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Andrés G.

@and88x

verified-v2.svg
5.0
27
5.1
5.1
96%

Data Science | Machine Learning Developer

$26 USD / Hour
・
Flag of
Ecuador (6:16 AM)
・
Joined on July 21, 2016
$26 USD / Hour
・
In my first steps as a freelancer, I worked on projects related to automatic control, embedded systems, IoT, and intelligent systems. From the beginning of 2021, I decided to focus on Data Science. This subfield of artificial intelligence allows me to learn a lot about other fields that are not related to engineering. I'm glad to know that all my analytical skills, statistical knowledge, and programming skills are really helpful in achieving my goal of becoming an expert data scientist. As a data scientist, I have experience in tasks such as: ✔️ Exploratory data analysis (EDA) and visualization (Dash/Plotly, Shiny, Tableau) ✔️ Getting and cleaning data ✔️ Clustering (k-means, DBSCAN, hierarchical, spectral clustering) ✔️ Neural Networks (ADALINE, perceptron, feed-forward NN, Hopfield NN) ✔️ Classification algorithms (desition trees, logistic regression, gradient boosting, SVC, random forest, k-nearest neighbors, and so on) ✔️ Regression algorithms (DT, LR, GB, SVR, RF, k-NN, and so on) ✔️ Dimensionality reduction (PCA, SVD) ✔️ A/B testing ✔️ Time series forecasting (ML, DL) ✔️ SQL and NoSQL databases (MySQL, PostgreSQL, Cassandra, Neo4j) ✔️ Statistical inference ✔️ Reproducible research I can work in other fields that are not directly related to data science such as: ✔️ Fuzzy Logic (Mandani, Sugeno models) ✔️ Bioinspired optimization (PSO, Ant Colony Optimization, Artificial Bee Colony, Firefly Optimization, Dragonfly Optimization, Grey Wolf Optimizer) and Linear Programming ✔️ Linear programming ✔️ Autopilots for Unmanned Aerial Vehicles ✔️ C/C++ programming for Arduino and Microcontrollers ✔️ Python, R, MATLAB, Java, and Bash programming languages I prefer to use open-source tools like R or python to work. These programming languages remain on the cutting edge thanks to their active and supportive communities. I also use MATLAB a lot due to its power in math. Mostly available on Saturdays from 8 am to 4 pm GMT -5.
Verifications
On time
95%
On budget
91%
Accept rate
100%
Repeat hire rate
22%
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Portfolio

Portfolio

9654308
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
9654302
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
9654293
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
9654278
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
9654258
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
9654250
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
9654308
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
The problem aborted by this project is the search and location of an air pollutant source. A plume of SO2 is simulated and placed in a region to perform the search. The proposed navigation strategy is compared with three other tracking strategies: circular paths and leader-follower behavior, random walk with particle swarm optimization, and a hill climb traceability algorithm. The parameters for comparison were: proximity to the source, best pollutant sample, time to finish the exploration phase, and so on.
UAV-based Air Pollutant Source Localization
9654302
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
The project consists of replicating the time series forecasting approach of the scientific article "Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction". This paper uses historical energy consumption data for training a model based on three different algorithms. The Hourly Energy Consumption dataset (from Kaggle.com) is used for this purpose.
Ensemble Machine Learning Model for Time Series
9654293
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
The present project consists of separating into groups a dataset of Electric Meters. The application of this work can be useful for many purposes such as: - Detecting failures - Detecting fraudulent usage - Distinguish user types for tariff design - Demand-side management. The dataset consists of measures of 1507 smart electric meters (after deleting meters with missing data) during 1 year.
Electric Energy Meters Clustering
9654278
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
The present project consists of a load balancer based on the Artificial Bee Colony (ABC) algorithm and Fuzzy Logic. The project is programmed in Java using the CloudSim framework, in its version 3.0.3. In the first instance, the cloudlets’ tasks are assigned to a Virtual Machine (VM) according to the ABC algorithm. After that, tasks are reassigned using Fuzzy Logic blocks to select the best Host and VM. This project pretends to analyze if this approach improves some Quality of service (QoS) parameters as Degree of Imbalance, Response Time, Cost, and so on. All QoS parameters mentioned in the present work are shown in the article: “Honey bee behavior inspired load balancing of tasks in cloud computing environments”.
Fuzzy Logic based Load Balancer
9654258
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
Dognition is a web platform that understands the importance of a deep connection with our dogs and how it can influence their behavior. The Dognition team has done a lot of research to find out how our dogs see the world. To do this, they have developed a bunch of games that we can play with our dogs through their website. What is Dognition looking for? - They mainly need is: Find ways to increase the users’ completion rate. They need some insights from their data to push users and their dogs to complete a bit more tests. - So, the SMART objective is: Determine some strategies, applicable in the current test flow, to increase the number of tests users complete on the Dognition website by 10% over the incoming three months.
Dognition data analysis
9654250
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
Kin Security is a security service company that offers a variety of products to its clients. In specific, their star product, Kin Safety, has been in the market since 2012 and has been a total success in the European Market. For this product, a client must sign a contract, pay the installation costs, and a monthly fee for the service. However, in recent years, Kin Security has seen that a lot of its clients have canceled Kin Safety before two years. The biggest problem with this phenomenon is that Kin Safety has a significant fixed cost. 80% of this cost is assumed by Kin Security in order to be able to match the prices of its competition. Therefore, if a client leaves before two years, the company is not able to recover its investment.
Two-years Churn Prediction
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5.0 · 27 Reviews
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Python SQL Microsoft SQL Server Pandas • $60 USD
Excelente profesional !
Leandro O.
@oleandro78
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Capital federal, Argentina
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3 years ago
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Weka project • $20 USD
On time Professional Finished with all required
Mostafa O.
@mostafaomidi
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Arlington, United States
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3 years ago
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Project 33549351 has been deleted • $205 USD
Andres is an excellent professional. He have developed the algorithm with 134 constraints. Persevering to solve the work in the shortest possible time. I will definitely hire him again!
Leandro O.
@oleandro78
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Capital federal, Argentina
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3 years ago
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Translate my Python code to R • $83 USD
Excelente! Terminó el proyecto mucho antes de lo planeado. Altamente recomendado!
Carlos E M.
@mroder75
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Coyoacan, Mexico
•
3 years ago
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Generar GSlides con script de python • $90 USD
La comunicación con Andrés fue excelente. Al final entregó satisfactoriamente el trabajo con todos los requisitos solicitados y de manera autónoma resolvió los conflictos que iban surgiendo al momento del desarrollo. Lo recomiendo para trabajar con él, esta muy atento a responder a las inquietudes aun después de finalizar el trabajo.
Luisa Z.
@LuisaZapata2
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Medellín, Colombia
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3 years ago
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Experience
Data Engineer
Jun, 2022 - Present
•
2 years, 7 months
Devsu LLC
Jun, 2022 - Present
•
2 years, 7 months
- ETL - Data ingestion using RESTful API - Batch and streaming queries with Kafka - Azure Databricks, PySpark, SQL, Azure Data Factory
Jun, 2022 - Present
•
2 years, 7 months
Research Assistant
May, 2018 - Dec, 2019
•
1 year, 7 months
ITESM
May, 2018 - Dec, 2019
•
1 year, 7 months
- Measurement, storage, and analysis of atmospheric pollutant concentrations, to track the sources of air pollutants - Develop an environmental monitoring platform based on 2 UAVs (quadcopters) - Teach courses on tools for data analysis and dimensionality reduction
May, 2018 - Dec, 2019
•
1 year, 7 months
Education
Tecnológico de Monterrey
2017 - 2019
•
2 years
Master in Sciences of Engineering
Flag of
Mexico
2017 - 2019
•
2 years
Universidad de Cuenca
2011 - 2017
•
6 years
Electronics and Telecommunications Engineering (Bachelor's degree)
Flag of
Ecuador
2011 - 2017
•
6 years
Qualifications
Data Science-Specialized Program
2020
Johns Hopkins University - Coursera
The Data Science Specialization covers the concepts and tools for an entire data science pipeline. Successful participants learn how to use the tools of the trade, think analytically about complex problems, manage large data sets, deploy statistical principles, create visualizations, build and evaluate machine learning algorithms, publish reproducible analyses, and develop data products.
2020
Publications
Equidistributed Search+Probability Based Tracking Strategy to Locate an Air Pollutant Source
IEEE Access
A probability-based tracking strategy is proposed for guiding two cooperative unmanned aerial vehicles (UAVs) within a quest area to find an atmospheric pollutant source. This implies deploying algorithmically two phases: exploration and exploitation. During the exploration phase, each vehicle follows a trajectory based on plane coordinates generated from a Hammersley sequence. The navigation trajectories are smoothed by an TSP solver and a cubic spline planning algorithm.
Evaluating the Mindwave Head-set for Automatic Upper Body Motion Classification
INCISCOS - IEEE
Certifications
P
Preferred Freelancer Program SLA
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