scholarly journals A Novel Rule based Data Mining Approach towards Movie Recommender System

2020 ◽  
Vol 44 (1) ◽  
pp. 157-170
Author(s):  
Mugdha Sharma ◽  
Laxmi Ahuja ◽  
Vinay Kumar
Keyword(s):  
Recommender System ◽  
Recommendation System ◽  
Phase 1 ◽  
Research Work ◽  
Rule Learning ◽  
Rule Base ◽  
Individual Member ◽  
Phase 2 ◽  
Group Recommendation ◽  
Rule Based

The proposed research work is an effort to provide accurate movie recommendations to a group of users with the help of a rule-based content-based group recommender system. The whole approach is categorized into 2 phases. In phase 1, a rule- based approach has been proposed which considers the users’ viewing history to provide the Rule Base for every individual user. In phase 2, a novel group recommendation system has been proposed which considers the ratings of the movies as per the rule base generated in phase 1. Phase 2 also considers the weightage of every individual member of the group to provide the accurate movie recommendation to that particular group of users. The results of experimental setup also establish the fact that the proposed system provides more accurate outcomes in terms of precision and recall over other rule learning algorithms such as C4.5.

scholarly journals Product Recommendation System: A Systematic Literature Review

2021 ◽  
Vol 9 (VII) ◽  
pp. 3330-3339
Author(s):  
Ketki Kinkar
Keyword(s):  
Literature Review ◽  
Performance Measures ◽  
Systematic Literature Review ◽  
Recommender System ◽  
Recommendation System ◽  
Consumer Preferences ◽  
Research Work ◽  
Product Recommendation ◽  
System A ◽  
Scientific Papers

In today's world, we find a wide variety of search options and we may have difficulty selecting what we really need. The recommendation System plays an important part in dealing with these problems. A recommender system is a framework that is a filtering system that filters the data with various algorithms and recommends the user with the most relevant data. Recommendation systems are productive customization mechanisms, often up-to-date and recommendations based on current consumer preferences. These systems have shown to be extremely helpful in different areas of e-commerce, education, movies, music, books, films, scientific papers, and various products. This paper reviews many approaches of recommendation techniques with their upsides and downsides and diverse performance measures. We have reviewed various articles, analyzed their technique and approach, major features of the algorithm utilized, and potential areas for improvement in that research work.

scholarly journals Enhancing Service Life of Power Transformer through Inhibiting the Degradation of Insulating oil by New Approach

2020 ◽  
Vol 7 (10) ◽  
pp. 369-374
Author(s):  
Yasir Khan ◽  
◽  
Muhammad Iftikhar Khan ◽  
Keyword(s):  
Phase 1 ◽  
Research Work ◽  
Transformer Oil ◽  
Phase 2 ◽  
Distribution Transformer ◽  
Insulation System ◽  
Experimental Process ◽  
Two Phases ◽  
Paper Insulation ◽  
Monitoring And Measurement

Oil reclamation is a transformer insulation reconditioning technique which may be used as on-line or off-line. However, there is a need of evidence showing the effect of this process on conditions of the paper insulation, which indeed affects the life span of a transformer. This research work focuses on oil reclamation experiment on an old retired distribution transformer. Electrical testing and post-mortem analysis of the transformer were conducted, aimed at investigating the design aspects and collecting information on the insulation conditions prior to the oil reclamation. Temperature and moisture sensors were installed to monitor the conditions within the transformer during the oil reclamation [2]. The experimental process of transformer Oil reclamation was performed into two phases, with regular oil sampling to analyze the changes in key oil parameters, namely acidity number (AN), moisture and breakdown voltage (BV). This was accompanied by paper sampling at the end of each reclamation cycle to study the effects of oil reclamation on properties, particularly moisture, LMA and degree of polymerization. The transformer which was used for the entire experimental process was about 45 years old, 200kVA, 1100 / 415-240V distribution transformer. In order to study the long-term effect of oil reclamation, oil samples were collected from an on-site reclamation exercise performed in a laboratory-accelerated thermal ageing experiment. Oil samples collected before and after the reclamation were aged alongside new oils for comparison [4]. Through the regular monitoring and measurement of oil parameters (AN, moisture and BD strength) over 144 hours and paper parameters (LMA, moisture and DP) at specific stages of phase 1, it was observed that the transformer oil-paper insulation system was significantly improved. The entire research work was performed into two phases (phase 1 and phase 2). The “phase 1” was aimed to improve and restore the oil parameters comparable to the parameters of new oil as specified in “IEC-60296” and its effect on the paper insulation. “Phase 2” was aimed to compare the life span of reclaimed oil filled transformer with the transformer in which aged oil has been replaced by new oil[6]. Effective study of oil reclamation was analyzed through laboratory accelerated aging experiment and real time application on a 45 years old transformer. Through the regular monitoring and measurement of oil parameters (AN, moisture and BD strength) over 144 hours and paper parameters (LMA, moisture and DP) at specific stages of phase1, it was observed that the transformer oil-paper insulation system was significantly improved [15].

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 Comparative Study of Machine Learning Algorithms for Recommendation System

2020 ◽  
Vol 22 (12) ◽  
Author(s):  
Sonam Singh ◽  
◽  
Kriti Srivastva ◽  
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Research Work ◽  
Previous History ◽  
Machine Learning Algorithms ◽  
The Given ◽  
User History

The role of recommender system is very vital in recent times for a lot of individuals. It helps in taking decisions without exploring physically. Broadly there are two types of recommender system: Content based and Collaborative Filtering. The first one focus on user’s history and takes decisions. But there could be times when decisions based on only user history is not sufficient. For this, there is a need to analyze many parameters influencing the decision such as previous history, Age, gender, location etc. In the second approach it finds similar group of users based on several parameters and then takes decisions. Over the last few decades machine learning algorithms have proved their worth in this area because of their ability to learn from the given data and identify various hidden patterns. With this learning, these algorithms are able to generalize very well for unknown data. In this research work, a survey on three different machine learning based collaborative filtering methods are presented using Movie Lens dataset. The comparison of all three methods based on RMSE and MAE error is also discussed.

scholarly journals Group Recommendation for Cold Start Users using Hybrid Recommendation Technique

2019 ◽  
Vol 8 (5S3) ◽  
pp. 452-456
Keyword(s):  
Recommender System ◽  
Recommendation System ◽  
Retrieval System ◽  
Data Retrieval ◽  
Cold Start ◽  
Group Recommendation ◽  
Huge Amount ◽  
New Era ◽  
The Social ◽  
Hybrid Recommendation

Recommender system is an data retrieval system that gives customers the recommendations for the items that a customer may be willing to have. It helps in making the search easy by sorting the huge amount of data. We have progressed from the era of paucity to the new era of plethora due to which there is lot of development in the recommender system. In today’s scenario the interaction between the groups of friends, family or colleagues has increased due to the advancement in mobile devices and the social media. So, group recommendation has become a necessity in all kinds of domains. In this paper a system has been proposed using the group recommendation system based on hybrid based filtering method to overcome the cold start user issue which arises when a new user signs in and he/she doesn’t have any past records. So, the recommender system does not have enough information related to the user to recommend an item which will be of his/her interest. The dataset has been taken from the MovieLens is used in the experiment.

Rationalization and internalization

2001 ◽  
Vol 60 (4) ◽  
pp. 215-230 ◽  
Author(s):  
Jean-Léon Beauvois
Keyword(s):  
Phase 1 ◽  
Facilitatory Effect ◽  
Phase 2 ◽  
Causal Explanations ◽  
Reduction Effect ◽  
Dissonance Reduction

After having been told they were free to accept or refuse, pupils aged 6–7 and 10–11 (tested individually) were led to agree to taste a soup that looked disgusting (phase 1: initial counter-motivational obligation). Before tasting the soup, they had to state what they thought about it. A week later, they were asked whether they wanted to try out some new needles that had supposedly been invented to make vaccinations less painful. Agreement or refusal to try was noted, along with the size of the needle chosen in case of agreement (phase 2: act generalization). The main findings included (1) a strong dissonance reduction effect in phase 1, especially for the younger children (rationalization), (2) a generalization effect in phase 2 (foot-in-the-door effect), and (3) a facilitatory effect on generalization of internal causal explanations about the initial agreement. The results are discussed in relation to the distinction between rationalization and internalization.

PENERAPAN MODEL QUANTUM LEARNING UNTUK MENINGKATKAN HASIL BELAJAR AUTOCAD SISWA KELAS X TEKNIK PEMESINAN SMK NEGERI 1 STABAT

2013 ◽  
Vol 5 (1) ◽  
Author(s):  
Abdul Hasan Saragih
Keyword(s):  
Positive Response ◽  
Phase 1 ◽  
First Grade ◽  
Learning Model ◽  
Second Phase ◽  
Phase 2 ◽  
Phase 3 ◽  
The Third ◽  
Third Phase ◽  
Quantum Learning

This classroom research was conducted on the autocad instructions to the first grade of mechinary class of SMK Negeri 1 Stabat aiming at : (1) improving the student’ archievementon autocad instructional to the student of mechinary architecture class of SMK Negeri 1 Stabat, (2) applying Quantum Learning Model to the students of mechinary class of SMK Negeri 1 Stabat, arising the positive response to autocad subject by applying Quantum Learning Model of the students of mechinary class of SMK Negeri 1 Stabat. The result shows that (1) by applying quantum learning model, the students’ achievement improves significantly. The improvement ofthe achievement of the 34 students is very satisfactory; on the first phase, 27 students passed (70.59%), 10 students failed (29.41%). On the second phase 27 students (79.41%) passed and 7 students (20.59%) failed. On the third phase 30 students (88.24%) passed and 4 students (11.76%) failed. The application of quantum learning model in SMK Negeri 1 Stabat proved satisfying. This was visible from the activeness of the students from phase 1 to 3. The activeness average of the students was 74.31% on phase 1,81.35% on phase 2, and 83.63% on phase 3. (3) The application of the quantum learning model on teaching autocad was very positively welcome by the students of mechinary class of SMK Negeri 1 Stabat. On phase 1 the improvement was 81.53% . It improved to 86.15% on phase 3. Therefore, The improvement ofstudent’ response can be categorized good.

In situ deteriorating patient simulation in general practice

2020 ◽  
Vol 70 (suppl 1) ◽  
pp. bjgp20X711425
Author(s):  
Joanna Lawrence ◽  
Petronelle Eastwick-Field ◽  
Anne Maloney ◽  
Helen Higham
Keyword(s):  
Life Support ◽  
Early Recognition ◽  
Phase 1 ◽  
Patient Simulation ◽  
Medical Emergency ◽  
Phase 2 ◽  
Action Plans ◽  
Integrated Simulation ◽  
Deteriorating Patient

BackgroundGP practices have limited access to medical emergency training and basic life support is often taught out of context as a skills-based event.AimTo develop and evaluate a whole team integrated simulation-based education, to enhance learning, change behaviours and provide safer care.MethodPhase 1: 10 practices piloted a 3-hour programme delivering 40 minutes BLS and AED skills and 2-hour deteriorating patient simulation. Three scenarios where developed: adult chest pain, child anaphylaxis and baby bronchiolitis. An adult simulation patient and relative were used and a child and baby manikin. Two facilitators trained in coaching and debriefing used the 3D debriefing model. Phase 2: 12 new practices undertook identical training derived from Phase 1, with pre- and post-course questionnaires. Teams were scored on: team working, communication, early recognition and systematic approach. The team developed action plans derived from their learning to inform future response. Ten of the 12 practices from Phase 2 received an emergency drill within 6 months of the original session. Three to four members of the whole team integrated training, attended the drill, but were unaware of the nature of the scenario before. Scoring was repeated and action plans were revisited to determine behaviour changes.ResultsEvery emergency drill demonstrated improved scoring in skills and behaviour.ConclusionA combination of: in situ GP simulation, appropriately qualified facilitators in simulation and debriefing, and action plans developed by the whole team suggests safer care for patients experiencing a medical emergency.

Using Gleams to Evaluate the Agricultural Waste Application Rule-Based Decision Support (AWARDS) Computer Program

1993 ◽  
Vol 28 (3-5) ◽  
pp. 625-634 ◽  
Author(s):  
D. A. Ford ◽  
A. P. Kruzic ◽  
R. L. Doneker
Keyword(s):  
Surface Water ◽  
Ground Water ◽  
Management Practices ◽  
Agricultural Waste ◽  
Rule Base ◽  
Rule Based ◽  
Pollutant Loading ◽  
Physical Features ◽  
To Receive ◽  
Complex Computer

AWARDS is a rule-based program that uses artificial intelligence techniques. It predicts the potential for fields receiving agricultural waste applications to degrade water quality. Input data required by AWARDS include the physical features, management practices, and crop nutrient needs for all fields scheduled to receive these nutrients. Based on a series of rules AWARDS analyzes the data and categorizes each field as acceptable or unacceptable for agricultural waste applications. The acceptable fields are then ranked according to their potential for pollutant loading. To evaluate the validity of the AWARDS field ranking system, it was compared to pollutant loading output from GLEAMS, a complex computer model. GLEAMS simulated the characteristics of each field ranked by AWARDS. Comparison of the AWARDS field ranking to the GLEAMS pollutant loading was favorable where ground water and both surface and ground water were to be protected and less favorable where surface water was to be protected. The rule base in AWARDS may need to be refined to provide more reasonable results where surface water is the resource of concern.

Clinical Drug Development for the Treatment of Pulmonary Arterial Hypertension: Collaboration With the Pharmaceutical Industry and Regulatory Agencies

2010 ◽  
Vol 9 (4) ◽  
pp. 214-219
Author(s):  
Robyn J. Barst
Keyword(s):  
Clinical Trials ◽  
Pulmonary Arterial Hypertension ◽  
Drug Development ◽  
Phase 1 ◽  
Preclinical Studies ◽  
Phase 2 ◽  
Phase 3 ◽  
Preclinical Testing ◽  
New Drug ◽  
Pulmonary Arterial

Drug development is the entire process of introducing a new drug to the market. It involves drug discovery, screening, preclinical testing, an Investigational New Drug (IND) application in the US or a Clinical Trial Application (CTA) in the EU, phase 1–3 clinical trials, a New Drug Application (NDA), Food and Drug Administration (FDA) review and approval, and postapproval studies required for continuing safety evaluation. Preclinical testing assesses safety and biologic activity, phase 1 determines safety and dosage, phase 2 evaluates efficacy and side effects, and phase 3 confirms efficacy and monitors adverse effects in a larger number of patients. Postapproval studies provide additional postmarketing data. On average, it takes 15 years from preclinical studies to regulatory approval by the FDA: about 3.5–6.5 years for preclinical, 1–1.5 years for phase 1, 2 years for phase 2, 3–3.5 years for phase 3, and 1.5–2.5 years for filing the NDA and completing the FDA review process. Of approximately 5000 compounds evaluated in preclinical studies, about 5 compounds enter clinical trials, and 1 compound is approved (Tufts Center for the Study of Drug Development, 2011). Most drug development programs include approximately 35–40 phase 1 studies, 15 phase 2 studies, and 3–5 pivotal trials with more than 5000 patients enrolled. Thus, to produce safe and effective drugs in a regulated environment is a highly complex process. Against this backdrop, what is the best way to develop drugs for pulmonary arterial hypertension (PAH), an orphan disease often rapidly fatal within several years of diagnosis and in which spontaneous regression does not occur?

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