1) The document proposes using Hadoop and MapReduce to analyze student result data to provide predictive modeling and insights. This can help students, faculty, and administrators improve outcomes.
2) Traditional data analysis methods take a long time when dealing with large datasets. Hadoop can distribute the work across clusters to speed up analysis. MapReduce breaks the work into smaller tasks that can run in parallel.
3) The proposed system would use Hadoop to extract and analyze accident data, then use predictive modeling to forecast times and locations of high accident rates. Encryption would secure the data during network transfer.
The Architecture of System for Predicting Student Performance based on the Da...Thada Jantakoon
The goals of this study are to develop the architecture of a system for predicting student performance based on data science approaches (SPPS-DSA Architecture) and evaluate the SPPS-DSA Architecture. The research process is divided into two stages: (1) context analysis and (2) development and assessment. The data is analyzed by means of standardized deviations statistically. The research findings suggested that the SPPS-DSA architecture, according to the research findings, consists of three key components: (i) data source, (ii) machine learning methods and attributes, and (iii) data science process. The SPPS-DSA architecture is rated as the highest appropriate overall. Predicting student performance helps educators and students improve their teaching and learning processes. Predicting student performance using various analytical methods is reviewed here. Most researchers used CGPA and internal assessment as data sets. In terms of prediction methods, classification is widely used in educational data science. Researchers most commonly used neural networks and decision trees to predict student performance under classification techniques.
With the growth of voluminous amount of data in educational institutes’, the need is to mine the large dataset to produce some useful information out of it. In this research we focused on to form a decision support system for the educational institutes’ which can help them to know about the placement possibility of students. Our research is not limited to find out placement possibility but we did multi-level analysis on student performance dataset which will predict that what level of interview process a student is likely to pass. For this we have applied Naïve Bayes and Improved Naïve Bayes which is integrated with relief feature selection technique to obtain the prediction. Data analysis was done using NetBeans and WEKA. For this our proposed technique gave better accuracy than existing naïve Bayes which was 84.7% and naïve Bayes gave 80.96% accuracy.
The Architecture of System for Predicting Student Performance based on the Da...Thada Jantakoon
The goals of this study are to develop the architecture of a system for predicting student performance based on data science approaches (SPPS-DSA Architecture) and evaluate the SPPS-DSA Architecture. The research process is divided into two stages: (1) context analysis and (2) development and assessment. The data is analyzed by means of standardized deviations statistically. The research findings suggested that the SPPS-DSA architecture, according to the research findings, consists of three key components: (i) data source, (ii) machine learning methods and attributes, and (iii) data science process. The SPPS-DSA architecture is rated as the highest appropriate overall. Predicting student performance helps educators and students improve their teaching and learning processes. Predicting student performance using various analytical methods is reviewed here. Most researchers used CGPA and internal assessment as data sets. In terms of prediction methods, classification is widely used in educational data science. Researchers most commonly used neural networks and decision trees to predict student performance under classification techniques.
With the growth of voluminous amount of data in educational institutes’, the need is to mine the large dataset to produce some useful information out of it. In this research we focused on to form a decision support system for the educational institutes’ which can help them to know about the placement possibility of students. Our research is not limited to find out placement possibility but we did multi-level analysis on student performance dataset which will predict that what level of interview process a student is likely to pass. For this we have applied Naïve Bayes and Improved Naïve Bayes which is integrated with relief feature selection technique to obtain the prediction. Data analysis was done using NetBeans and WEKA. For this our proposed technique gave better accuracy than existing naïve Bayes which was 84.7% and naïve Bayes gave 80.96% accuracy.
Medical Assistant Design during this Pandemic Like Covid-19AI Publications
In the current world scenario, individuals square measure additional involved regarding their health. However, it's terribly troublesome to get consultation with the doctor just in case of any health problems. Since the invention of the Coronavirus (nCOV-19), it's become a world pandemic. At an equivalent time, it's been a good challenge to hospitals or health care employees to manage the flow of the high variety of cases. particularly in remote areas, it's becoming tougher to consult a doctor once the immediate hit of the epidemic has occurred. So, to steer an honest life, care is incredibly vital. The planned plan is to form a medical chatbot victimization Machine Learning algorithm which will diagnose the illness and supply basic details regarding the illness before consulting a doctor. Several studies will solve this downside with some reasonably chatbot or health assistant. This project report proposes a colloquial care larva that's designed to order, counsel and provides data on generic medicines for diseases to the patients. During this paper, we would like to explore and deepen additional information regarding chatbots that would facilitate individuals to urge an equivalent and correct treatment as a doctor would do. In addition, presenting a virtual assistant may live with the infection severity and connect with registered doctors once symptoms become serious.
Prediction of Default Customer in Banking Sector using Artificial Neural Networkrahulmonikasharma
The aim of this article is to present perdition and risk accuracy analysis of default customer in the banking sector. The neural network is a learning model inspired by biological neuron it is used to estimate and predict that can depend on a large number of inputs. The bank customer dataset from UCI repository, used for data analysis method to extract informative data set from a large volume of the dataset. This dataset is used in the neural network for training data and testing data. In a training of data, the data set is iterated till the desired output. This training data is cross check with test data. This paper focuses on predicting default customer by using deep learning neural network (DNN) algorithm.
Identification of important features and data mining classification technique...IJECEIAES
Employees absenteeism at the work costs organizations billions a year. Prediction of employees’ absenteeism and the reasons behind their absence help organizations in reducing expenses and increasing productivity. Data mining turns the vast volume of human resources data into information that can help in decision-making and prediction. Although the selection of features is a critical step in data mining to enhance the efficiency of the final prediction, it is not yet known which method of feature selection is better. Therefore, this paper aims to compare the performance of three well-known feature selection methods in absenteeism prediction, which are relief-based feature selection, correlation-based feature selection and information-gain feature selection. In addition, this paper aims to find the best combination of feature selection method and data mining technique in enhancing the absenteeism prediction accuracy. Seven classification techniques were used as the prediction model. Additionally, cross-validation approach was utilized to assess the applied prediction models to have more realistic and reliable results. The used dataset was built at a courier company in Brazil with records of absenteeism at work. Regarding experimental results, correlationbased feature selection surpasses the other methods through the performance measurements. Furthermore, bagging classifier was the best-performing data mining technique when features were selected using correlation-based feature selection with an accuracy rate of (92%).
Developing a PhD Research Topic for Your Research| PhD Assistance UKPhDAssistanceUK
According to UGC report, every year approximately 77,000 scholars are enrolling for PhD research programs in India. but only 1/3rd of them successfully complete the doctorate every year (Marg, 2015). This result clearly indicates the difficulty and standard of evaluation of this program, so it is necessary that the scholar thoroughly analyze and select a good PhD research topic before plunging into the research work. There are many methodologies and techniques introduced over a period for effective selection and decision making in various field. Data Driven Decision Making which is also referred as Data Based Decision Making is a modern method which was found to be effective and efficient for strategic and systematic development and decision making. So, this article summarizes the framework, effectiveness, benefits of DDDM in selection of Research topics for the PhD scholars.
For more Details:-
UK: +44 7424997337
Email : info@phdassistance.com
RDAP 15: The Role of Assessment in Research Data ServicesASIS&T
Research Data Access and Preservation Summit, 2015
Minneapolis, MN
April 22-23, 2015
Amanda Whitmire, Jake Carlson, Patricia Hswe, Susan Wells Parham, Lizzy Rolando and Brian Westra
“Using assessment of NSF data management plans to enable evidence-based evolution of research data services”
Travis Weller, Amalia Monroe-Gulick
“Evaluating Research Needs by Methodology: Assessment at the University of Kansas”
Kathleen Fear, Data Librarian, University of Rochester
“Where’s the data? Assessing researcher compliance with publisher requirements for data sharing”
Biometric Identification and Authentication Providence using Fingerprint for ...IJECEIAES
The raise in the recent security incidents of cloud computing and its challenges is to secure the data. To solve this problem, the integration of mobile with cloud computing, Mobile biometric authentication in cloud computing is presented in this paper. To enhance the security, the biometric authentication is being used, since the Mobile cloud computing is popular among the mobile user. This paper examines how the mobile cloud computing (MCC) is used in security issue with finger biometric authentication model. Through this fingerprint biometric, the secret code is generated by entropy value. This enables the person to request for accessing the data in the desk computer. When the person requests the access to the authorized user through Bluetooth in mobile, the Authorized user sends the permit access through fingerprint secret code. Finally this fingerprint is verified with the database in the Desk computer. If it is matched, then the computer can be accessed by the requested person.
Selecting Experts Using Data Quality Conceptsijdms
Personal networks are not always diverse or large enough to reach those with the right information. This
problem increases when assembling a group of experts from around the world, something which is a
challenge in Future-oriented Technology Analysis (FTA). In this work, we address the formation of a panel
of experts, specifically how to select a group of experts from a huge group of people. We propose an
approach which uses data quality dimensions to improve expert selection quality and provide quality
metrics to the forecaster. We performed a case study and successfully showed that it is possible to use data
quality methods to support the expert search process.
Slides presenting preliminary overview of thesis work presented at the International Conference on Electronic Learning in the Workplace at Columbia University on June 11, 2010.
Medical Assistant Design during this Pandemic Like Covid-19AI Publications
In the current world scenario, individuals square measure additional involved regarding their health. However, it's terribly troublesome to get consultation with the doctor just in case of any health problems. Since the invention of the Coronavirus (nCOV-19), it's become a world pandemic. At an equivalent time, it's been a good challenge to hospitals or health care employees to manage the flow of the high variety of cases. particularly in remote areas, it's becoming tougher to consult a doctor once the immediate hit of the epidemic has occurred. So, to steer an honest life, care is incredibly vital. The planned plan is to form a medical chatbot victimization Machine Learning algorithm which will diagnose the illness and supply basic details regarding the illness before consulting a doctor. Several studies will solve this downside with some reasonably chatbot or health assistant. This project report proposes a colloquial care larva that's designed to order, counsel and provides data on generic medicines for diseases to the patients. During this paper, we would like to explore and deepen additional information regarding chatbots that would facilitate individuals to urge an equivalent and correct treatment as a doctor would do. In addition, presenting a virtual assistant may live with the infection severity and connect with registered doctors once symptoms become serious.
Prediction of Default Customer in Banking Sector using Artificial Neural Networkrahulmonikasharma
The aim of this article is to present perdition and risk accuracy analysis of default customer in the banking sector. The neural network is a learning model inspired by biological neuron it is used to estimate and predict that can depend on a large number of inputs. The bank customer dataset from UCI repository, used for data analysis method to extract informative data set from a large volume of the dataset. This dataset is used in the neural network for training data and testing data. In a training of data, the data set is iterated till the desired output. This training data is cross check with test data. This paper focuses on predicting default customer by using deep learning neural network (DNN) algorithm.
Identification of important features and data mining classification technique...IJECEIAES
Employees absenteeism at the work costs organizations billions a year. Prediction of employees’ absenteeism and the reasons behind their absence help organizations in reducing expenses and increasing productivity. Data mining turns the vast volume of human resources data into information that can help in decision-making and prediction. Although the selection of features is a critical step in data mining to enhance the efficiency of the final prediction, it is not yet known which method of feature selection is better. Therefore, this paper aims to compare the performance of three well-known feature selection methods in absenteeism prediction, which are relief-based feature selection, correlation-based feature selection and information-gain feature selection. In addition, this paper aims to find the best combination of feature selection method and data mining technique in enhancing the absenteeism prediction accuracy. Seven classification techniques were used as the prediction model. Additionally, cross-validation approach was utilized to assess the applied prediction models to have more realistic and reliable results. The used dataset was built at a courier company in Brazil with records of absenteeism at work. Regarding experimental results, correlationbased feature selection surpasses the other methods through the performance measurements. Furthermore, bagging classifier was the best-performing data mining technique when features were selected using correlation-based feature selection with an accuracy rate of (92%).
Developing a PhD Research Topic for Your Research| PhD Assistance UKPhDAssistanceUK
According to UGC report, every year approximately 77,000 scholars are enrolling for PhD research programs in India. but only 1/3rd of them successfully complete the doctorate every year (Marg, 2015). This result clearly indicates the difficulty and standard of evaluation of this program, so it is necessary that the scholar thoroughly analyze and select a good PhD research topic before plunging into the research work. There are many methodologies and techniques introduced over a period for effective selection and decision making in various field. Data Driven Decision Making which is also referred as Data Based Decision Making is a modern method which was found to be effective and efficient for strategic and systematic development and decision making. So, this article summarizes the framework, effectiveness, benefits of DDDM in selection of Research topics for the PhD scholars.
For more Details:-
UK: +44 7424997337
Email : info@phdassistance.com
RDAP 15: The Role of Assessment in Research Data ServicesASIS&T
Research Data Access and Preservation Summit, 2015
Minneapolis, MN
April 22-23, 2015
Amanda Whitmire, Jake Carlson, Patricia Hswe, Susan Wells Parham, Lizzy Rolando and Brian Westra
“Using assessment of NSF data management plans to enable evidence-based evolution of research data services”
Travis Weller, Amalia Monroe-Gulick
“Evaluating Research Needs by Methodology: Assessment at the University of Kansas”
Kathleen Fear, Data Librarian, University of Rochester
“Where’s the data? Assessing researcher compliance with publisher requirements for data sharing”
Biometric Identification and Authentication Providence using Fingerprint for ...IJECEIAES
The raise in the recent security incidents of cloud computing and its challenges is to secure the data. To solve this problem, the integration of mobile with cloud computing, Mobile biometric authentication in cloud computing is presented in this paper. To enhance the security, the biometric authentication is being used, since the Mobile cloud computing is popular among the mobile user. This paper examines how the mobile cloud computing (MCC) is used in security issue with finger biometric authentication model. Through this fingerprint biometric, the secret code is generated by entropy value. This enables the person to request for accessing the data in the desk computer. When the person requests the access to the authorized user through Bluetooth in mobile, the Authorized user sends the permit access through fingerprint secret code. Finally this fingerprint is verified with the database in the Desk computer. If it is matched, then the computer can be accessed by the requested person.
Selecting Experts Using Data Quality Conceptsijdms
Personal networks are not always diverse or large enough to reach those with the right information. This
problem increases when assembling a group of experts from around the world, something which is a
challenge in Future-oriented Technology Analysis (FTA). In this work, we address the formation of a panel
of experts, specifically how to select a group of experts from a huge group of people. We propose an
approach which uses data quality dimensions to improve expert selection quality and provide quality
metrics to the forecaster. We performed a case study and successfully showed that it is possible to use data
quality methods to support the expert search process.
Slides presenting preliminary overview of thesis work presented at the International Conference on Electronic Learning in the Workplace at Columbia University on June 11, 2010.
Abstract
Mostly 5 to 15% of the women in the stage of reproduction face the disease called Polycystic Ovarian Syndrome (PCOS) which is the multifaceted, heterogeneous and complex. The long term consequences diseases like endometrial hyperplasia, type 2 diabetes mellitus and coronary disease are caused by the polycystic ovaries, chronic anovulation and hyperandrogenism are characterized with the resistance of insulin and the hypertension, abdominal obesity and dyslipidemia and hyperinsulinemia are called as Metabolic syndrome (frequent metabolic traits) The above cause the common disease called Anovulatory infertility. Computer based information along with advanced Data mining techniques are used for appropriate results. Classification is a classic data mining task, with roots in machine learning. Naïve Bayesian, Artificial Neural Network, Decision Tree, Support Vector Machines are the classification tasks in the data mining. Feature selection methods involve generation of the subset, evaluation of each subset, criteria for stopping the search and validation procedures. The characteristics of the search method used are important with respect to the time efficiency of the feature selection methods. PCA (Principle Component Analysis), Information gain Subset Evaluation, Fuzzy rough set evaluation, Correlation based Feature Selection (CFS) are some of the feature selection techniques, greedy first search, ranker etc are the search algorithms that are used in the feature selection. In this paper, a new algorithm which is based on Fuzzy neural subset evaluation and artificial neural network is proposed which reduces the task of classification and feature selection separately. This algorithm combines the neural fuzzy rough subset evaluation and artificial neural network together for the better performance than doing the tasks separately.
Keywords: ANN, SVM, PCA, CFS
ow-a-days data volumes are growing rapidly in several domains. Many factors have contributed to this growth, including inter alia proliferation of observational devices, miniaturization of various sensors ,improved logging and tracking of systems, and improvements in the quality and capacity of both disk storage and networks .Analyzing such data provides insights that can be used to guide decision making. To be effective, analysis must be timely and cope with data scales. The scale of the data and the rates at which they arrive make manual inspection infeasible. As an educational management tool, predictive analytics can help and improve the quality of education by letting decision makers address critical issues such as enrollment management and curriculum Development. This paper presents an analytical study of this approach’s prospects for education planning. The goals of predictive analytics are to produce relevant information, actionable insight, better outcomes, and smarter decisions, and to predict future events by analyzing the volume, veracity, velocity, variety, value of large amounts of data and interactive exploration.
Recommendation of Data Mining Technique in Higher Education Prof. Priya Thaka...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Running Header 1APPLICATION DEVELOPMENT METHODS2.docxrtodd599
Running Header: 1
APPLICATION DEVELOPMENT METHODS 2
Unit 1 Group Project
Application Development Methods
Group 4
John Holmberg, Sean Austin, Christian Dillon, Charles Williams, Matthew Serdy, Frank Opoku
April 10, 2019
IT487 – IT Capstone 1
Nolyn Johnson
Table of Contents
Section 1 - Overview of Company and Client Business Case 3
Section 2 - Application Requirement Elicitation Strategy 5
Section 3 - System Components and Design Requirements 7
Section 4 - Methodology for Application Development Process 8
Section 5 - Complete Features and Trade-off Analysis 10
Section 6 - Milestones and Deliverables Based on Date and Dependencies 11
Section 7 - System Architecture Aligned with System Requirements 12
Section 8 - Technical Design Document 13
Section 9 - Design Review Checklist 14
Section 10 - Testing and Deployment 15
References 16
Section 1 - Overview of Company and Client Business Case
The company Education Information Systems. (EiS) is an information and management company that specializes in the creation and care of large-scale educational information and technology systems. EiS has implemented and managed systems ranging from the pre-K to 12th year primary school systems, and is developing larger scale systems to facilitate collegiate, graduate and post graduate educational institutions. EiS is a privately held organization that has the primary focus of providing the best possible systems to help grow the educational sector. Previous clients have implemented system wide software replacement and upgrades. With a stellar track record of previous educational institutions, and references, EiS has completed all the projects on time, and within budgetary guidelines. All problem issues or negative feedback from clients were handled in professional and timely manner that resulted in a completely satisfied client.
Moving toward post high school educational institutions, EiS is working with an extremely talented development team to move into the graduate and post graduate sector with ease. With new projects being developed, and more clients, EiS also works to recruit the best talent in the development, and technical aspects of information technology.
The information system to be developed by EiS for the institution will allow for all student, and faculty to store, share, and secure data. Utilizing a web-based UI, the information will be easily accessed, with the proper credentials. Data can be shared among staff, and students with preferences designed to mitigate corruption of data, loss of information, especially personal and financial information. All faculty and staff can be added to the application via an admin portal and all security is designated there. All remote access to the application will require a 2 factor
authentication system for another level of security to ensure that the proper access protocols are being followed. All information that is stored will be designed to the student or faculty member, and kept throughout the .
Running Header: 1
SYSTEM ARCHITECTURE 2
Unit 3 Group Project
System Architecture
Group 4
John Holmberg, Sean Austin, Christian Dillon, Charles Williams, Matthew Serdy, Frank Opoku
24 April 2019
IT487 – IT Capstone 1
Nolyn Johnson
Table of Contents
Section 1 - Overview of Company and Client Business Case 3
Section 2 - Application Requirement Elicitation Strategy 5
Section 3 - System Components and Design Requirements 7
Section 4 - Methodology for Application Development Process 10
Section 5 - Complete Features and Trade-off Analysis 12
Section 6 - Milestones and Deliverables Based on Date and Dependencies 15
Section 7 - System Architecture Aligned with System Requirements 21
Section 8 - Technical Design Document 24
Section 9 - Design Review Checklist 25
Section 10 - Testing and Deployment 26
References 27
Section 1 - Overview of Company and Client Business Case
The company Education Information Systems. (EiS) is an information and management company that specializes in the creation and care of large-scale educational information and technology systems. EiS has implemented and managed systems ranging from the pre-K to 12th year primary school systems, and is developing larger scale systems to facilitate collegiate, graduate and post graduate educational institutions. EiS is a privately held organization that has the primary focus of providing the best possible systems to help grow the educational sector. Previous clients have implemented system wide software replacement and upgrades. With a stellar track record of previous educational institutions, and references, EiS has completed all the projects on time, and within budgetary guidelines. All problem issues or negative feedback from clients were handled in professional and timely manner that resulted in a completely satisfied client.
Moving toward post high school educational institutions, EiS is working with an extremely talented development team to move into the graduate and post graduate sector with ease. With new projects being developed, and more clients, EiS also works to recruit the best talent in the development, and technical aspects of information technology.
The information system to be developed by EiS for the institution will allow for all student, and faculty to store, share, and secure data. Utilizing a web-based UI, the information will be easily accessed, with the proper credentials. Data can be shared among staff, and students with preferences designed to mitigate corruption of data, loss of information, especially personal and financial information. All faculty and staff can be added to the application via an admin portal and all security is designated there. All remote access to the application will require a 2 factor
authentication system for another level of security to ensure that the proper access protocols are being followed. All information that is stored will be designed to the student or faculty member, and kept throughout the students’ academic caree.
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.
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.
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.
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.
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