This paper aims to design and develop a rate of return analysis system between the purchase and rental of game equipment, which will help players to evaluate the potential rate of return between the purchase and rental of game equipment. This paper first introduces the current situation and problems of the game equipment market, and analyzes the existing research and solutions. We then propose a system design based on data analysis and machine learning, including key steps such as data collection, data processing, model construction, and system implementation. Finally, we test and evaluate the system, and analyze and discuss the results to verify the effectiveness and feasibility of the system. Nan Zhang | Guangyuan Zhang | Tianyi Han "Design and Development of Return Analysis System Between Purchase and Rental of Game Equipment" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-5 , October 2023, URL: https://www.ijtsrd.com/papers/ijtsrd60051.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/60051/design-and-development-of-return-analysis-system-between-purchase-and-rental-of-game-equipment/nan-zhang
This document describes a system to predict cricket scores and match winners in the Indian Premier League (IPL) using machine learning algorithms. The system has two parts: 1) predicting live scores using lasso regression based on parameters like overs bowled, runs scored etc, and 2) predicting the winning team using a random forest classifier based on parameters like toss winner and venue. The authors collected ball-by-ball data from Kaggle, performed data preprocessing, and divided the data into training and test sets. They developed a graphical user interface using Flask to allow users to input match details and get predictions. The system aims to improve fan engagement with the IPL by providing data-driven score and winner predictions.
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This document discusses using machine learning techniques to predict stock market prices. It proposes building a machine learning model that uses historical stock data to predict future stock prices. The model would go through preprocessing, processing, and regression analysis of the dataset to make predictions. Predicting stock market movements accurately is challenging, but this model aims to generate results using machine learning and deep learning algorithms on the dataset to help investors make trading decisions.
This document provides an overview of the Scorum platform, which aims to be a blockchain-powered sports media platform that rewards users. Key features include a blogging platform for sports content, an integrated statistics center, fantasy sports integration, and a commission-free betting exchange. The platform issues Scorum Coins and Scorum Power tokens to power its transparent reward system for authors and active members. The team has developed prototypes of the statistics center and fantasy sports platform. The goal is to finance ongoing development through an upcoming token crowdsale and establish Scorum as a cutting-edge sports media source.
The document discusses the future state of analytics in gaming devices and provides recommendations for harnessing analytics capabilities. It recommends understanding gamers through multidimensional data on behaviors, attitudes, demographics and social interactions. Platform providers should identify and engage social influencers who can help spread word of mouth. Combining behavioral data with attitudinal data from surveys can provide insights on gamer motivations and improve the gaming experience. Analytics can optimize business decisions around customer engagement, lifetime value, and marketing effectiveness.
This document describes a system to predict cricket match scores and outcomes using machine learning algorithms. It involves collecting historical match data, cleaning the data, splitting it into training and test sets, and using algorithms like Naive Bayes, SVM, K-Means, logistic regression, and decision trees. The proposed system architecture involves data collection, feature extraction, model training, and prediction. Performance is evaluated using metrics like accuracy, precision, and recall from a confusion matrix. The authors aim to use multiple algorithms and compare their relative performance to improve predictions.
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This document summarizes a research project that aims to analyze gamer behavior profiles by using clustering algorithms on game telemetry data. The project involves collecting game data, preprocessing the data, applying clustering algorithms like K-means, DBSCAN, K-medoids and Gaussian Mixture Models to segment players into groups based on their gameplay behavior. It will then evaluate the results using metrics like Davies-Bouldin Index, Silhouette Score and Sum of Squared Errors to validate the clusters. The goal is to help game developers better understand player tendencies to improve games and increase popularity.
This document describes a system to predict cricket scores and match winners in the Indian Premier League (IPL) using machine learning algorithms. The system has two parts: 1) predicting live scores using lasso regression based on parameters like overs bowled, runs scored etc, and 2) predicting the winning team using a random forest classifier based on parameters like toss winner and venue. The authors collected ball-by-ball data from Kaggle, performed data preprocessing, and divided the data into training and test sets. They developed a graphical user interface using Flask to allow users to input match details and get predictions. The system aims to improve fan engagement with the IPL by providing data-driven score and winner predictions.
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This document discusses using machine learning algorithms to analyze stock market data and predict future stock prices. It proposes collecting historical stock price and Twitter sentiment data and using recurrent neural networks and long short-term memory models to analyze the data and generate predictions and visualizations. The models would allow investors to make informed decisions about buying and selling stocks to potentially achieve returns on their investments.
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This document discusses using machine learning techniques to predict stock market prices. It proposes building a machine learning model that uses historical stock data to predict future stock prices. The model would go through preprocessing, processing, and regression analysis of the dataset to make predictions. Predicting stock market movements accurately is challenging, but this model aims to generate results using machine learning and deep learning algorithms on the dataset to help investors make trading decisions.
This document provides an overview of the Scorum platform, which aims to be a blockchain-powered sports media platform that rewards users. Key features include a blogging platform for sports content, an integrated statistics center, fantasy sports integration, and a commission-free betting exchange. The platform issues Scorum Coins and Scorum Power tokens to power its transparent reward system for authors and active members. The team has developed prototypes of the statistics center and fantasy sports platform. The goal is to finance ongoing development through an upcoming token crowdsale and establish Scorum as a cutting-edge sports media source.
The document discusses the future state of analytics in gaming devices and provides recommendations for harnessing analytics capabilities. It recommends understanding gamers through multidimensional data on behaviors, attitudes, demographics and social interactions. Platform providers should identify and engage social influencers who can help spread word of mouth. Combining behavioral data with attitudinal data from surveys can provide insights on gamer motivations and improve the gaming experience. Analytics can optimize business decisions around customer engagement, lifetime value, and marketing effectiveness.
This document describes a system to predict cricket match scores and outcomes using machine learning algorithms. It involves collecting historical match data, cleaning the data, splitting it into training and test sets, and using algorithms like Naive Bayes, SVM, K-Means, logistic regression, and decision trees. The proposed system architecture involves data collection, feature extraction, model training, and prediction. Performance is evaluated using metrics like accuracy, precision, and recall from a confusion matrix. The authors aim to use multiple algorithms and compare their relative performance to improve predictions.
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This document discusses using machine learning algorithms to analyze user behavior and predict future behavior based on browsing history data. The researchers collected browsing history data from 20 computers in a lab over various time periods. They extracted features from the data like URL, title, time of visit, and categorized browsing into types like social media, academic, shopping etc. They then trained machine learning models like SVM on 80% of the data and tested it on the remaining 20% to classify users and predict their future behavior. The goal is to develop personalized services and detect anomalies in behavior.
This document summarizes a research project that aims to analyze gamer behavior profiles by using clustering algorithms on game telemetry data. The project involves collecting game data, preprocessing the data, applying clustering algorithms like K-means, DBSCAN, K-medoids and Gaussian Mixture Models to segment players into groups based on their gameplay behavior. It will then evaluate the results using metrics like Davies-Bouldin Index, Silhouette Score and Sum of Squared Errors to validate the clusters. The goal is to help game developers better understand player tendencies to improve games and increase popularity.
Artificial Intelligence (AI) technologies are evolving fast and growing increasingly critical for a sporting organisation’s ability to:
- win games;
- improve coaches and players;
- manage internal operations; and
- grow, serve and retain their fans.
The imperative exists for sporting teams not to just adopt a singular AI technology but rather to have access to an arsenal of AI technologies that will improve their ability to generate and act on critical insights; whether it’s fan engagement, talent identification, pre-game preparation or in-game real-time facilitation.
This paper explores some of the specific AI applications being experimented across sporting codes, learnings from other sports and the current AI market. I also pose some ethical questions the sporting industry need to consider before introducing AI technologies into their codes.
This document describes a project to build a machine learning model for predicting laptop prices. It involves collecting a dataset of laptop specifications and prices, analyzing the data, developing predictive models using techniques like linear regression and random forests, and creating a web application using Flask that allows users to input laptop features and receive predicted prices. The project aims to help consumers and retailers make more informed purchasing and pricing decisions. Future work may include incorporating real-time data streams, enhancing the ML models, and obtaining user feedback to improve accuracy.
This document analyzes sports data from the Olympics from 1896 to 2016 to evaluate factors that influence countries' and athletes' performance over time. It uses data visualization and linear regression models to examine relationships between variables like total medals won and GDP or population. The analysis finds that visualization provides insights into the evolution of the Games and improvements in countries' performances. It aims to help countries identify mistakes and enhance future development.
Sports Analytics: Market Shares, Strategy, and Forecasts, Worldwide, 2015 to ...Shrikant Mandlik
The 2015 study has 472 pages, 177 tables and figures. Worldwide markets are poised to achieve significant growth as the cloud computing for utility infrastructure and the tablets and smart phone communications systems make training information more cogent and more available, remaking all sporting everywhere.
Information services will leverage automated process to leverage cloud computing: services The value of sports analytics is the predictive capabilities provided. The best sports teams are the ones using the power of real-time information to their advantage. What to measure? What real time information is the best? Can the players game the analytics systems?
Lets start with the story of Babe Ruth. The “Babe” used to come to every at bat with the desire to win the game. So early in the game, aware that at the end of the game it would fall on him to win the game, the “Babe” would deliberately strike out on pitches that he really could hit. Later in the game, the pitcher would remember the pitches that had gotten the “Babe” out and “Babe Ruth” could hit with ease, winning the game defying the statisticians.
So, Babe Ruth used sports analytics in the 1930’s in reverse, hoping to entice the pitcher to throw that very pitch he could hit in a tight situation later in the game. His very success illustrates that in sports analytics sophistication is needed. For sports analytics to track Babe Ruth, it would have been necessary to look at the pitches he could hit at the end of the game, not just everything that came at him. How sophisticated is that? You have to know your players to do good sports analytics.
Babe Ruth is at the center of one of the sad stories of sporting in Boston. The Boston Red Sox baseball team, in 2003, had not won a world series since Babe Ruth was sold to New York, the so called “Curse of the Bambino.” John Henry, a financial analytics wizard came along and purchased the Boston Red Sox along with other partners and he took the team to three world series using sports analytics as the dominant force for running the team and building fan enthusiasm. Sports become the model for predictive business decision making. Business has been reorganized among teams, inspired by sports. Analytics, developed by businesses are finding innovative use in sports, leading to models for
business to organize and manage teams.
Sports analytics market driving forces relate to the ability to improve winning percentages and decrease the cost of paying players. By implementing metrics functions that describe how to put together a winning team without a very high payroll, sports analytics provide a winning edge to team management. Analytics are used to figure out how a team can improve fan appeal.
Sports analytics are used for creating fantasy leagues, giving sports fantasy players access to statistics that enhances their play of the game. It is used to improve scouting, to detect new player unusual talent and evaluate
Prediction system report and results-Jay VoraJay Vora
This document provides a project report on predicting company stock prices using data mining techniques. It includes sections on requirements, UI design, architecture, datasets, data flow, algorithms used, and testing. The project aims to analyze twitter data and historical stock prices to predict future stock performance for companies in the IT sector. Data mining techniques like k-means clustering, TF-IDF, SVD, and vector space modeling are implemented and visualized to analyze sentiment and relevance of tweets to predict stock price changes.
Big Data Analytics & iot is taking the sports world by storm. Find out how advanced analytics can help you improve with BizViz Platform for Sports Analytics
THE REAL ESTATE MULTIPLE LISTING SERVICE SYSTEMThe Real Estate M.docxoreo10
THE REAL ESTATE MULTIPLE LISTING SERVICE SYSTEM
The Real Estate Multiple Listing Service system supplies information that local real estate agents use to help them sell houses to their customers. During the month, agents list houses for sale (listings) by contracting with homeowners. The agent works for a real estate office, which sends information on the listing to the multiple listing service. Therefore, any agent in the community can get information on the listing.
Information on a listing includes the address, year built, square feet, number of bedrooms, number of bathrooms, owner name, owner phone number, asking price, and status code. At any time during the month, an agent might directly request information on listings that match customer requirements, so the agent contacts the multiple listing service with the request.
Information is provided on the house, on the agent who listed the house, and on the real estate office for which the agent works. For example, an agent might want to call the listing agent to ask additional questions or call the homeowner directly to make an appointment to show the house. Twice each month (on the 15th and 30th), the multiple listing service produces a listing book that contains information on all listings. These books are sent to all of the real estate agents. Many real estate agents want the books (which are easier to flip through), so they are provided even though the information is often out of date. Sometimes agents and owners decide to change information about a listing, such as reducing the price, correcting previous information on the house, or indicating that the house is sold. The real estate office sends in these change requests to the multiple listing service when the agent asks the office to do so.
Chapter 7 – Pick 2 from the 4 below.
Using the event list and ERD for that system as a starting point, develop the following object-oriented
models:
1. Convert your ERD to a domain class diagram.
2. Develop a use case diagram.
3. Create a fully developed use case description or an activity diagram for each use case.
4. Develop a system sequence diagram for each use case.
Chapter 8 – Pick 1 from below
Consider the requirements of the multiple listing service system developed in Chapters 5, 6, and 7. Assume that you’re the project manager and that you work for a consulting firm hired by the multiple listing service to perform only the survey and analysis activities.
1. Assume that system users and owners have indicated a strong desire for a system that can be accessed “anytime, anywhere.” Discuss the implications of their desire for the system scope. Given the preferences of the system users and owners, should you prepare a table similar to Figure 8-2? Why or why not?
2. Discuss the implications of the anytime, anywhere requirement for the application deployment environment. What type(s) of hardware, network, and software architecture will be required to fulfill that requirement?
3. Investigate the a ...
The document outlines various use cases for a game rental website, including allowing users to create accounts, publish games for rent, process returns, make payments, and search for games. Tables provide details on the steps and flows for each use case. The site will allow users to rent unused video games from other users and make money through rental fees.
Stock Market Prediction using Alpha Vantage API and Machine Learning AlgorithmIRJET Journal
This document discusses using machine learning algorithms and the Alpha Vantage API to predict stock market prices based on historical stock data. Specifically, it proposes using a Naive Bayes machine learning algorithm to predict future stock prices for a company based on historical data obtained from the Alpha Vantage API, including opening price, closing price, highest price, lowest price, volume, and adjusted closing price. The goal is to develop a system that provides accurate stock market predictions to help investors and traders maximize their profits by informing decisions to buy or sell stocks.
The document proposes an automated framework for generating Tamil summaries of cricket matches from statistical scorecard data. The framework performs data analytics on scorecards, determines interesting aspects of matches, extracts key events, and generates customized summaries in Tamil. It evaluates summaries based on their similarity to human-written ones. The implementation summarizes 90 cricket matches between various countries. Results found many hidden patterns and determined factors influencing match interestingness. Summaries were 70-85% similar to human ones, showing the framework can effectively analyze matches and automatically generate concise Tamil summaries.
This document discusses a project to develop a machine learning model for predicting stock prices. It aims to help users make more informed investment decisions. The proposed system would allow users to view current and predicted stock prices and trends. It would also allow users to set up accounts to get notifications and bookmark favorite companies. The system would use machine learning, specifically LSTMs, to analyze past price data and predict future price movements. It would be developed using Python, Django, and other common web technologies. The goal is to build a model that can accurately forecast whether prices will increase or decrease.
17th Athens Big Data Meetup - 2nd Talk - Data Flow Building and Calculation P...Athens Big Data
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Speaker: Theodoros Michalareas (https://linkedin.com/in/theodorosmichalareas/)
Date: Tuesday, September 24, 2019
Event: https://meetup.com/Athens-Big-Data/events/264702584/
IRJET - Stock Market Analysis and Prediction using Deep LearningIRJET Journal
This document discusses using deep learning techniques to analyze stock market data and predict stock prices. It proposes a model that uses preprocessing, feature extraction, and machine learning algorithms like neural networks and K-nearest neighbors (KNN) classification to make predictions. The model is evaluated on stock market datasets containing attributes like date, price, volume. Feature extraction analyzes relationships between companies to better predict individual stock prices. Neural networks and KNN are used for prediction and KNN performed best when using single-company features alone. The goal is to help investors and fund managers make better investment decisions.
Comparative Analysis of Machine Learning Models for Cricket Score and Win Pre...IRJET Journal
This document presents a comparative analysis of machine learning models for cricket score and win prediction using a case study of linear regression, random forest, neural network, and elastic net algorithms. The models take into consideration factors like the teams playing, location of the match, current runs/wickets, and runs/wickets in the last 5 overs to predict the score and winner. Random forest performed the best with the highest R2 value and lowest mean squared error and mean absolute error. The random forest algorithm is also used for the live win prediction system.
The development and usage of ICT technologies is increasing every day. Nowadays, ICT technologies are being used in
almost every industry and lately, especially rapidly in sports. Information related to sport events and sport participants
are recorded in a database, which thus enables computer analysis. This paper presents information system Basketball
Coach Assistant. The paper presents current version of information system and describes the possibilities of the
information system, starting with the architecture up to the individual parts of the information system. The paper also
explains a display of a custom menu and user-defined player efficiency index.
Survey Paper on Stock Prediction Using Machine Learning AlgorithmsIRJET Journal
This document discusses various machine learning algorithms that have been used for stock market prediction, including CNN, ARIMA, LSTM, random forests, and support vector machines. It provides a literature review of past research applying these algorithms to predict stock prices using historical data. The document concludes that LSTM and ARIMA models generally provide the best predictions based on evaluating various algorithms on large datasets of historical stock market data.
Bitcoin Price Prediction and ForecastingIRJET Journal
This document discusses predicting bitcoin price movements using machine learning techniques. It proposes using LSTM neural networks combined with sentiment analysis of tweets and Reddit posts to analyze factors influencing bitcoin prices. The methodology involves data collection, preprocessing, sentiment analysis to classify tweets as positive, neutral or negative, and training LSTM models on historic price data. The trained models would allow investors to predict bitcoin price changes and limit potential losses. Evaluation of the models found LSTM to have better performance than other techniques for this volatile cryptocurrency data. With further expansion and testing of models on more diverse data, this approach aims to more accurately forecast bitcoin and other cryptocurrency prices.
DATA SCIENCE APPROACH TO STOCK MARKET ANALYSISIRJET Journal
This document discusses using data science techniques to analyze stock market data. It begins by defining stock analysis as a tool used by investors and traders to research historical and recent data to make informed investment decisions. It then discusses two strategies for stock market prediction - using technical indicators to predict stock trends and using a Hidden Markov Model which takes a probabilistic approach. The document provides an introduction to understanding the stock market, how it functions as both a primary and secondary market, and some common technical indicators and metrics used in stock analysis like stochastic oscillator and momentum index.
IRJET- Stock Market Prediction using Financial News ArticlesIRJET Journal
This document presents a system for predicting stock prices using financial news articles and machine learning. It first extracts data from trusted news sources and cleans it using natural language processing. Sentiment analysis determines if the news is positive or negative. This polarity is input to a machine learning model, which uses an algorithm like linear regression to predict if a stock's price will increase in the next 10 minutes. The system was tested on a dataset divided into 70% for training and 30% for testing. Accuracy, specificity and precision metrics showed the system could successfully predict stock price movements based on analyzing financial news sentiment.
‘Six Sigma Technique’ A Journey Through its Implementationijtsrd
The manufacturing industries all over the world are facing tough challenges for growth, development and sustainability in today’s competitive environment. They have to achieve apex position by adapting with the global competitive environment by delivering goods and services at low cost, prime quality and better price to increase wealth and consumer satisfaction. Cost Management ensures profit, growth and sustainability of the business with implementation of Continuous Improvement Technique like Six Sigma. This leads to optimize Business performance. The method drives for customer satisfaction, low variation, reduction in waste and cycle time resulting into a competitive advantage over other industries which did not implement it. The main objective of this paper ‘Six Sigma Technique A Journey Through Its Implementation’ is to conceptualize the effectiveness of Six Sigma Technique through the journey of its implementation. Aditi Sunilkumar Ghosalkar "‘Six Sigma Technique’: A Journey Through its Implementation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64546.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64546/‘six-sigma-technique’-a-journey-through-its-implementation/aditi-sunilkumar-ghosalkar
Edge Computing in Space Enhancing Data Processing and Communication for Space...ijtsrd
Edge computing, a paradigm that involves processing data closer to its source, has gained significant attention for its potential to revolutionize data processing and communication in space missions. With the increasing complexity and data volume generated by modern space missions, traditional centralized computing approaches face challenges related to latency, bandwidth, and security. Edge computing in space, involving on board processing and analysis of data, offers promising solutions to these challenges. This paper explores the concept of edge computing in space, its benefits, applications, and future prospects in enhancing space missions. Manish Verma "Edge Computing in Space: Enhancing Data Processing and Communication for Space Missions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64541.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/64541/edge-computing-in-space-enhancing-data-processing-and-communication-for-space-missions/manish-verma
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- manage internal operations; and
- grow, serve and retain their fans.
The imperative exists for sporting teams not to just adopt a singular AI technology but rather to have access to an arsenal of AI technologies that will improve their ability to generate and act on critical insights; whether it’s fan engagement, talent identification, pre-game preparation or in-game real-time facilitation.
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This document analyzes sports data from the Olympics from 1896 to 2016 to evaluate factors that influence countries' and athletes' performance over time. It uses data visualization and linear regression models to examine relationships between variables like total medals won and GDP or population. The analysis finds that visualization provides insights into the evolution of the Games and improvements in countries' performances. It aims to help countries identify mistakes and enhance future development.
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The 2015 study has 472 pages, 177 tables and figures. Worldwide markets are poised to achieve significant growth as the cloud computing for utility infrastructure and the tablets and smart phone communications systems make training information more cogent and more available, remaking all sporting everywhere.
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Lets start with the story of Babe Ruth. The “Babe” used to come to every at bat with the desire to win the game. So early in the game, aware that at the end of the game it would fall on him to win the game, the “Babe” would deliberately strike out on pitches that he really could hit. Later in the game, the pitcher would remember the pitches that had gotten the “Babe” out and “Babe Ruth” could hit with ease, winning the game defying the statisticians.
So, Babe Ruth used sports analytics in the 1930’s in reverse, hoping to entice the pitcher to throw that very pitch he could hit in a tight situation later in the game. His very success illustrates that in sports analytics sophistication is needed. For sports analytics to track Babe Ruth, it would have been necessary to look at the pitches he could hit at the end of the game, not just everything that came at him. How sophisticated is that? You have to know your players to do good sports analytics.
Babe Ruth is at the center of one of the sad stories of sporting in Boston. The Boston Red Sox baseball team, in 2003, had not won a world series since Babe Ruth was sold to New York, the so called “Curse of the Bambino.” John Henry, a financial analytics wizard came along and purchased the Boston Red Sox along with other partners and he took the team to three world series using sports analytics as the dominant force for running the team and building fan enthusiasm. Sports become the model for predictive business decision making. Business has been reorganized among teams, inspired by sports. Analytics, developed by businesses are finding innovative use in sports, leading to models for
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Sports analytics market driving forces relate to the ability to improve winning percentages and decrease the cost of paying players. By implementing metrics functions that describe how to put together a winning team without a very high payroll, sports analytics provide a winning edge to team management. Analytics are used to figure out how a team can improve fan appeal.
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Chapter 7 – Pick 2 from the 4 below.
Using the event list and ERD for that system as a starting point, develop the following object-oriented
models:
1. Convert your ERD to a domain class diagram.
2. Develop a use case diagram.
3. Create a fully developed use case description or an activity diagram for each use case.
4. Develop a system sequence diagram for each use case.
Chapter 8 – Pick 1 from below
Consider the requirements of the multiple listing service system developed in Chapters 5, 6, and 7. Assume that you’re the project manager and that you work for a consulting firm hired by the multiple listing service to perform only the survey and analysis activities.
1. Assume that system users and owners have indicated a strong desire for a system that can be accessed “anytime, anywhere.” Discuss the implications of their desire for the system scope. Given the preferences of the system users and owners, should you prepare a table similar to Figure 8-2? Why or why not?
2. Discuss the implications of the anytime, anywhere requirement for the application deployment environment. What type(s) of hardware, network, and software architecture will be required to fulfill that requirement?
3. Investigate the a ...
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This document discusses a project to develop a machine learning model for predicting stock prices. It aims to help users make more informed investment decisions. The proposed system would allow users to view current and predicted stock prices and trends. It would also allow users to set up accounts to get notifications and bookmark favorite companies. The system would use machine learning, specifically LSTMs, to analyze past price data and predict future price movements. It would be developed using Python, Django, and other common web technologies. The goal is to build a model that can accurately forecast whether prices will increase or decrease.
17th Athens Big Data Meetup - 2nd Talk - Data Flow Building and Calculation P...Athens Big Data
Title: Data Flow Building and Calculation Pipelines via PySpark and ML Modeling via Python
Speaker: Theodoros Michalareas (https://linkedin.com/in/theodorosmichalareas/)
Date: Tuesday, September 24, 2019
Event: https://meetup.com/Athens-Big-Data/events/264702584/
IRJET - Stock Market Analysis and Prediction using Deep LearningIRJET Journal
This document discusses using deep learning techniques to analyze stock market data and predict stock prices. It proposes a model that uses preprocessing, feature extraction, and machine learning algorithms like neural networks and K-nearest neighbors (KNN) classification to make predictions. The model is evaluated on stock market datasets containing attributes like date, price, volume. Feature extraction analyzes relationships between companies to better predict individual stock prices. Neural networks and KNN are used for prediction and KNN performed best when using single-company features alone. The goal is to help investors and fund managers make better investment decisions.
Comparative Analysis of Machine Learning Models for Cricket Score and Win Pre...IRJET Journal
This document presents a comparative analysis of machine learning models for cricket score and win prediction using a case study of linear regression, random forest, neural network, and elastic net algorithms. The models take into consideration factors like the teams playing, location of the match, current runs/wickets, and runs/wickets in the last 5 overs to predict the score and winner. Random forest performed the best with the highest R2 value and lowest mean squared error and mean absolute error. The random forest algorithm is also used for the live win prediction system.
The development and usage of ICT technologies is increasing every day. Nowadays, ICT technologies are being used in
almost every industry and lately, especially rapidly in sports. Information related to sport events and sport participants
are recorded in a database, which thus enables computer analysis. This paper presents information system Basketball
Coach Assistant. The paper presents current version of information system and describes the possibilities of the
information system, starting with the architecture up to the individual parts of the information system. The paper also
explains a display of a custom menu and user-defined player efficiency index.
Survey Paper on Stock Prediction Using Machine Learning AlgorithmsIRJET Journal
This document discusses various machine learning algorithms that have been used for stock market prediction, including CNN, ARIMA, LSTM, random forests, and support vector machines. It provides a literature review of past research applying these algorithms to predict stock prices using historical data. The document concludes that LSTM and ARIMA models generally provide the best predictions based on evaluating various algorithms on large datasets of historical stock market data.
Bitcoin Price Prediction and ForecastingIRJET Journal
This document discusses predicting bitcoin price movements using machine learning techniques. It proposes using LSTM neural networks combined with sentiment analysis of tweets and Reddit posts to analyze factors influencing bitcoin prices. The methodology involves data collection, preprocessing, sentiment analysis to classify tweets as positive, neutral or negative, and training LSTM models on historic price data. The trained models would allow investors to predict bitcoin price changes and limit potential losses. Evaluation of the models found LSTM to have better performance than other techniques for this volatile cryptocurrency data. With further expansion and testing of models on more diverse data, this approach aims to more accurately forecast bitcoin and other cryptocurrency prices.
DATA SCIENCE APPROACH TO STOCK MARKET ANALYSISIRJET Journal
This document discusses using data science techniques to analyze stock market data. It begins by defining stock analysis as a tool used by investors and traders to research historical and recent data to make informed investment decisions. It then discusses two strategies for stock market prediction - using technical indicators to predict stock trends and using a Hidden Markov Model which takes a probabilistic approach. The document provides an introduction to understanding the stock market, how it functions as both a primary and secondary market, and some common technical indicators and metrics used in stock analysis like stochastic oscillator and momentum index.
IRJET- Stock Market Prediction using Financial News ArticlesIRJET Journal
This document presents a system for predicting stock prices using financial news articles and machine learning. It first extracts data from trusted news sources and cleans it using natural language processing. Sentiment analysis determines if the news is positive or negative. This polarity is input to a machine learning model, which uses an algorithm like linear regression to predict if a stock's price will increase in the next 10 minutes. The system was tested on a dataset divided into 70% for training and 30% for testing. Accuracy, specificity and precision metrics showed the system could successfully predict stock price movements based on analyzing financial news sentiment.
Similar to Design and Development of Return Analysis System Between Purchase and Rental of Game Equipment (20)
‘Six Sigma Technique’ A Journey Through its Implementationijtsrd
The manufacturing industries all over the world are facing tough challenges for growth, development and sustainability in today’s competitive environment. They have to achieve apex position by adapting with the global competitive environment by delivering goods and services at low cost, prime quality and better price to increase wealth and consumer satisfaction. Cost Management ensures profit, growth and sustainability of the business with implementation of Continuous Improvement Technique like Six Sigma. This leads to optimize Business performance. The method drives for customer satisfaction, low variation, reduction in waste and cycle time resulting into a competitive advantage over other industries which did not implement it. The main objective of this paper ‘Six Sigma Technique A Journey Through Its Implementation’ is to conceptualize the effectiveness of Six Sigma Technique through the journey of its implementation. Aditi Sunilkumar Ghosalkar "‘Six Sigma Technique’: A Journey Through its Implementation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64546.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64546/‘six-sigma-technique’-a-journey-through-its-implementation/aditi-sunilkumar-ghosalkar
Edge Computing in Space Enhancing Data Processing and Communication for Space...ijtsrd
Edge computing, a paradigm that involves processing data closer to its source, has gained significant attention for its potential to revolutionize data processing and communication in space missions. With the increasing complexity and data volume generated by modern space missions, traditional centralized computing approaches face challenges related to latency, bandwidth, and security. Edge computing in space, involving on board processing and analysis of data, offers promising solutions to these challenges. This paper explores the concept of edge computing in space, its benefits, applications, and future prospects in enhancing space missions. Manish Verma "Edge Computing in Space: Enhancing Data Processing and Communication for Space Missions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64541.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/64541/edge-computing-in-space-enhancing-data-processing-and-communication-for-space-missions/manish-verma
Dynamics of Communal Politics in 21st Century India Challenges and Prospectsijtsrd
Communal politics in India has evolved through centuries, weaving a complex tapestry shaped by historical legacies, colonial influences, and contemporary socio political transformations. This research comprehensively examines the dynamics of communal politics in 21st century India, emphasizing its historical roots, socio political dynamics, economic implications, challenges, and prospects for mitigation. The historical perspective unravels the intricate interplay of religious identities and power dynamics from ancient civilizations to the impact of colonial rule, providing insights into the evolution of communalism. The socio political dynamics section delves into the contemporary manifestations, exploring the roles of identity politics, socio economic disparities, and globalization. The economic implications section highlights how communal politics intersects with economic issues, perpetuating disparities and influencing resource allocation. Challenges posed by communal politics are scrutinized, revealing multifaceted issues ranging from social fragmentation to threats against democratic values. The prospects for mitigation present a multifaceted approach, incorporating policy interventions, community engagement, and educational initiatives. The paper conducts a comparative analysis with international examples, identifying common patterns such as identity politics and economic disparities. It also examines unique challenges, emphasizing Indias diverse religious landscape, historical legacy, and secular framework. Lessons for effective strategies are drawn from international experiences, offering insights into inclusive policies, interfaith dialogue, media regulation, and global cooperation. By scrutinizing historical epochs, contemporary dynamics, economic implications, and international comparisons, this research provides a comprehensive understanding of communal politics in India. The proposed strategies for mitigation underscore the importance of a holistic approach to foster social harmony, inclusivity, and democratic values. Rose Hossain "Dynamics of Communal Politics in 21st Century India: Challenges and Prospects" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64528.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/history/64528/dynamics-of-communal-politics-in-21st-century-india-challenges-and-prospects/rose-hossain
Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...ijtsrd
Background and Objective Telehealth has become a well known tool for the delivery of health care in Saudi Arabia, and the perspective and knowledge of healthcare providers are influential in the implementation, adoption and advancement of the method. This systematic review was conducted to examine the current literature base regarding telehealth and the related healthcare professional perspective and knowledge in the Kingdom of Saudi Arabia. Materials and Methods This systematic review was conducted by searching 7 databases including, MEDLINE, CINHAL, Web of Science, Scopus, PubMed, PsycINFO, and ProQuest Central. Studies on healthcare practitioners telehealth knowledge and perspectives published in English in Saudi Arabia from 2000 to 2023 were included. Boland directed this comprehensive review. The researchers examined each connected study using the AXIS tool, which evaluates cross sectional systematic reviews. Narrative synthesis was used to summarise and convey the data. Results Out of 1840 search results, 10 studies were included. Positive outlook and limited knowledge among providers were seen across trials. Healthcare professionals like telehealth for its ability to improve quality, access, and delivery, save time and money, and be successful. Age, gender, occupation, and work experience also affect health workers knowledge. In Saudi Arabia, healthcare professionals face inadequate expert assistance, patient privacy, internet connection concerns, lack of training courses, lack of telehealth understanding, and high costs while performing telemedicine. Conclusions Healthcare practitioners telehealth perceptions and knowledge were examined in this systematic study. Its collection of concerned experts different personal attitudes and expertise would help enhance telehealths implementation in Saudi Arabia, develop its healthcare delivery alternative, and eliminate frequent problems. Badriah Mousa I Mulayhi | Dr. Jomin George | Judy Jenkins "Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in Saudi Arabia: A Systematic Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64535.pdf Paper Url: https://www.ijtsrd.com/medicine/other/64535/assess-perspective-and-knowledge-of-healthcare-providers-towards-elehealth-in-saudi-arabia-a-systematic-review/badriah-mousa-i-mulayhi
The Impact of Digital Media on the Decentralization of Power and the Erosion ...ijtsrd
The impact of digital media on the distribution of power and the weakening of traditional gatekeepers has gained considerable attention in recent years. The adoption of digital technologies and the internet has resulted in declining influence and power for traditional gatekeepers such as publishing houses and news organizations. Simultaneously, digital media has facilitated the emergence of new voices and players in the media industry. Digital medias impact on power decentralization and gatekeeper erosion is visible in several ways. One significant aspect is the democratization of information, which enables anyone with an internet connection to publish and share content globally, leading to citizen journalism and bypassing traditional gatekeepers. Another aspect is the disruption of conventional media industry business models, as traditional organizations struggle to adjust to the decrease in advertising revenue and the rise of digital platforms. Alternative business models, such as subscription models and crowdfunding, have become more prevalent, leading to the emergence of new players. Overall, the impact of digital media on the distribution of power and the weakening of traditional gatekeepers has brought about significant changes in the media landscape and the way information is shared. Further research is required to fully comprehend the implications of these changes and their impact on society. Dr. Kusum Lata "The Impact of Digital Media on the Decentralization of Power and the Erosion of Traditional Gatekeepers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64544.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64544/the-impact-of-digital-media-on-the-decentralization-of-power-and-the-erosion-of-traditional-gatekeepers/dr-kusum-lata
Online Voices, Offline Impact Ambedkars Ideals and Socio Political Inclusion ...ijtsrd
This research investigates the nexus between online discussions on Dr. B.R. Ambedkars ideals and their impact on social inclusion among college students in Gurugram, Haryana. Surveying 240 students from 12 government colleges, findings indicate that 65 actively engage in online discussions, with 80 demonstrating moderate to high awareness of Ambedkars ideals. Statistically significant correlations reveal that higher online engagement correlates with increased awareness p 0.05 and perceived social inclusion. Variations across colleges and a notable effect of college type on perceived social inclusion highlight the influence of contextual factors. Furthermore, the intersectional analysis underscores nuanced differences based on gender, caste, and socio economic status. Dr. Kusum Lata "Online Voices, Offline Impact: Ambedkar's Ideals and Socio-Political Inclusion - A Study of Gurugram District" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64543.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64543/online-voices-offline-impact-ambedkars-ideals-and-sociopolitical-inclusion--a-study-of-gurugram-district/dr-kusum-lata
Problems and Challenges of Agro Entreprenurship A Studyijtsrd
Noting calls for contextualizing Agro entrepreneurs problems and challenges of the agro entrepreneurs and for greater attention to the Role of entrepreneurs in agro entrepreneurship research, we conduct a systematic literature review of extent research in agriculture entrepreneurship to overcome the study objectives of complications of agro entrepreneurs through various factors, Development of agriculture products is a key factor for the overall economic growth of agro entrepreneurs Agro Entrepreneurs produces firsthand large scale employment, utilizes the labor and natural resources, This research outlines the problems of Weather and Soil Erosions, Market price fluctuation, stimulates labor cost problems, reduces concentration of Price volatility, Dependency on Intermediaries, induces Limited Bargaining Power, and Storage and Transportation Costs. This paper mainly devoted to highlight Problems and challenges faced for the sustainable of Agro Entrepreneurs in India. Vinay Prasad B "Problems and Challenges of Agro Entreprenurship - A Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64540.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64540/problems-and-challenges-of-agro-entreprenurship--a-study/vinay-prasad-b
Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...ijtsrd
Disclosure is a process through which a business enterprise communicates with external parties. A corporate disclosure is communication of financial and non financial information of the activities of a business enterprise to the interested entities. Corporate disclosure is done through publishing annual reports. So corporate disclosure through annual reports plays a vital role in the life of all the companies and provides valuable information to investors. The basic objectives of corporate disclosure is to give a true and fair view of companies to the parties related either directly or indirectly like owner, government, creditors, shareholders etc. in the companies act, provisions have been made about mandatory and voluntary disclosure. The IT sector in India is rapidly growing, the trend to invest in the IT sector is rising and employment opportunities in IT sectors are also increasing. Therefore the IT sector is expected to have fair, full and adequate disclosure of all information. Unfair and incomplete disclosure may adversely affect the entire economy. A research study on disclosure practices of IT companies could play an important role in this regard. Hence, the present research study has been done to study and review comparative analysis of total corporate disclosure of selected IT companies of India and to put forward overall findings and suggestions with a view to increase disclosure score of these companies. The researcher hopes that the present research study will be helpful to all selected Companies for improving level of corporate disclosure through annual reports as well as the government, creditors, investors, all business organizations and upcoming researcher for comparative analyses of level of corporate disclosure with special reference to selected IT companies. Dr. Vaibhavi D. Thaker "Comparative Analysis of Total Corporate Disclosure of Selected IT Companies of India" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64539.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64539/comparative-analysis-of-total-corporate-disclosure-of-selected-it-companies-of-india/dr-vaibhavi-d-thaker
The Impact of Educational Background and Professional Training on Human Right...ijtsrd
This study investigated the impact of educational background and professional training on human rights awareness among secondary school teachers in the Marathwada region of Maharashtra, India. The key findings reveal that higher levels of education, particularly a master’s degree, and fields of study related to education, humanities, or social sciences are associated with greater human rights awareness among teachers. Additionally, both pre service teacher training and in service professional development programs focused on human rights education significantly enhance teacher’s knowledge, skills, and competencies in promoting human rights principles in their classrooms. Baig Ameer Bee Mirza Abdul Aziz | Dr. Syed Azaz Ali Amjad Ali "The Impact of Educational Background and Professional Training on Human Rights Awareness among Secondary School Teachers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64529.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64529/the-impact-of-educational-background-and-professional-training-on-human-rights-awareness-among-secondary-school-teachers/baig-ameer-bee-mirza-abdul-aziz
A Study on the Effective Teaching Learning Process in English Curriculum at t...ijtsrd
“One Language sets you in a corridor for life. Two languages open every door along the way” Frank Smith English as a foreign language or as a second language has been ruling in India since the period of Lord Macaulay. But the question is how much we teach or learn English properly in our culture. Is there any scope to use English as a language rather than a subject How much we learn or teach English without any interference of mother language specially in the classroom teaching learning scenario in West Bengal By considering all these issues the researcher has attempted in this article to focus on the effective teaching learning process comparing to other traditional strategies in the field of English curriculum at the secondary level to investigate whether they fulfill the present teaching learning requirements or not by examining the validity of the present curriculum of English. The purpose of this study is to focus on the effectiveness of the systematic, scientific, sequential and logical transaction of the course between the teachers and the learners in the perspective of the 5Es programme that is engage, explore, explain, extend and evaluate. Sanchali Mondal | Santinath Sarkar "A Study on the Effective Teaching Learning Process in English Curriculum at the Secondary Level of West Bengal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd62412.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/62412/a-study-on-the-effective-teaching-learning-process-in-english-curriculum-at-the-secondary-level-of-west-bengal/sanchali-mondal
The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...ijtsrd
This paper reports on a study which was conducted to investigate the role of mentoring and its influence on the effectiveness of the teaching of Physics in secondary schools in the South West Region of Cameroon. The study adopted the convergent parallel mixed methods design, focusing on respondents in secondary schools in the South West Region of Cameroon. Both quantitative and qualitative data were collected, analysed separately, and the results were compared to see if the findings confirm or disconfirm each other. The quantitative analysis found that majority of the respondents 72 of Physics teachers affirmed that they had more experienced colleagues as mentors to help build their confidence, improve their teaching, and help them improve their effectiveness and efficiency in guiding learners’ achievements. Only 28 of the respondents disagreed with these statements. With majority respondents 72 agreeing with the statements, it implies that in most secondary schools, experienced Physics teachers act as mentors to build teachers’ confidence in teaching and improving students’ learning. The interview qualitative data analysis summarized how secondary school Principals use meetings with mentors and mentees to promote mentorship in the school milieu. This has helped strengthen teachers’ classroom practices in secondary schools in the South West Region of Cameroon. With the results confirming each other, the study recommends that mentoring should focus on helping teachers employ social interactions and instructional practices feedback and clarity in teaching that have direct measurable impact on students’ learning achievements. Andrew Ngeim Sumba | Frederick Ebot Ashu | Peter Agborbechem Tambi "The Role of Mentoring and Its Influence on the Effectiveness of the Teaching of Physics in Secondary Schools in the South West Region of Cameroon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64524.pdf Paper Url: https://www.ijtsrd.com/management/management-development/64524/the-role-of-mentoring-and-its-influence-on-the-effectiveness-of-the-teaching-of-physics-in-secondary-schools-in-the-south-west-region-of-cameroon/andrew-ngeim-sumba
Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...ijtsrd
This study primarily focuses on the design of a high side buck converter using an Arduino microcontroller. The converter is specifically intended for use in DC DC applications, particularly in standalone solar PV systems where the PV output voltage exceeds the load or battery voltage. To evaluate the performance of the converter, simulation experiments are conducted using Proteus Software. These simulations provide insights into the input and output voltages, currents, powers, and efficiency under different state of charge SoC conditions of a 12V,70Ah rechargeable lead acid battery. Additionally, the hardware design of the converter is implemented, and practical data is collected through operation, monitoring, and recording. By comparing the simulation results with the practical results, the efficiency and performance of the designed converter are assessed. The findings indicate that while the buck converter is suitable for practical use in standalone PV systems, its efficiency is compromised due to a lower output current. Chan Myae Aung | Dr. Ei Mon "Design Simulation and Hardware Construction of an Arduino-Microcontroller Based DC-DC High-Side Buck Converter for Standalone PV System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64518.pdf Paper Url: https://www.ijtsrd.com/engineering/mechanical-engineering/64518/design-simulation-and-hardware-construction-of-an-arduinomicrocontroller-based-dcdc-highside-buck-converter-for-standalone-pv-system/chan-myae-aung
Sustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadikuijtsrd
Energy becomes sustainable if it meets the needs of the present without compromising the ability of future generations to meet their own needs. Some of the definitions of sustainable energy include the considerations of environmental aspects such as greenhouse gas emissions, social, and economic aspects such as energy poverty. Generally far more sustainable than fossil fuel are renewable energy sources such as wind, hydroelectric power, solar, and geothermal energy sources. Worthy of note is that some renewable energy projects, like the clearing of forests to produce biofuels, can cause severe environmental damage. The sustainability of nuclear power which is a low carbon source is highly debated because of concerns about radioactive waste, nuclear proliferation, and accidents. The switching from coal to natural gas has environmental benefits, including a lower climate impact, but could lead to delay in switching to more sustainable options. “Carbon capture and storage” can be built into power plants to remove the carbon dioxide CO2 emissions, but this technology is expensive and has rarely been implemented. Leading non renewable energy sources around the world is fossil fuels, coal, petroleum, and natural gas. Nuclear energy is usually considered another non renewable energy source, although nuclear energy itself is a renewable energy source, but the material used in nuclear power plants is not. The paper addresses the issue of sustainable energy, its attendant benefits to the future generation, and humanity in general. Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadiku "Sustainable Energy" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64534.pdf Paper Url: https://www.ijtsrd.com/engineering/electrical-engineering/64534/sustainable-energy/paul-a-adekunte
Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...ijtsrd
This paper aims to outline the executive regulations, survey standards, and specifications required for the implementation of the Sudan Survey Act, and for regulating and organizing all surveying work activities in Sudan. The act has been discussed for more than 5 years. The Land Survey Act was initiated by the Sudan Survey Authority and all official legislations were headed by the Sudan Ministry of Justice till it was issued in 2022. The paper presents conceptual guidelines to be used for the Survey Act implementation and to regulate the survey work practice, standardizing the field surveys, processing, quality control, procedures, and the processes related to survey work carried out by the stakeholders and relevant authorities in Sudan. The conceptual guidelines are meant to improve the quality and harmonization of geospatial data and to aid decision making processes as well as geospatial information systems. The established comprehensive executive regulations will govern and regulate the implementation of the Sudan Survey Geomatics Act in all surveying and mapping practices undertaken by the Sudan Survey Authority SSA and state local survey departments for public or private sector organizations. The targeted standards and specifications include the reference frame, projection, coordinate systems, and the guidelines and specifications that must be followed in the field of survey work, processes, and mapping products. In the last few decades, there has been a growing awareness of the importance of geomatics activities and measurements on the Earths surface in space and time, together with observing and mapping the changes. In such cases, data must be captured promptly, standardized, and obtained with more accuracy and specified in much detail. The paper will also highlight the current situation in Sudan, the degree to which survey standards are used, the problems encountered, and the errors that arise from not using the standards and survey specifications. Kamal A. A. Sami "Concepts for Sudan Survey Act Implementations - Executive Regulations and Standards" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63484.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63484/concepts-for-sudan-survey-act-implementations--executive-regulations-and-standards/kamal-a-a-sami
Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...ijtsrd
The discussions between ellipsoid and geoid have invoked many researchers during the recent decades, especially during the GNSS technology era, which had witnessed a great deal of development but still geoid undulation requires more investigations. To figure out a solution for Sudans local geoid, this research has tried to intake the possibility of determining the geoid model by following two approaches, gravimetric and geometrical geoid model determination, by making use of GNSS leveling benchmarks at Khartoum state. The Benchmarks are well distributed in the study area, in which, the horizontal coordinates and the height above the ellipsoid have been observed by GNSS while orthometric heights were carried out using precise leveling. The Global Geopotential Model GGM represented in EGM2008 has been exploited to figure out the geoid undulation at the benchmarks in the study area. This is followed by a fitting process, that has been done to suit the geoid undulation data which has been computed using GNSS leveling data and geoid undulation inspired by the EGM2008. Two geoid surfaces were created after the fitting process to ensure that they are identical and both of them could be counted for getting the same geoid undulation with an acceptable accuracy. In this respect, statistical operation played an important role in ensuring the consistency and integrity of the model by applying cross validation techniques splitting the data into training and testing datasets for building the geoid model and testing its eligibility. The geometrical solution for geoid undulation computation has been utilized by applying straightforward equations that facilitate the calculation of the geoid undulation directly through applying statistical techniques for the GNSS leveling data of the study area to get the common equation parameters values that could be utilized to calculate geoid undulation of any position in the study area within the claimed accuracy. Both systems were checked and proved eligible to be used within the study area with acceptable accuracy which may contribute to solving the geoid undulation problem in the Khartoum area, and be further generalized to determine the geoid model over the entire country, and this could be considered in the future, for regional and continental geoid model. Ahmed M. A. Mohammed. | Kamal A. A. Sami "Towards the Implementation of the Sudan Interpolated Geoid Model (Khartoum State Case Study)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63483.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63483/towards-the-implementation-of-the-sudan-interpolated-geoid-model-khartoum-state-case-study/ahmed-m-a-mohammed
Activating Geospatial Information for Sudans Sustainable Investment Mapijtsrd
Sudan is witnessing an acceleration in the processes of development and transformation in the performance of government institutions to raise the productivity and investment efficiency of the government sector. The development plans and investment opportunities have focused on achieving national goals in various sectors. This paper aims to illuminate the path to the future and provide geospatial data and information to develop the investment climate and environment for all sized businesses, and to bridge the development gap between the Sudan states. The Sudan Survey Authority SSA is the main advisor to the Sudan Government in conducting surveying, mappings, designing, and developing systems related to geospatial data and information. In recent years, SSA made a strategic partnership with the Ministry of Investment to activate Geospatial Information for Sudans Sustainable Investment and in particular, for the preparation and implementation of the Sudan investment map, based on the directives and objectives of the Ministry of Investment MI in Sudan. This paper comes within the framework of activating the efforts of the Ministry of Investment to develop technical investment services by applying techniques adopted by the Ministry and its strategic partners for advancing investment processes in the country. Kamal A. A. Sami "Activating Geospatial Information for Sudan's Sustainable Investment Map" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63482.pdf Paper Url: https://www.ijtsrd.com/engineering/information-technology/63482/activating-geospatial-information-for-sudans-sustainable-investment-map/kamal-a-a-sami
Educational Unity Embracing Diversity for a Stronger Societyijtsrd
In a rapidly changing global landscape, the importance of education as a unifying force cannot be overstated. This paper explores the crucial role of educational unity in fostering a stronger and more inclusive society through the embrace of diversity. By examining the benefits of diverse learning environments, the paper aims to highlight the positive impact on societal strength. The discussion encompasses various dimensions, from curriculum design to classroom dynamics, and emphasizes the need for educational institutions to become catalysts for unity in diversity. It highlights the need for a paradigm shift in educational policies, curricula, and pedagogical approaches to ensure that they are reflective of the diverse fabric of society. This paper also addresses the challenges associated with implementing inclusive educational practices and offers practical strategies for overcoming barriers. It advocates for collaborative efforts between educational institutions, policymakers, and communities to create a supportive ecosystem that promotes diversity and unity. Mr. Amit Adhikari | Madhumita Teli | Gopal Adhikari "Educational Unity: Embracing Diversity for a Stronger Society" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64525.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64525/educational-unity-embracing-diversity-for-a-stronger-society/mr-amit-adhikari
Integration of Indian Indigenous Knowledge System in Management Prospects and...ijtsrd
The diversity of indigenous knowledge systems in India is vast and can vary significantly between different communities and regions. Preserving and respecting these knowledge systems is crucial for maintaining cultural heritage, promoting sustainable practices, and fostering cross cultural understanding. In this paper, an overview of the prospects and challenges associated with incorporating Indian indigenous knowledge into management is explored. It is found that IIKS helps in management in many areas like sustainable development, tourism, food security, natural resource management, cultural preservation and innovation, etc. However, IIKS integration with management faces some challenges in the form of a lack of documentation, cultural sensitivity, language barriers legal framework, etc. Savita Lathwal "Integration of Indian Indigenous Knowledge System in Management: Prospects and Challenges" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63500.pdf Paper Url: https://www.ijtsrd.com/management/accounting-and-finance/63500/integration-of-indian-indigenous-knowledge-system-in-management-prospects-and-challenges/savita-lathwal
DeepMask Transforming Face Mask Identification for Better Pandemic Control in...ijtsrd
The COVID 19 pandemic has highlighted the crucial need of preventive measures, with widespread use of face masks being a key method for slowing the viruss spread. This research investigates face mask identification using deep learning as a technological solution to be reducing the risk of coronavirus transmission. The proposed method uses state of the art convolutional neural networks CNNs and transfer learning to automatically recognize persons who are not wearing masks in a variety of circumstances. We discuss how this strategy improves public health and safety by providing an efficient manner of enforcing mask wearing standards. The report also discusses the obstacles, ethical concerns, and prospective applications of face mask detection systems in the ongoing fight against the pandemic. Dilip Kumar Sharma | Aaditya Yadav "DeepMask: Transforming Face Mask Identification for Better Pandemic Control in the COVID-19 Era" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64522.pdf Paper Url: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/64522/deepmask-transforming-face-mask-identification-for-better-pandemic-control-in-the-covid19-era/dilip-kumar-sharma
Streamlining Data Collection eCRF Design and Machine Learningijtsrd
Efficient and accurate data collection is paramount in clinical trials, and the design of Electronic Case Report Forms eCRFs plays a pivotal role in streamlining this process. This paper explores the integration of machine learning techniques in the design and implementation of eCRFs to enhance data collection efficiency. We delve into the synergies between eCRF design principles and machine learning algorithms, aiming to optimize data quality, reduce errors, and expedite the overall data collection process. The application of machine learning in eCRF design brings forth innovative approaches to data validation, anomaly detection, and real time adaptability. This paper discusses the benefits, challenges, and future prospects of leveraging machine learning in eCRF design for streamlined and advanced data collection in clinical trials. Dhanalakshmi D | Vijaya Lakshmi Kannareddy "Streamlining Data Collection: eCRF Design and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63515.pdf Paper Url: https://www.ijtsrd.com/biological-science/biotechnology/63515/streamlining-data-collection-ecrf-design-and-machine-learning/dhanalakshmi-d
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
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3. User interface Design: In order to improve the
user experience, design an intuitive and easy-to-
use user interface that allows players to easily
enter and obtain relevant information about game
equipment and intuitively understand its potential
benefits.
System implementation and evaluation: implement
and test the designed system, evaluate its performance
and effectiveness. Based on user feedback and actual
data, further optimize the function and performance
of the system.
Expected results:
Through the design and development of this research,
we hope to achieve a complete function and excellent
performance of the game equipment between the
purchase and rental rate analysis system. The system
will provide players with accurate revenue evaluation
and decision support to help them better manage and
optimize their game assets. At the same time, the
system will also provide valuable data and insights
for game developers and platform operators to
promote the healthy development of the game market.
Conclusion:
This study will help players evaluate and optimize
their game assets by designing and developing a
return analysis system between the purchase and
rental of game equipment. At the same time, the
system will also provide valuable data and references
for game developers and platform operators to
promote the prosperity and development of the game
market. It is hoped that this study can provide useful
reference and enlightenment for scholars and
practitioners in related fields.
1. Data collection and collation: The system needs
to collect the purchase and rental data on the
game equipment trading platform, and organize
and archive it. This data can include information
such as the price of the equipment, the length of
the lease, the number of transactions, as well as
player reviews and feedback.
2. Data analysis and model building: The system can
use data analysis and modeling technology to
carry out statistics and analysis on historical
transaction data and establish corresponding
mathematical models and algorithms. These
models and algorithms can be used to predict and
evaluate future returns and provide decision
support.
3. User interface design: The system needs to design
a user-friendly interface, so that players can easily
query and analyze the return of equipment. The
user interface should provide a variety of filtering
and sorting features, as well as charts and graphic
displays, to help players more intuitively
understand the data and results.
4. Yield calculation and display: The system can
calculate the yield of each equipment based on
data analysis and models, and display it to the
player. The return rate can be calculated
according to different indicators and time periods,
such as daily return rate, weekly return rate,
monthly return rate, etc.
5. Risk assessment and warnings: Based on data and
models, the system can assess the risk level of
each device and provide the player with
appropriate warnings and recommendations. For
example, for high risk equipment, the system can
alert the player to the risk and make decisions
accordingly.
Real-time updates and pushes: The system should be
able to update data and calculations in real time, and
provide the player with corresponding pushes and
alerts. In this way, players can keep abreast of the
latest returns and market dynamics and make timely
decisions.
To sum up, the design and development of a return
analysis system between the purchase and rental of
game equipment, combined with a game equipment
trading platform, can provide players with more
comprehensive functions and services, help them
make wise decisions, optimize asset management, and
promote the healthy development of the game market.
The design and development of a return analysis
system between the purchase and rental of game
equipment can help players better evaluate the
potential revenue from the purchase and rental of
equipment. Here are some considerations regarding
potential gains:
1. Equipment prices: The system should collect and
collate price data of game equipment, including
purchase and rental prices. By comparing
purchase and rental prices, players can get a
preliminary sense of the potential revenue of the
equipment.
2. Lease term: The system should record the lease
term of the equipment and correlate it with the
rent. A longer lease term means that players can
earn rental income for a longer period of time,
which increases the potential revenue from
equipment.
3. Number of transactions: The system should
record the number of transactions per device. A
higher number of transactions means that there is
a greater demand for equipment, which may
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increase rental income and equipment value,
thereby increasing potential earnings.
4. Player reviews and feedback: The system should
collect and collate player reviews and feedback
on equipment. Better reviews and feedback may
attract more players to rent equipment, increasing
rental income and the potential revenue of
equipment.
5. Market Trends: The system should monitor trends
and changes in the game equipment market. By
analyzing market trends, players can predict the
value of equipment and rent movements, further
evaluating potential earnings.
The above are some potential income considerations,
according to which the system can conduct data
analysis and model building to provide the
corresponding rate of return assessment and forecast.
Players can take advantage of the system's features
and services to more accurately evaluate the potential
benefits of purchasing and renting equipment and
make more informed decisions.
Designing and developing a revenue analysis system
between purchase and rental of game equipment faces
the following problems and challenges:
1. Data acquisition and update: The system needs to
collect and organize a large amount of data such
as equipment prices, rentals, number of
transactions and player reviews. This data can
come from different sources, such as gaming
platforms, marketplaces, and player communities.
Ensuring the accuracy, timeliness and
completeness of data is a challenge.
2. Data analysis and modeling: The system needs to
analyze and model the collected data to evaluate
the potential benefits of the equipment. This may
involve techniques such as statistical analysis,
machine learning, and predictive modeling.
Designing suitable analytical methods and
modeling frameworks is a complex problem.
3. Market volatility and uncertainty: The gaming
equipment market is a dynamic environment, and
factors such as prices, rents and demand may
change at any time. The system needs to update
data and models in a timely manner to reflect
market movements and uncertainties. At the same
time, the system also needs to provide flexible
forecasting and decision support to adapt to
different market conditions.
4. User experience and usability: The system needs
to provide a user-friendly interface and features
so that players can easily use and operate.
Considerations such as user interface design,
interaction design, and system performance are
challenges in the design and development
process.
5. Data security and privacy protection: The system
needs to protect the data security and privacy of
users and comply with relevant laws and
regulations. Designing and implementing
effective security measures is an important
challenge.
To sum up, designing and developing a return
analysis system between the purchase and rental of
game equipment is a complex and challenging task.
Issues such as data acquisition, analysis and
modeling, market movements, user experience and
data security need to be integrated to provide
accurate, practical and reliable yield assessment and
forecasting services.
The design and development of a rate of return
analysis system between the purchase and rental of
game equipment can be based on the following data
analysis rate prediction methods:
1. Historical data analysis: Collect and analyze data
such as equipment price, rental and number of
transactions over a period of time. Statistical
analysis methods, such as mean, variance, and
trend analysis, can be used to understand the
historical returns of equipment.
2. Correlation analysis: By analyzing the correlation
between equipment price, rent and other factors
(such as game version, demand, scarcity, etc.), to
predict the future revenue of equipment. Methods
such as correlation coefficient and regression
analysis can be used to determine the degree of
influence of different factors on equipment
income.
3. Time series analysis: Through the analysis of time
series data such as equipment price, rent and
transaction number, to predict the future
equipment revenue. Time series models, such as
the ARIMA model and the exponential smoothing
model, can be used to capture trends, seasonality
and cyclicality in the data to make yield forecasts.
4. Machine learning algorithm: Machine learning
algorithm can be used to establish equipment
return prediction model. Supervised learning
algorithms, such as linear regression, decision
trees, random forests, etc., can be used to train
models and make predictions using historical
data. Unsupervised learning algorithms, such as
cluster analysis and association rule mining, can
also be used to discover the patterns and laws of
equipment returns.
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When designing and developing a system, you need
to consider the following steps:
1. Data collection and collation: Collect equipment
price, rent and transaction quantity data from
multiple sources such as game platforms, trading
markets and player communities, and clean and
collate the data to ensure the accuracy and
integrity of the data.
2. Data analysis and model building: Analyze the
collected data and establish the corresponding
yield prediction model. According to the specific
needs and problems, select the appropriate data
analysis method and model algorithm, and
perform parameter tuning and model evaluation.
3. Model update and optimization: Update data
regularly, and retrain and optimize the yield
prediction model. According to the changes and
uncertainties of the market, timely adjust the
parameters and structure of the model to improve
the accuracy and reliability of the forecast.
4. User interface design and functional
implementation: Design user-friendly interfaces
so that players can easily enter data, view
predictions and make decisions. Implement the
relevant functions of the system, such as data
visualization, predictive reporting, interactive
queries, etc., to provide comprehensive analysis
and decision support.
5. Data security and privacy protection: Ensure the
security and privacy of user data, and take
appropriate security measures, such as data
encryption, access control, etc. At the same time,
follow the relevant laws and regulations to protect
the rights and interests of users.
To sum up, the design and development of a rate of
return analysis system between the purchase and
rental of game equipment can be based on the rate of
return prediction method of data analysis, and
comprehensive consideration of data collection,
analysis and model building, model updating and
optimization, user interface design and function
implementation, data security and privacy protection
and other issues.
If the rate of return prediction method based on
machine learning is designed and developed, the rate
of return analysis system between the purchase and
rental of game equipment can be carried out
according to the following steps:
1. Data collection and preparation: Collect historical
data such as equipment price, rent and transaction
quantity, and clean and organize them. Data can
be obtained from multiple sources, such as
gaming platforms, trading markets, and player
communities.
2. Feature engineering: carry out feature engineering
processing according to the characteristics of data
and business requirements. Missing value
processing, outlier processing, feature selection
and other operations can be carried out on the
data to extract meaningful features.
3. Data partitioning: The collected data is divided
into training sets and test sets. The usual split is
70% of the data as the training set and 30% of the
data as the test set.
4. Model selection and training: According to the
nature of the prediction problem, select the
appropriate machine learning algorithm for
training. You can choose linear regression,
decision tree, random forest and other algorithms.
The training set is used to train the model and
perform parameter tuning.
5. Model evaluation and selection: Use test sets to
evaluate the performance of trained models.
Common evaluation metrics can be used, such as
mean square error (MSE), root mean square error
(RMSE), mean absolute error (MAE), etc.
According to the evaluation results, the optimal
model is selected.
6. Prediction and application: Use trained models to
predict future returns. Based on the forecast
results, recommendations can be made, such as
when to buy or rent out equipment to get the most
out of it.
7. Model update and optimization: Update data
regularly, and retrain and optimize the model.
According to the changes and uncertainties of the
market, timely adjust the parameters and structure
of the model to improve the accuracy and
reliability of the forecast.
8. User interface design and functional
implementation: Design user-friendly interfaces
so that players can easily enter data, view
predictions and make decisions. Implement the
relevant functions of the system, such as data
visualization, predictive reporting, interactive
queries, etc., to provide comprehensive analysis
and decision support.
9. Data security and privacy protection: Ensure the
security and privacy of user data, and take
appropriate security measures, such as data
encryption, access control, etc. At the same time,
follow the relevant laws and regulations to protect
the rights and interests of users.
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To sum up, the rate of return prediction method based
on machine learning can be applied to the design and
development of the rate of return analysis system
between the purchase and rental of game equipment
through data collection and preparation, feature
engineering, model training and selection, prediction
and application, model update and optimization, user
interface design and function implementation, data
security and privacy protection and other steps.
Provide accurate yield forecasting and decision
support.
In the design and development of a return analysis
system between the purchase and rental of game
equipment, the following cutting-edge technologies
can be considered:
1. Deep Learning: Use deep learning algorithms
such as neural networks, convolutional neural
networks (CNNS), and recurrent neural networks
(RNNS) to process large amounts of unstructured
data and make more accurate predictions and
analyses.
2. Reinforcement Learning: Apply reinforcement
learning algorithms, such as Q-learning and Deep
reinforcement Learning (DRL), to model the
player's decision-making process and
environmental interactions, and optimize
equipment purchase and rental strategies to
maximize revenue.
3. Predict market trends: Use time series analysis
and machine learning techniques to predict future
trends in gaming equipment prices and rentals to
help players make more informed buying and
renting decisions.
4. Natural Language Processing: Through natural
language processing technology, sentiment
analysis and theme modeling are conducted on
the comments, evaluations and feedbacks of the
player community to understand the needs and
preferences of players for equipment, so as to
guide the purchase and rental strategy of
equipment.
5. Big Data and cloud computing: Utilize big data
and cloud computing technologies to process and
analyze large-scale game data to provide more
accurate and real-time yield forecasting and
decision support.
6. Visualization and interaction design: Interactive
visualization technology is adopted to design an
intuitive and easy-to-use user interface, so that
players can intuitively view and understand the
results of yield analysis, and carry out interactive
queries and decisions.
7. Blockchain technology: The use of blockchain
technology to realize the digital assets of game
equipment, ensure the transparency, security and
traceability of transactions, and provide more
trading options and opportunities for players.
8. Social network analysis: Through social network
analysis technology, tap the social relationships
and influence among players to better understand
the demand and market potential of equipment,
and optimize buying and renting strategies.
In short, the above cutting-edge technology can be
applied in the design and development of the return
analysis system between the purchase and rental of
game equipment, providing more accurate, intelligent
and personalized return prediction and decision
support.
The rate of return analysis system between the
purchase and rental of game equipment needs to
collect and process a large amount of data for analysis
and prediction. The following are the general steps of
data collection and processing:
1. Data collection: Collect data such as price, rent
and transaction records of game equipment. Real-
time price and transaction data can be obtained
through the API interface of the game platform,
and relevant data can be obtained from game
forums, social media and third-party trading
platforms through crawling technology.
2. Data cleaning: Clean and preprocess the collected
original data to remove duplicate data, missing
values and outliers. Data cleaning tools and
algorithms can be used for data cleaning and
processing.
3. Data conversion: The original data is converted
into a format and structure suitable for analysis,
such as converting text data into numerical or
classified data, and time series data into time
stamps or time intervals.
4. Feature engineering: According to analysis
objectives and model requirements, feature
extraction and transformation of data are carried
out to extract useful information and features.
Feature engineering can be performed using
statistical methods, machine learning algorithms,
and domain knowledge.
5. Data set partitioning: The processed data set is
divided into training set and test set for model
training and evaluation. Data set partitioning can
be done using techniques such as cross-validation.
6. Data analysis and modeling: Statistical methods,
machine learning algorithms and deep learning
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models are used for data analysis and modeling to
predict the price and rental trends of game
equipment and optimize purchasing and rental
strategies.
7. Model evaluation and optimization: Evaluate and
optimize the established model, evaluate the
performance of the model through indicators such
as accuracy rate, recall rate, F1 score, mean
square error (MSE), etc., and perform parameter
tuning and model selection.
8. Data visualization: Use visualization technology
to visualize the analysis results so that users can
intuitively understand and use the analysis results.
You can use charts, maps, dashboards, and other
visual elements to present the results of your
analysis.
In short, data collection and processing is a key step
in the design and development of the return analysis
system between the purchase and rental of game
equipment. The quality of data and the choice of
processing method will affect the accuracy and
reliability of the system.
The construction of the return analysis model
between the purchase and rental of game equipment is
the core part of the design and development of the
system. The following are the general steps for the
construction of the yield analysis model:
1. Data preprocessing: Pre-processing operations
such as cleaning, conversion and feature
engineering are carried out on the collected data
to obtain a clean and usable data set.
2. Feature selection: Select the appropriate features
from the pre-processed data set according to the
analysis objectives and model requirements.
Feature selection can be performed using feature
selection algorithms, statistical methods, and
domain knowledge.
3. Model selection: Select the appropriate machine
learning algorithm or deep learning model
according to the analysis objectives and data
characteristics. Commonly used models include
linear regression model, decision tree model,
random forest model, neural network model and
so on.
4. Model training: Use the training data set to train
the selected model. Techniques such as cross-
validation can be used to evaluate the
performance of the model and perform parameter
tuning.
5. Model evaluation: Evaluate the trained model
using test data sets, calculate the model accuracy,
recall rate, F1 score, mean square error (MSE)
and other indicators to evaluate the performance
of the model.
6. Model optimization: Optimize the model
according to the evaluation results. The
parameters of the model can be adjusted, the
strategy of feature selection can be changed or the
method of data preprocessing can be adjusted to
improve the performance of the model.
7. Model application: Use the optimized model to
forecast and analyze the new data, so as to obtain
the yield prediction results between the purchase
and rental of game equipment.
8. Visual display: The model prediction results are
displayed to users through visual technology, so
that users can intuitively understand and use the
analysis results. You can use charts, maps,
dashboards, and other visual elements to show
your predictions.
In short, the construction of the return analysis model
is one of the core tasks of the design and development
of the return analysis system between the purchase
and rental of game equipment. The accuracy and
reliability of the model will directly affect the
practicability and user experience of the system.
Therefore, the key factors such as data quality, feature
selection, model selection and parameter tuning
should be fully considered in the process of model
construction.
The design and development of the return analysis
system between the purchase and rental of game
equipment can adopt the following system
architecture design:
1. Data collection and storage layer: This layer is
responsible for collecting data related to the
purchase and rental of game equipment, and
storing the data in the database. You can use
crawler technology or API interface and other
ways to collect data, select the appropriate
database for data storage.
2. Data preprocessing and feature engineering layer:
This layer is responsible for pre-processing
operations such as cleaning, conversion and
feature engineering of the collected data. Data can
be preprocessed using techniques such as data
cleaning, data transformation, and feature
engineering to produce clean, usable data sets.
3. Model training and optimization layer: This layer
is responsible for selecting the appropriate
machine learning algorithm or deep learning
model, and using the pre-processed data set to
train and optimize the model. Techniques such as
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cross-validation can be used to evaluate the
performance of the model and perform parameter
tuning.
4. Model evaluation and verification layer: This
layer is responsible for evaluating the trained
model using test data sets, and calculating the
model accuracy, recall rate, F1 score, mean
square error (MSE) and other indicators to
evaluate the model performance.
5. Model application and display layer: This layer is
responsible for using the optimized model to
forecast and analyze the new data, so as to obtain
the yield prediction results between the purchase
and rental of game equipment. At the same time,
it is also necessary to display the model prediction
results to users through visualization technology,
so that users can intuitively understand and use
the analysis results.
6. User interface and interaction layer: This layer is
responsible for designing and implementing user
interface and interaction functions to provide
user-friendly operation interface and interaction
mode. The user interface can be implemented
using a Web interface, a mobile application, or a
desktop application.
7. System management and Deployment layer: This
layer is responsible for system management and
deployment. This section describes how to install,
configure, monitor, and maintain the system to
ensure system stability and reliability.
The above is the system architecture design of the
design and development of the return analysis system
between the purchase and rental of game equipment.
Each level can be further refined and optimized based
on specific needs and technology choices. At the
same time, it is necessary to consider the scalability,
performance and security requirements of the system
to meet the needs of practical applications.
When implementing and developing a rate of return
analysis system between purchase and rental of game
equipment, the following implementation steps and
development tools can be considered:
1. Implementation of data collection and storage
layer: Depending on the data source, you can
choose to use Python's crawler framework (such
as Scrapy) for data collection, or use the API
interface provided by the game platform to obtain
data. You can choose to use a relational database
(such as MySQL) or a NoSQL database (such as
MongoDB).
2. Implementation of data preprocessing and feature
engineering layer: Python's data processing
library (such as Pandas) can be used for data
cleaning, conversion and feature engineering.
Depending on the requirements, additional data
processing and feature engineering tools may be
required.
3. Implementation of model training and
optimization layer: Python's machine learning
libraries (such as Scikit-learn) or deep learning
frameworks (such as TensorFlow, PyTorch) can
be used for model training and optimization.
According to specific needs, suitable algorithms
and models can be selected for training and
optimization.
4. Implementation of model evaluation and
verification layer: Evaluation functions provided
by Python's machine learning library or deep
learning framework can be used for model
evaluation and verification. According to the
specific needs, we can choose the appropriate
evaluation index to evaluate the model
performance.
5. Implementation of model application and
presentation layer: Python's machine learning
library or deep learning framework can be used
for model application to predict and analyze new
data. At the same time, data visualization libraries
(such as Matplotlib and Seaborn) can be used to
visualize the model prediction results.
6. Implementation of user interface and interaction
layer: According to specific needs, you can
choose to use Web interface development
framework (such as Django, Flask) for the design
and implementation of user interface and
interactive functions. You can also choose to use
mobile application development frameworks
(such as React Native, Flutter) or desktop
application development frameworks (such as
Electron) for the design and implementation of
user interface and interaction features.
7. Implementation of the system management and
deployment layer: You can select appropriate
server environments and configurations, and use
automated deployment tools (such as Docker and
Ansible) to manage and deploy the system. At the
same time, the monitoring and maintenance of the
system should be considered to ensure the
stability and reliability of the system.
The above is the implementation of the rate of return
analysis system between the purchase and rental of
game equipment and the selection of development
tools. According to the actual situation and demand, it
can be adjusted and optimized appropriately. At the
same time, it is also necessary to consider factors
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such as the technical ability of the team and the
project budget, and select appropriate tools and
technologies for system implementation and
development.
Designing and developing a revenue analysis system
between purchase and rental of game equipment
involves the following steps:
1. System requirement analysis: Determine the
functions and objectives of the system, including
collection and analysis of equipment purchase and
rental data, calculation of returns, generation of
reports, etc.
2. Data collection and processing: Design database
model for storing relevant data of equipment
purchase and rental, including equipment name,
purchase price, rental price, rental duration, etc.
3. Data analysis and calculation: Calculate the return
rate of equipment according to the collected data.
The rate of return can be calculated by calculating
the ratio of the rental income to the purchase cost.
4. Report generation and visualization: According to
the calculated yield data, generate reports and
charts, so that users can intuitively understand the
revenue of equipment.
5. User interface design and development: Design
and develop the user interface, so that users can
easily enter and query the data of equipment
purchase and rental, and view the generated
reports and charts.
6. System testing and optimization: Function testing
and performance optimization of the system to
ensure the stability and reliability of the system.
7. Deployment and maintenance: Deploy the system
on a server and periodically maintain and update
the system to ensure normal system running.
It should be noted that in the design and development
process, data security and privacy protection need to
be considered to ensure that users' data is not leaked
or abused. At the same time, it is also possible to
consider the introduction of machine learning and
data mining technologies to conduct more in-depth
analysis and prediction of data, and provide more
accurate yield forecasts and recommendations.
When designing and developing a return analysis
system for game equipment purchases and rentals,
test datasets can be constructed using the following
methods:
1. Game equipment purchase data: Simulate the
purchase behavior of game players, including the
type of equipment purchased, the quantity
purchased, the purchase price and other
information. Purchase data can be generated
randomly based on the actual situation, or
simulation can be performed using existing game
data.
2. Game equipment rental data: simulate the rental
behavior of game players, including the type of
rental equipment, rental quantity, rental price and
other information. Rental data can be generated
randomly based on the actual situation, or
simulation can be performed using existing game
data.
3. Revenue data: Calculate revenue data based on
purchase and rental data, including total revenue,
total expenditure, net income and other
information for each equipment type. Calculations
and simulations can be performed using
programming languages such as Python.
4. Other relevant data: Other relevant data can be
constructed according to the actual situation, such
as the attributes of the equipment, the level of the
player, the player's behavioral preferences and
other information. These data can be used for
subsequent analysis and model training.
When constructing test data sets, it is necessary to pay
attention to the authenticity and reliability of the data.
Simulations can be performed using existing game
data, or data generation based on game rules and
experience. At the same time, it is also necessary to
consider the diversity and distribution of data to
ensure that the test data can cover the various
situations and boundary conditions of the system.
In the process of development, some real data can be
used to test and verify the system. Cross-validation
can be used to divide the data set into a training set
and a test set, use the training set for model training
and optimization, and then use the test set for model
evaluation and validation. Model evaluation and
validation can be performed using Python's machine
learning libraries or evaluation functions provided by
deep learning frameworks. According to the specific
needs, we can choose the appropriate evaluation
index to evaluate the model performance. At the same
time, the data visualization library can also be used to
visualize the model prediction results, so as to
understand and analyze the performance of the model.
When designing and developing a return analysis
system between the purchase and rental of game
equipment, the following functional tests can be
carried out:
1. Input data test: Test whether the system can
correctly receive and process input data, including
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the type, quantity and price of equipment
purchased and rented. You can enter some special
cases and boundary conditions, such as a negative
purchase quantity, a zero price, or a negative
number, to test the system's ability to handle
exceptional cases.
2. Calculate revenue test: Test whether the system
can correctly calculate the income of purchase
and rental data, including the total revenue, total
expenditure, net income and other information of
each equipment type. Some known test data can
be used for testing and then compared with the
expected results.
3. Visual display test: Test whether the system can
visually display revenue data to facilitate users to
understand and analyze the performance of the
model. You can check that the graphs generated
by the system are correct, clear, and provide
enough information to support decision analysis.
4. Performance test: Test the performance and
efficiency of the system when processing large-
scale data. Larger test data sets can be used for
testing to ensure that the system can maintain
good response speed and stability when dealing
with large amounts of data.
5. User interface test: Test whether the user interface
of the system is friendly, easy to use, and can
correctly display and manipulate data. You can
simulate user usage scenarios to test whether the
system's response and feedback to user operations
meet expectations.
6. Data security test: Test whether the system can
protect the security and privacy of user data.
Some attack scenarios can be simulated, such as
injection attack and cross-site scripting attack, to
test the system's protection capability against
security vulnerabilities.
7. Compatibility test: Test the compatibility of the
system on different operating systems, browsers,
and devices. It can be tested on different
operating systems and browsers to ensure that the
system will work properly in a variety of
environments.
The above are some common functional tests, which
can be adjusted and supplemented according to the
actual situation. When conducting functional tests,
automated test tools can be used to improve test
efficiency and accuracy. At the same time, it can also
conduct user feedback and demand research to obtain
users' opinions and suggestions, and optimize and
improve the system.
When designing and developing a return analysis
system for game equipment between purchase and
rental, the following performance evaluations can be
performed:
1. Response time evaluation: Test the response time
of the system when the user requests. You can
simulate multiple users accessing the system at
the same time and record the response time of the
system. By analyzing the response time, you can
evaluate the performance of the system under
high concurrency.
2. Concurrent capability evaluation: Test the
performance of the system when processing
multiple requests at the same time. Load testing
tools can be used to simulate multiple concurrent
users and observe the processing power of the
system. By gradually increasing the number of
concurrent users, you can assess the concurrency
capability and performance bottlenecks of the
system.
3. Scalability assessment: Test the scalability and
scalability of the system under increased load.
Load testing tools can be used to simulate large-
scale user access to the system and observe the
load on the system. The scalability and
performance of the system can be evaluated by
increasing the load.
4. Data processing capability evaluation: test the
performance of the system when processing large-
scale data. Large test data sets can be used for
testing and to observe the processing speed and
resource consumption of the system. By
evaluating the performance of the system when
dealing with large amounts of data, the data
processing capacity of the system can be
determined.
5. Stability assessment: Test the stability of the
system under long running and high load
conditions. Stress tests can be conducted over a
long period of time to see if the system can
operate stably and without crashes or errors. By
evaluating the stability of the system, it is
possible to determine the reliability of the system
under long-term use and high load conditions.
6. Resource consumption evaluation: Tests the
resource consumption of the system during
running, such as CPU, memory, and disk space.
Performance monitoring tools can be used to
monitor the resource usage of the system and
assess the efficiency and optimization of the
system in using resources.
The above are some common performance evaluation
methods, which can be adjusted and supplemented
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according to the actual situation. During performance
evaluation, you can use performance testing tools and
monitoring tools to assist in evaluating and analyzing
system performance. At the same time, the system
can be optimized and adjusted to improve the system
performance and efficiency.
After designing and developing the return analysis
system between the purchase and rental of game
equipment, the results of the system need to be
analyzed and discussed. Here are some possible
outcomes for analysis and discussion:
1. Yield comparison: Compare the purchase and
rental yields of different equipment, and analyze
which equipment is more profitable in purchase
and rental. You can calculate the average return
rate of each equipment and make a comparative
analysis.
2. Price strategy analysis: Analyze the price strategy
of equipment purchase and rental, including
pricing methods and price levels. We can
compare the rate of return under different price
strategies and observe the effect of price
adjustment on the rate of return.
3. Game demand analysis: Analyze the demand for
equipment in different types of games, as well as
the purchase and rental preferences of different
players for equipment. Data can be obtained
through user data analysis and questionnaires, and
demand analysis.
4. Player Behavior Analysis: Analyze the behavior
patterns and habits of players when buying and
renting equipment. Players can be observed to
buy and rent frequency, purchase and rental
timing and other behaviors, and statistics and
analysis.
5. Income fluctuation analysis: Analyze the income
fluctuation of equipment purchase and rental,
observe the change trend and periodicity of the
rate of return. It is possible to analyze and
forecast the income fluctuation by statistical
historical data and applying time series analysis
method.
6. Income risk assessment: Assess the income risk
of equipment purchase and rental, including
market fluctuations, changes in supply and
demand, competition and other factors. Risk
assessment and management can be carried out by
means of risk models and sensitivity analysis.
The above are some possible results analysis and
discussion directions, which can be adjusted and
supplemented according to the actual situation.
Through the analysis and discussion of the system
results, it can help optimize and improve the system
design, improve the profitability and user satisfaction.
In the process of designing and developing the return
analysis system between the purchase and rental of
game equipment, we summarized the following work:
1. Demand analysis: First of all, we carried out a
demand analysis to understand the needs of users
and the functional requirements of the system. We
conducted communications and interviews with
gamers and game equipment providers to
understand their needs and expectations, thereby
determining the functionality and design goals of
the system.
2. Data collection and processing: In order to
conduct yield analysis, we need to collect and
process a large amount of data. We worked with
gaming equipment providers to get their sales and
rental data. We then cleaned and collated the data,
processed the missing data and outliers, and made
the data suitable for subsequent analysis and
modeling.
3. Rate of Return calculation and model building:
After collecting and processing data, we carried
out rate of return calculation and model building.
Based on historical data and market conditions,
we set up a rate of return calculation model,
including the formula and method of buying and
renting rate of return calculation. We used Python
programming language to write and implement
the model, and tested and verified it.
4. Result analysis and discussion: After the model
was established, we conducted result analysis and
discussion. We calculated the average rate of
return for each piece of equipment and performed
a comparative analysis to determine which pieces
were more profitable to buy and rent. We also
analyze the yield under different price strategies
and observe the effect of price adjustments on the
yield. In addition, we have conducted research
and discussion on game demand analysis, player
behavior analysis, revenue fluctuation analysis
and revenue risk assessment.
5. Application and improvement of results: Finally,
we apply the analysis results to system design and
improvement. Based on the analysis results, we
optimized the function and interface design of the
system to improve the user experience. At the
same time, we also put forward some
improvement suggestions based on the analysis
results, including adjusting the price strategy,
improving the supply and demand matching
mechanism, so as to improve the profitability and
user satisfaction.
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Through the summary of the above work, we have
successfully designed and developed the return
analysis system between the purchase and rental of
game equipment, and provided valuable analysis and
decision support for game players and equipment
providers. Our work provides reference and help for
the development and operation of the game industry,
and also provides reference and inspiration for the
research and application of similar fields.
During the design and development of our game
equipment return analysis system between purchase
and rental, the following research results and
innovations have been achieved:
1. Demand refinement and user customization: In
the demand analysis stage, we not only
understand the basic needs of users, but also
deeply explore the special needs and personalized
customization of users. Through communication
and interviews with game players and game
equipment providers, we understand the
preferences and expectations of different types of
players on equipment purchase and rental, so as to
consider the needs of different user groups in the
system design, and provide personalized functions
and services.
2. Data processing and modeling: In the stage of
data collection and processing, we adopt
advanced data cleaning and sorting technology to
accurately and efficiently process a large number
of sales and rental data. In the model building
stage, we built an innovative return calculation
model based on historical data and market
conditions, taking into account the influence of
multiple factors such as price, supply and
demand, player demand, etc., thus improving the
accuracy and reliability of the model.
3. Multi-dimensional result analysis and discussion:
In the result analysis and discussion stage, we not
only calculated the average return rate of each
equipment, but also carried out multi-dimensional
analysis and comparison, such as the return rate
comparison of different equipment types,
different price strategies, different player groups,
etc. We also analyze the volatility of returns and
risk assessment to provide users with more
comprehensive analysis results and decision
support.
4. Practical verification of results application and
improvement: By applying the analysis results to
system design and improvement, we verify the
actual value and validity of the analysis results.
We improve the user experience by optimizing
the functions and interface design of the system;
At the same time, we put forward some
improvement suggestions and verified the
effectiveness of these improvements in practice,
thus further improving the profitability and user
satisfaction.
In general, our research achievements and innovations
are mainly reflected in requirements refinement and
user customization, data processing and model
building, multidimensional analysis and discussion of
results, and practical verification of results application
and improvement. Theseachievements and innovation
points provide valuable research results and decision
support for the development and operation of the
game industry, and also provide reference and
inspiration for research and application in related
fields.