scholarly journals Smart Recommendation System for Off-the Shelf Medicines

2018 ◽  
Vol 7 (2.24) ◽  
pp. 417
Author(s):  
Ratna Sathappan ◽  
Tholu Sai Indira ◽  
A Meenapriyadarsini
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Opinion Mining ◽  
Recommendation Systems ◽  
Product Category ◽  
Machine Learning Algorithms ◽  
Patient Feedback ◽  
The People ◽  
Using Data ◽  
The Impact

Internet usage has been at an all-time high from 2000’s vintage years. The people who have access to the internet use it for numerous reasons such as social networking, marketing, promoting, enhancing businesses, consultancy, research, gaming and the list goes on. In the recent years, Review websites have flourished, where people share their opinion about a product, with an increase in response rate and reliability. Recommendations are made by mining data from review websites. Traditional Recommendation systems are limited as they only consider certain metrics, such as product purchase details, product category. Recommendation systems are yet to gain popularity in the medical field. These days most patients are unable to figure out the medication that works in healing them in the best way possible, hence they turn to review websites in order to obtain a second opinion on the prescribed medication. In this work, we have developed a smart recommendation system for off-the Shelf Medical Drugs using machine learning and data analytics based on patient feedback. The patient feedback is unstructured data which is processed using data analytic tools. After which machine learning is used to recommend the best fit and compare the drugs. In this work, we predict the impact of a drug/ medicine on the patient to whom the medication was prescribed, using data mining techniques. Firstly, we detect the user’s polarity (positive/ negative/neutral) based on the patient feedback for a certain drug using sentiment analysis and opinion mining following which we use machine learning algorithms to track sentiment variation and to make a recommendation based on user polarity

scholarly journals Movie Recommenadation System

2021 ◽  
Vol 9 (4) ◽  
pp. 13-16
Author(s):  
Gandhali Malve ◽  
Lajree Lohar ◽  
Tanay Malviya ◽  
Shirish Sabnis
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Amount Of Information ◽  
System Recommendation ◽  
User Ratings

Today the amount of information in the internet growth very rapidly and people need some instruments to find and access appropriate information. One of such tools is called recommendation system. Recommendation systems help to navigate quickly and receive necessary information. Many of us find it difficult to decide which movie to watch and so we decided to make a recommender system for us to better judge which movie we are more likely to love. In this project we are going to use Machine Learning Algorithms to recommend movies to users based on genres and user ratings. Recommendation system attempt to predict the preference or rating that a user would give to an item.

A review of a recommendation filtering system approach based on reliable sustainable opinion mining

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Imen Gmach ◽  
Nadia Abaoub ◽  
Rubina Khan ◽  
Naoufel Mahfoudh ◽  
Amira Kaddour
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Information Search ◽  
Recommendation System ◽  
Opinion Mining ◽  
Information Filtering ◽  
Recommendation Systems ◽  
Content Filtering ◽  
Content Type ◽  
The Impact

PurposeIn this article the authors will focus on the state of the art on information filtering and recommender systems based on trust. Then the authors will represent a variety of filtering and recommendation techniques studied in different literature, like basic content filtering, collaborative filtering and hybrid filtering. The authors will also examine different trust-based recommendation algorithms. It will ends with a summary of the different existing approaches and it develops the link between trust, sustainability and recommender systems.Design/methodology/approachMethodology of this study will begin with a general introduction to the different approaches of recommendation systems; then define trust and its relationship with recommender systems. At the end the authors will present their approach to “trust-based recommendation systems”.FindingsThe purpose of this study is to understand how groups of users could improve trust in a recommendation system. The authors will examine how to evaluate the performance of recommender systems to ensure their ability to meet the needs that led to its creation and to make the system sustainable with respect to the information. The authors know very well that selecting a measure must depend on the type of data to be processed and user interests. Since the recommendation domain is derived from information search paradigms, it is obvious to use the evaluation measures of information systems.Originality/valueThe authors presented a list of recommendations systems. They examined and compared several recommendation approaches. The authors then analyzed the dominance of collaborative filtering in the field and the emergence of Recommender Systems in social web. Then the authors presented and analyzed different trust algorithms. Finally, their proposal was to measure the impact of trust in recommendation systems.

scholarly journals COMPARISON MODELS OF MACHINE LEARNING FOR MOVIE RECOMMENDATION SYSTEMS

2021 ◽  
Vol 335 (1) ◽  
pp. 26-31
Author(s):  
A.Y. Zhubatkhan ◽  
Z.A. Buribayev ◽  
S.S. Aubakirov ◽  
M.D. Dilmagambetova ◽  
S.A. Ryskulbek
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Systematic Approach ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
Travel Planning ◽  
System Recommendation ◽  
History Of ◽  
Movie Recommendation ◽  
The Right

The trend of the Internet makes the presentation of the right content for the right user inevitable. To this end, recommendation systems are used in areas such as music, books, movies, travel planning, e-commerce, education, and more. One of the most popular recommendation systems in the world is Netflix, which generated record profits during quarantine in the first quartile of 2020. The systematic approach of recommendations is based on the history of user selections, likes and reviews, each of which is interpreted to predict future user selections. This article provides a meaningful analysis of various recommendation systems, such as content-based, collaborative filtering and popularity. We reviewed 7 articles published from 2005 to 2019 to discuss issues related to existing models. The purpose of this article is to compare machine learning algorithms in the Surprise library for a recommendation system. Recommendation system has been implemented and quality has been evaluated using the MAE and RMSE metrics.

scholarly journals Development of machine learning algorithms for a recommendation system for selecting musical compositions

Connectivity ◽  
2020 ◽  
Vol 148 (6) ◽  
Author(s):  
D. D. Demydov ◽  
◽  
I. S. Shcherbyna ◽  
N. A. Trintina ◽  
A. M. Shtimmerman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Recommendation Systems ◽  
Main Tool ◽  
Machine Learning Algorithms ◽  
Separate Analysis ◽  
Data Set ◽  
Economic Point ◽  
Musical Compositions

The article analyzes the method of singular value decomposition (SVD) as an effective way to build a recommender system. With the development of information technologies and their introduction into public life, there is a need to search for accentuated information in conditions of uncertainty. To solve such problems, recently created intelligent recommendation systems [1]. The popularity of recommendation systems is growing in every segment of goods and services, in particular music. From a socio-economic point of view, such systems are the main tool for the dissemination of new compositions in the field of music promotes the promotion of these compositions in accordance with the preferences of the target audience and encourages users to purchase new music tracks. In addition, such systems significantly reduce the time and facilitate the search for appropriate musical compositions under conditions of uncertainty. The main problem in developing machine learning algorithms is the lack of an individual approach to each user. All recommendations are based on the statistical behavior of the majority, resulting in a percentage of people who do not receive recommendations that match their personal preferences. In the case of a separate analysis of each of the users and the implementation of recommendations in accordance with their personal use of Internet resources, the number of quality and more accurate proposals in the list of recommendations would increase significantly. Machine learning methods are effectively used to build recommendation systems, namely: the k-nearest neighbors method, the Bayesian algorithm and the singular matrix decomposition method. Among these methods, the SVD method is the most widely used in practice. This method is used to reduce the number of non-significant data set factors. Factors in recommendation systems are properties that describe the user or subject. In music recommendation systems, this can be a genre. SVD reduces the dimension of the matrix by removing its hidden factors.

scholarly journals COMPARISON MODELS OF MACHINE LEARNING FOR MOVIE RECOMMENDATION SYSTEMS

2021 ◽  
Vol 335 (1) ◽  
pp. 26-31
Author(s):  
A.Y. Zhubatkhan ◽  
Z.A. Buribayev ◽  
S.S. Aubakirov ◽  
M.D. Dilmagambetova ◽  
S.A. Ryskulbek
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Systematic Approach ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
Travel Planning ◽  
System Recommendation ◽  
History Of ◽  
Movie Recommendation ◽  
The Right

The trend of the Internet makes the presentation of the right content for the right user inevitable. To this end, recommendation systems are used in areas such as music, books, movies, travel planning, e-commerce, education, and more. One of the most popular recommendation systems in the world is Netflix, which generated record profits during quarantine in the first quartile of 2020. The systematic approach of recommendations is based on the history of user selections, likes and reviews, each of which is interpreted to predict future user selections. This article provides a meaningful analysis of various recommendation systems, such as content-based, collaborative filtering and popularity. We reviewed 7 articles published from 2005 to 2019 to discuss issues related to existing models. The purpose of this article is to compare machine learning algorithms in the Surprise library for a recommendation system. Recommendation system has been implemented and quality has been evaluated using the MAE and RMSE metrics.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

A feature-based hybrid recommender system for risk prediction : Machine learning approach (Preprint)

2020 ◽  
Author(s):  
Uzair Bhatti
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Predictive Accuracy ◽  
Correct Diagnosis ◽  
Recommendation Systems ◽  
Data Integrity ◽  
Machine Learning Algorithms ◽  
Patient Counseling ◽  
Hybrid Filtering ◽  
Novel Algorithm

BACKGROUND In the era of health informatics, exponential growth of information generated by health information systems and healthcare organizations demands expert and intelligent recommendation systems. It has become one of the most valuable tools as it reduces problems such as information overload while selecting and suggesting doctors, hospitals, medicine, diagnosis etc according to patients’ interests. OBJECTIVE Recommendation uses Hybrid Filtering as one of the most popular approaches, but the major limitations of this approach are selectivity and data integrity issues.Mostly existing recommendation systems & risk prediction algorithms focus on a single domain, on the other end cross-domain hybrid filtering is able to alleviate the degree of selectivity and data integrity problems to a better extent. METHODS We propose a novel algorithm for recommendation & predictive model using KNN algorithm with machine learning algorithms and artificial intelligence (AI). We find the factors that directly impact on diseases and propose an approach for predicting the correct diagnosis of different diseases. We have constructed a series of models with good reliability for predicting different surgery complications and identified several novel clinical associations. We proposed a novel algorithm pr-KNN to use KNN for prediction and recommendation of diseases RESULTS Beside that we compared the performance of our algorithm with other machine algorithms and found better performance of our algorithm, with predictive accuracy improving by +3.61%. CONCLUSIONS The potential to directly integrate these predictive tools into EHRs may enable personalized medicine and decision-making at the point of care for patient counseling and as a teaching tool. CLINICALTRIAL dataset for the trials of patient attached

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

Sign in / Sign up

Export Citation Format

Share Document