scholarly journals An AI-Based Exercise Prescription Recommendation System

2021 ◽  
Vol 11 (6) ◽  
pp. 2661
Author(s):  
Hung-Kai Chen ◽  
Fueng-Ho Chen ◽  
Shien-Fong Lin
Keyword(s):  
Recommendation System ◽  
Exercise Prescription ◽  
Rest Heart Rate ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Exercise Mode ◽  
Preventive Cardiology ◽  
Community Data ◽  
Testing Accuracy ◽  
Digital Training

The European Association of Preventive Cardiology Exercise Prescription in Everyday Practice and Rehabilitative Training (EXPERT) tool has been developed for digital training and decision support in cardiovascular disease patients in clinical practice. Exercise prescription recommendation systems for sub-healthy people are essential to enhance this dominant group’s physical ability as well. This study aims to construct a guided exercise prescription system for sub-healthy groups using exercise community data to train an AI model. The system consists of six modules, including three-month suggested exercise mode (3m-SEM), predicted value of rest heart rate (rest HR) difference after following three-month suggested exercise mode (3m-PV), two-month suggested exercise mode (2m-SEM), predicted value of rest HR difference after following two-month suggested exercise mode (2m-PV), one-month suggested exercise mode (1m-SEM) and predicted value of rest HR difference after following one-month suggested exercise mode (1m-PV). A new user inputs gender, height, weight, age, and current rest HR value, and the above six modules will provide the user with a prescription. A four-layer neural network model is applied to construct the above six modules. The AI-enabled model produced 95.80%, 100.00%, and 95.00% testing accuracy in 1m-SEM, 2m-SEM, and 3m-SEM, respectively. It reached 3.15, 2.89, and 2.75 BPM testing mean absolute error in 1m-PV, 2m-PV, and 3m-PV. The developed system provides quantitative exercise prescriptions to guide the sub-healthy group to engage in effective exercise programs.

A New Contextual Influencer User Measure to Improve the Accuracy of Recommender System

2018 ◽  
Vol 9 (4) ◽  
pp. 38-51
Author(s):  
Maryam Jallouli ◽  
Sonia Lajmi ◽  
Ikram Amous
Keyword(s):  
Social Network ◽  
Recommender System ◽  
Recommendation System ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
User Model ◽  
Context Aware ◽  
Additional Information ◽  
Context Element ◽  
Cold Start Problem

In the last decade, social-based recommender systems have become the best way to resolve a user's cold start problem. In fact, it enriches the user's model by adding additional information provided from his social network. Most of those approaches are based on a collaborative filtering and compute similarities between the users. The authors' preliminary objective in this work is to propose an innovative context aware metric between users (called contextual influencer user). These new similarities are called C-COS, C-PCC and C-MSD, where C refers to the category. The contextual influencer user model is integrated into a social based recommendation system. The category of the items is considered as the most pertinent context element. The authors' proposal is implemented and tested within the food dataset. The experimentation proved that the contextual influencer user measure achieves 0.873, 0.874, and 0.882 in terms of Mean Absolute Error (MAE) corresponding to C-cos, C-pcc and C-msd, respectively. The experimental results showed that their model outperforms several existing methods.

scholarly journals Location Based Agricultural Product Recommendation System Using Novel KNN Algorithm

2019 ◽  
pp. 945-950
Author(s):  
Sachin J ◽  
Geethatharani P ◽  
Surya M K ◽  
Kavin K V
Keyword(s):  
Euclidean Distance ◽  
Recommendation System ◽  
Mean Absolute Error ◽  
Market Price ◽  
Absolute Error ◽  
Agricultural Products ◽  
Agricultural Product ◽  
Product Recommendation ◽  
Use Of Data ◽  
Great Circle Distance

It is evident that the need for personalized product recommendation is much needed these days. Generally, product recommender systems are implemented in web servers that make use of data, implicitly obtained as results of the collection of Web browsing patterns of the users. Here, the project's motive is to provide location-based agricultural product recommendation system using a novel KNN algorithm by ensuring effective communication and transparency in agriculture trade marketing among buyers and sellers (farmers). It helps the farmer to fix up the market price by preventing the rue pricing of their products. The farmer can post their products into the application with price and other details like a timestamp of harvesting, color, size, the absence of pest, freshness, ripeness etc. Based on the location, the distance between the seller and buyer is calculated using great circle distance. An improved Novel KNN algorithm is used to find the K Nearest Seller by calculating the distance between the sellers and buyer using a Euclidean distance metric. The details posted by the farmers and buyers are stored and updated in a database dynamically. The recommender system recommends nearest sellers and their agricultural products based on buyer interest. The performance of the system is analyzed in terms of accuracy and mean absolute error.

Hybrid Model for Movie Recommendation System Using Fireflies and Fuzzy C-Means

2019 ◽  
Vol 11 (2) ◽  
pp. 1-13
Author(s):  
M. Sandeep Kumar ◽  
Prabhu J.
Keyword(s):  
Hybrid Model ◽  
Recommender System ◽  
Firefly Algorithm ◽  
Recommendation System ◽  
Mean Absolute Error ◽  
Research Work ◽  
Absolute Error ◽  
Experimental Result ◽  
Fuzzy C Means ◽  
Fcm Clustering

In the era of Big Data, extremely complicated data is delivered from the system, of which it is impossible to collect the correct information with an online platform. In this research work, it provides a hybrid model for a movie-based recommender system; based on meta-heuristic firefly algorithm and fuzzy c-means (FCM) clustering technique to evaluate rating of a movie for a specific user based on the similarity of users and historical data. The firefly algorithm was employed in the movie lens dataset to get the initial cluster and also to initialize the position of clusters. FCM is used to classify the similarity of the user ratings. The proposed collaborative recommender system performed well regarding accuracy and precision. Various metrics are used in a movie lens dataset like mean absolute error (MAE), precision, and recall. The experimental result delivered by the system provides more efficient performance compared to the existing system in term of mean absolute error (MAE).

scholarly journals Performa Algoritma User K-Nearest Neighbors pada Sistem Rekomendasi di Tokopedia

2020 ◽  
Vol 5 (3) ◽  
pp. 302
Author(s):  
Rama Dian Syah
Keyword(s):  
Recommendation System ◽  
Mean Absolute Error ◽  
Prediction Method ◽  
Large Data ◽  
Nearest Neighbors ◽  
Absolute Error ◽  
K Nearest Neighbors ◽  
Algorithm Performance ◽  
Specific Product ◽  
Rating Prediction

The biggest marketplace in Indonesia such as Tokopedia has data on e-commerce activities that always increase with time. Large data growth in Marketplace can cause problems for users. Buyers who have difficulty in finding the best product that suits their needs and sellers who have difficulty in promoting products that are often visited by buyers can be overcome. The recommendation system can overcome these problems by providing specific product recommendations to be promoted and offered to buyers. This research implements the Recommendation System using the Item Rating Prediction Method by applying the User K-Nearest Neighbors Algorithm. The Recommendation System provides recommendations based on ratings on products given by the buyer. Algorithm performance in Recommendation System is measured by the parameters of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Normalized Mean Absolute Error (NMAE). The performance values obtained are RMSE = 0.713, MAE = 0.488 and NMAE = 0.122. Perfomance values below 1 proves that the User K-Nearest Neighbors Algorithm is suitable as a rating prediction model on recommendation system.

Movie Recommendation to Friends using Whale Optimization Algorithm

2019 ◽  
Vol 12 ◽  
Author(s):  
Sachin Papneja ◽  
Kapil Sharma ◽  
Nitesh Khilwani
Keyword(s):  
Recommendation System ◽  
Mean Absolute Error ◽  
Optimal Solution ◽  
Semantic Networks ◽  
Absolute Error ◽  
Optimization Methods ◽  
Whale Optimization ◽  
Social Media Platforms ◽  
Novel Method ◽  
Movie Recommendation

Background: Recent advances in the World Wide Web and semantic networks have amplified social networking platforms, where the users share their photos, hobbies, location, interests, and experiences such as movie or restaurants. Social media platforms such as Face book, twitter and LinkedIn are used to recommend the users the things of their interests such as movie, food, locations and friends. Objective: A novel method for the recommendation of the movies to friend using whale optimization has been introduced. Ratings given by friends of various movies are employed to recommend movies. Method: Different evolutionary based optimization methods have been applied for movie recommendation The proposed method has been tested on movie-lense dataset and results are compared with 5 other methods namely, K-means, PCA K- means, SOM, PCA-SOM, PSO and ABC in terms of mean absolute error, precision and recall. Result: The experimental results demonstrate that proposed method outperformed all considered methods for 88.5% clusters centers in terms of precision, recall and mean absolute error. Conclusion: A novel recommendation system based on users rating has been designed to recommend the movies to friends. It leverages the strengths of whale optimization to find the optimal solution.

Contactless Core-Temperature Monitoring by Infrared Thermal Sensor using Mean Absolute Error Analysis

2020 ◽  
Vol 15 ◽  
Author(s):  
Fahad Layth Malallah ◽  
Baraa T. Shareef ◽  
Mustafah Ghanem Saeed ◽  
Khaled N. Yasen
Keyword(s):  
Body Temperature ◽  
Embedded System ◽  
Core Temperature ◽  
Integrated Circuit ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Thermal Sensor ◽  
Human Face ◽  
Body Contact ◽  
Arduino Microcontroller

Aims: Normally, the temperature increase of individuals leads to the possibility of getting a type of disease, which might be risky to other people such as coronavirus. Traditional techniques for tracking core-temperature require body contact either by oral, rectum, axillary, or tympanic, which are unfortunately considered intrusive in nature as well as causes of contagion. Therefore, sensing human core-temperature non-intrusively and remotely is the objective of this research. Background: Nowadays, increasing level of medical sectors is a necessary targets for the research operations, especially with the development of the integrated circuit, sensors and cameras that made the normal life easier. Methods: The solution is by proposing an embedded system consisting of the Arduino microcontroller, which is trained with a model of Mean Absolute Error (MAE) analysis for predicting Contactless Core-Temperature (CCT), which is the real body temperature. Results: The Arduino is connected to an Infrared-Thermal sensor named MLX90614 as input signal, and connected to the LCD to display the CCT. To evaluate the proposed system, experiments are conducted by participating 31-subject sensing contactless temperature from the three face sub-regions: forehead, nose, and cheek. Conclusion: Experimental results approved that CCT can be measured remotely depending on the human face, in which the forehead region is better to be dependent, rather than nose and cheek regions for CCT measurement due to the smallest

scholarly journals Towards Tracking of Deep Brain Stimulation Electrodes Using an Integrated Magnetometer

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2670
Author(s):  
Thomas Quirin ◽  
Corentin Féry ◽  
Dorian Vogel ◽  
Céline Vergne ◽  
Mathieu Sarracanie ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Mean Absolute Error ◽  
Tracking System ◽  
Three Dimensional ◽  
Absolute Error ◽  
Magnetic Tracking ◽  
Magnetic Camera ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Brain

This paper presents a tracking system using magnetometers, possibly integrable in a deep brain stimulation (DBS) electrode. DBS is a treatment for movement disorders where the position of the implant is of prime importance. Positioning challenges during the surgery could be addressed thanks to a magnetic tracking. The system proposed in this paper, complementary to existing procedures, has been designed to bridge preoperative clinical imaging with DBS surgery, allowing the surgeon to increase his/her control on the implantation trajectory. Here the magnetic source required for tracking consists of three coils, and is experimentally mapped. This mapping has been performed with an in-house three-dimensional magnetic camera. The system demonstrates how magnetometers integrated directly at the tip of a DBS electrode, might improve treatment by monitoring the position during and after the surgery. The three-dimensional operation without line of sight has been demonstrated using a reference obtained with magnetic resonance imaging (MRI) of a simplified brain model. We observed experimentally a mean absolute error of 1.35 mm and an Euclidean error of 3.07 mm. Several areas of improvement to target errors below 1 mm are also discussed.

scholarly journals A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3719
Author(s):  
Aoxin Ni ◽  
Arian Azarang ◽  
Nasser Kehtarnavaz
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Video Camera ◽  
Measurement Methods ◽  
Rate Measurement ◽  
Learning Methods ◽  
The Public ◽  
Heart Rate Measurement

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

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