less than one year lifetime. The system could help predict survival time and guide treatment decisions for liver cancer patients.
Framework for efficient transformation for complex medical data for improving...IJECEIAES
The adoption of various technological advancement has been already adopted in the area of healthcare sector. This adoption facilitates involuntary generation of medical data that can be autonomously programmed to be forwarded to a destined hub in the form of cloud storage units. However, owing to such technologies there is massive formation of complex medical data that significantly acts as an overhead towards performing analytical operation as well as unwanted storage utilization. Therefore, the proposed system implements a novel transformation technique that is capable of using a template based stucture over cloud for generating structured data from highly unstructured data in a non-conventional manner. The contribution of the propsoed methodology is that it offers faster processing and storage optimization. The study outcome also proves this fact to show propsoed scheme excels better in performance in contrast to existing data transformation scheme.
Iganfis Data Mining Approach for Forecasting Cancer Threatsijsrd.com
Healthcare facilities have at their disposal vast amounts of cancer patients’ data. Medical practitioners require more efficient techniques to extract relevant knowledge from this data for accurate decision-making. However the challenge is how to extract and act upon it in a timely manner. If well engineered, the huge data can aid in developing expert systems for decision support that can assist physicians in diagnosing and predicting some debilitating life threatening diseases such as cancer. Expert systems for decision support can reduce the cost, the waiting time, and liberate medical practitioners for more research, as well as reduce errors and mistakes that can be made by humans due to fatigue and tiredness. The process of utilizing health data effectively however, involves many challenges such as the problem of missing feature values, the curse of dimensionality due to a large number of attributes, and the course of actions to determine the features that can lead to more accurate diagnosis. Effective data mining tools can assist in early detection of diseases such as cancer. In This paper, we propose a new approach called IGANFIS. This approach optimally minimizes the number of features using the information gain (IG) algorithm which is usually used in text categorization to select the quality of text. The IG will be used for selecting the quality of cancer features by virtue of reducing them in number. The reduced number quality features dataset will then be applied to the Adaptive Neuro Fuzzy Inference System (ANFIS) to train and test the proposed approach. ANFIS method of training is ideally the hybrid learning algorithm which uses the gradient descent method and Least Square Estimate (LSE) for computing the error measure for each training pair. Each cycle of the ANFIS hybrid learning consists of a forward pass to present the input vector calculating the node outputs layer by layer repeating the process for all data and a backward pass using the steepest descent algorithm to update parameters, a process called back propagation.
Framework for efficient transformation for complex medical data for improving...IJECEIAES
The adoption of various technological advancement has been already adopted in the area of healthcare sector. This adoption facilitates involuntary generation of medical data that can be autonomously programmed to be forwarded to a destined hub in the form of cloud storage units. However, owing to such technologies there is massive formation of complex medical data that significantly acts as an overhead towards performing analytical operation as well as unwanted storage utilization. Therefore, the proposed system implements a novel transformation technique that is capable of using a template based stucture over cloud for generating structured data from highly unstructured data in a non-conventional manner. The contribution of the propsoed methodology is that it offers faster processing and storage optimization. The study outcome also proves this fact to show propsoed scheme excels better in performance in contrast to existing data transformation scheme.
Iganfis Data Mining Approach for Forecasting Cancer Threatsijsrd.com
Healthcare facilities have at their disposal vast amounts of cancer patients’ data. Medical practitioners require more efficient techniques to extract relevant knowledge from this data for accurate decision-making. However the challenge is how to extract and act upon it in a timely manner. If well engineered, the huge data can aid in developing expert systems for decision support that can assist physicians in diagnosing and predicting some debilitating life threatening diseases such as cancer. Expert systems for decision support can reduce the cost, the waiting time, and liberate medical practitioners for more research, as well as reduce errors and mistakes that can be made by humans due to fatigue and tiredness. The process of utilizing health data effectively however, involves many challenges such as the problem of missing feature values, the curse of dimensionality due to a large number of attributes, and the course of actions to determine the features that can lead to more accurate diagnosis. Effective data mining tools can assist in early detection of diseases such as cancer. In This paper, we propose a new approach called IGANFIS. This approach optimally minimizes the number of features using the information gain (IG) algorithm which is usually used in text categorization to select the quality of text. The IG will be used for selecting the quality of cancer features by virtue of reducing them in number. The reduced number quality features dataset will then be applied to the Adaptive Neuro Fuzzy Inference System (ANFIS) to train and test the proposed approach. ANFIS method of training is ideally the hybrid learning algorithm which uses the gradient descent method and Least Square Estimate (LSE) for computing the error measure for each training pair. Each cycle of the ANFIS hybrid learning consists of a forward pass to present the input vector calculating the node outputs layer by layer repeating the process for all data and a backward pass using the steepest descent algorithm to update parameters, a process called back propagation.
Cancer prognosis prediction using balanced stratified samplingijscai
High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the
rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the
analytical challenge exists in double. The use of effective sampling technique in classification algorithms
always yields good prediction accuracy. The SEER public use cancer database provides various prominent
class labels for prognosis prediction. The main objective of this paper is to find the effect of sampling
techniques in classifying the prognosis variable and propose an ideal sampling method based on the
outcome of the experimentation. In the first phase of this work the traditional random sampling and
stratified sampling techniques have been used. At the next level the balanced stratified sampling with
variations as per the choice of the prognosis class labels have been tested. Much of the initial time has been
focused on performing the pre-processing of the SEER data set. The classification model for
experimentation has been built using the breast cancer, respiratory cancer and mixed cancer data sets with
three traditional classifiers namely Decision Tree, Naïve Bayes and K-Nearest Neighbour. The three
prognosis factors survival, stage and metastasis have been used as class labels for experimental
comparisons. The results shows a steady increase in the prediction accuracy of balanced stratified model
as the sample size increases, but the traditional approach fluctuates before the optimum results.
A new model for large dataset dimensionality reduction based on teaching lear...TELKOMNIKA JOURNAL
One of the human diseases with a high rate of mortality each year is breast cancer (BC). Among all the forms of cancer, BC is the commonest cause of death among women globally. Some of the effective ways of data classification are data mining and classification methods. These methods are particularly efficient in the medical field due to the presence of irrelevant and redundant attributes in medical datasets. Such redundant attributes are not needed to obtain an accurate estimation of disease diagnosis. Teaching learning-based optimization (TLBO) is a new metaheuristic that has been successfully applied to several intractable optimization problems in recent years. This paper presents the use of a multi-objective TLBO algorithm for the selection of feature subsets in automatic BC diagnosis. For the classification task in this work, the logistic regression (LR) method was deployed. From the results, the projected method produced better BC dataset classification accuracy (classified into malignant and benign). This result showed that the projected TLBO is an efficient features optimization technique for sustaining data-based decision-making systems.
EVOLVING EFFICIENT CLUSTERING AND CLASSIFICATION PATTERNS IN LYMPHOGRAPHY DAT...ijsc
Data mining refers to the process of retrieving knowledge by discovering novel and relative patterns from
large datasets. Clustering and Classification are two distinct phases in data mining that work to provide an
established, proven structure from a voluminous collection of facts. A dominant area of modern-day
research in the field of medical investigations includes disease prediction and malady categorization. In
this paper, our focus is to analyze clusters of patient records obtained via unsupervised clustering
techniques and compare the performance of classification algorithms on the clinical data. Feature
selection is a supervised method that attempts to select a subset of the predictor features based on the
information gain. The Lymphography dataset comprises of 18 predictor attributes and 148 instances with
the class label having four distinct values. This paper highlights the accuracy of eight clustering algorithms
in detecting clusters of patient records and predictor attributes and highlights the performance of sixteen
classification algorithms on the Lymphography dataset that enables the classifier to accurately perform
multi-class categorization of medical data. Our work asserts the fact that the Random Tree algorithm and
the Quinlan’s C4.5 algorithm give 100 percent classification accuracy with all the predictor features and
also with the feature subset selected by the Fisher Filtering feature selection algorithm.. It is also stated
here that the Density Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm
offers increased clustering accuracy in less computation time.
Supervised machine learning based liver disease prediction approach with LASS...journalBEEI
In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.
Breast Cancer Diagnosis using a Hybrid Genetic Algorithm for Feature Selection based on Mutual Information (Abeer Alzubaidi, Georgina Cosma, David Brown and Graham Pockley)
Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
Medical informatics growth can be observed now days. Advancement in different medical fields
discovers the various critical diseases and provides the guidelines for their cure. This has been possible
only because of well heeled medical databases as well as automation of data analysis process. Towards
this analysis process lots of learning and intelligence is required, the data mining techniques provides the
basis for that and various data mining techniques are available like Decision tree Induction, Rule Based
Classification or mining, Support vector machine, Stochastic classification, Logistic regression, Naïve
bayes, Artificial Neural Network & Fuzzy Logic, Genetic Algorithms. This paper provides the basic of
data mining with their effective techniques availability in medical sciences & reveals the efforts done on
medical databases using data mining techniques for human disease diagnosis.
Improve The Performance of K-means by using Genetic Algorithm for Classificat...IJECEIAES
In this research the k-means method was used for classification purposes after it was improved using genetic algorithms. An automated classification system for heart attack was implemented based on the intelligent recruitment of computer capabilities at the same time characterized by high performance based on (270) real cases stored within a globally database known (Statlog). The proposed system aims to support the efforts of staff in medical felid to reduce the diagnostic errors committed by doctors who do not have sufficient experience or because of the fatigue that the doctor suffers as a result of work pressure. The proposed system goes through two stages: in the first-stage genetic algorithm is used to select important features that have a strong influence in the classification process. These features forms the inputs to the K-means method in the second-stage which uses the selected features to divide the database into two groups one of them contain cases infected with the disease while the other group contains the correct cases depending on the distance Euclidean. The comparison of performance for the method (Kmeans) before and after addition genetic algorithm shows that the accuracy of the classification improves remarkably where the accuracy of classification was raised from (68..1481) in the case of use (k- means only) to (84.741) when improved the method by using genetic algorithm.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...ahmad abdelhafeez
The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
A Threshold fuzzy entropy based feature selection method applied in various b...IJMER
Large amount of data have been stored and manipulated using various database
technologies. Processing all the attributes for the particular means is the difficult task. To avoid such
difficulties, feature selection process is processed.In this paper,we are collect a eight various benchmark
datasets from UCI repository.Feature selection process is carried out using fuzzy entropy based
relevance measure algorithm and follows three selection strategies like Mean selection strategy,Half
selection strategy and Neural network for threshold selection strategy. After the features are selected,
they are evaluated using Radial Basis Function (RBF) network,Stacking,Bagging,AdaBoostM1 and Antminer
classification methodologies.The test results depicts that Neural network for threshold selection
strategy works well in selecting features and Ant-miner methodology works best in bringing out better
accuracy with selected feature than processing with original dataset.The obtained result of this
experiment shows that clearly the Ant-miner is superiority than other classifiers.Thus, this proposed Antminer
algorithm could be a more suitable method for producing good results with fewer features than
the original datasets.
Cancer prognosis prediction using balanced stratified samplingijscai
High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the
rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the
analytical challenge exists in double. The use of effective sampling technique in classification algorithms
always yields good prediction accuracy. The SEER public use cancer database provides various prominent
class labels for prognosis prediction. The main objective of this paper is to find the effect of sampling
techniques in classifying the prognosis variable and propose an ideal sampling method based on the
outcome of the experimentation. In the first phase of this work the traditional random sampling and
stratified sampling techniques have been used. At the next level the balanced stratified sampling with
variations as per the choice of the prognosis class labels have been tested. Much of the initial time has been
focused on performing the pre-processing of the SEER data set. The classification model for
experimentation has been built using the breast cancer, respiratory cancer and mixed cancer data sets with
three traditional classifiers namely Decision Tree, Naïve Bayes and K-Nearest Neighbour. The three
prognosis factors survival, stage and metastasis have been used as class labels for experimental
comparisons. The results shows a steady increase in the prediction accuracy of balanced stratified model
as the sample size increases, but the traditional approach fluctuates before the optimum results.
A new model for large dataset dimensionality reduction based on teaching lear...TELKOMNIKA JOURNAL
One of the human diseases with a high rate of mortality each year is breast cancer (BC). Among all the forms of cancer, BC is the commonest cause of death among women globally. Some of the effective ways of data classification are data mining and classification methods. These methods are particularly efficient in the medical field due to the presence of irrelevant and redundant attributes in medical datasets. Such redundant attributes are not needed to obtain an accurate estimation of disease diagnosis. Teaching learning-based optimization (TLBO) is a new metaheuristic that has been successfully applied to several intractable optimization problems in recent years. This paper presents the use of a multi-objective TLBO algorithm for the selection of feature subsets in automatic BC diagnosis. For the classification task in this work, the logistic regression (LR) method was deployed. From the results, the projected method produced better BC dataset classification accuracy (classified into malignant and benign). This result showed that the projected TLBO is an efficient features optimization technique for sustaining data-based decision-making systems.
EVOLVING EFFICIENT CLUSTERING AND CLASSIFICATION PATTERNS IN LYMPHOGRAPHY DAT...ijsc
Data mining refers to the process of retrieving knowledge by discovering novel and relative patterns from
large datasets. Clustering and Classification are two distinct phases in data mining that work to provide an
established, proven structure from a voluminous collection of facts. A dominant area of modern-day
research in the field of medical investigations includes disease prediction and malady categorization. In
this paper, our focus is to analyze clusters of patient records obtained via unsupervised clustering
techniques and compare the performance of classification algorithms on the clinical data. Feature
selection is a supervised method that attempts to select a subset of the predictor features based on the
information gain. The Lymphography dataset comprises of 18 predictor attributes and 148 instances with
the class label having four distinct values. This paper highlights the accuracy of eight clustering algorithms
in detecting clusters of patient records and predictor attributes and highlights the performance of sixteen
classification algorithms on the Lymphography dataset that enables the classifier to accurately perform
multi-class categorization of medical data. Our work asserts the fact that the Random Tree algorithm and
the Quinlan’s C4.5 algorithm give 100 percent classification accuracy with all the predictor features and
also with the feature subset selected by the Fisher Filtering feature selection algorithm.. It is also stated
here that the Density Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm
offers increased clustering accuracy in less computation time.
Supervised machine learning based liver disease prediction approach with LASS...journalBEEI
In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.
Breast Cancer Diagnosis using a Hybrid Genetic Algorithm for Feature Selection based on Mutual Information (Abeer Alzubaidi, Georgina Cosma, David Brown and Graham Pockley)
Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
Medical informatics growth can be observed now days. Advancement in different medical fields
discovers the various critical diseases and provides the guidelines for their cure. This has been possible
only because of well heeled medical databases as well as automation of data analysis process. Towards
this analysis process lots of learning and intelligence is required, the data mining techniques provides the
basis for that and various data mining techniques are available like Decision tree Induction, Rule Based
Classification or mining, Support vector machine, Stochastic classification, Logistic regression, Naïve
bayes, Artificial Neural Network & Fuzzy Logic, Genetic Algorithms. This paper provides the basic of
data mining with their effective techniques availability in medical sciences & reveals the efforts done on
medical databases using data mining techniques for human disease diagnosis.
Improve The Performance of K-means by using Genetic Algorithm for Classificat...IJECEIAES
In this research the k-means method was used for classification purposes after it was improved using genetic algorithms. An automated classification system for heart attack was implemented based on the intelligent recruitment of computer capabilities at the same time characterized by high performance based on (270) real cases stored within a globally database known (Statlog). The proposed system aims to support the efforts of staff in medical felid to reduce the diagnostic errors committed by doctors who do not have sufficient experience or because of the fatigue that the doctor suffers as a result of work pressure. The proposed system goes through two stages: in the first-stage genetic algorithm is used to select important features that have a strong influence in the classification process. These features forms the inputs to the K-means method in the second-stage which uses the selected features to divide the database into two groups one of them contain cases infected with the disease while the other group contains the correct cases depending on the distance Euclidean. The comparison of performance for the method (Kmeans) before and after addition genetic algorithm shows that the accuracy of the classification improves remarkably where the accuracy of classification was raised from (68..1481) in the case of use (k- means only) to (84.741) when improved the method by using genetic algorithm.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...ahmad abdelhafeez
The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
A Threshold fuzzy entropy based feature selection method applied in various b...IJMER
Large amount of data have been stored and manipulated using various database
technologies. Processing all the attributes for the particular means is the difficult task. To avoid such
difficulties, feature selection process is processed.In this paper,we are collect a eight various benchmark
datasets from UCI repository.Feature selection process is carried out using fuzzy entropy based
relevance measure algorithm and follows three selection strategies like Mean selection strategy,Half
selection strategy and Neural network for threshold selection strategy. After the features are selected,
they are evaluated using Radial Basis Function (RBF) network,Stacking,Bagging,AdaBoostM1 and Antminer
classification methodologies.The test results depicts that Neural network for threshold selection
strategy works well in selecting features and Ant-miner methodology works best in bringing out better
accuracy with selected feature than processing with original dataset.The obtained result of this
experiment shows that clearly the Ant-miner is superiority than other classifiers.Thus, this proposed Antminer
algorithm could be a more suitable method for producing good results with fewer features than
the original datasets.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.