Design of a Movie Review Rating Prediction (MR2P) Algorithm

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206461 ◽

2020 ◽

pp. 423-432

Author(s):

Oluwatofunmi Adetunji ◽

Mamudu Hadiza ◽

Nzechukwu Otuneme

Keyword(s):

Systematic Review ◽

Prediction Accuracy ◽

Research Work ◽

Movie Industry ◽

Recommendation Algorithm ◽

Movie Recommendation ◽

Rating Prediction ◽

Movie Success

<p>Entertainment is no longer just anything that we enjoy occasionally, with over two million spectators a day, the amount generated by the movie industry is huge. The movie sector is one of the biggest contributors to the entertainment industry’s unpredictability in success and failure. The aim of this research work to design an efficient movie recommendation algorithm that will increase prediction accuracy, the Movie Review Rating Prediction (MR2P) was achieved through a systematic review of the existing movie success algorithm. This research work will enable movie stakeholders (producers, directors, crew, cast already in the movie industry or aspirants) to know the kind of movie to invest in which will, in turn, be beneficial in terms of higher profit.</p>

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A Collaborative Filtering Recommendation Algorithm Based on Cloud Model, Item Attribute and User Data

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.610.747 ◽

2014 ◽

Vol 610 ◽

pp. 747-751

Author(s):

Jian Sun ◽

Xiao Ying Chen

Keyword(s):

Collaborative Filtering ◽

Prediction Accuracy ◽

Cloud Model ◽

Prediction Algorithm ◽

Recommendation Algorithm ◽

Rating Data ◽

User Data ◽

Item Attribute ◽

Rating Prediction ◽

User Ratings

Aiming at the problems of extremely sparse of user-item rating data and poor recommendation quality, we put forward a collaborative filtering recommendation algorithm based on cloud model, item attribute and user data which combined with the existing literatures. A rating prediction algorithm based on cloud model and item attribute is proposed, based on idea that the similar users rating for the same item are similar and the same user ratings for the similar items are similar and stable. Through compare and analysis this paper’s and other studies experimental results, we get the conclusion that the rating prediction accuracy is improved.

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Recommendation Systems for Education: Systematic Review

Electronics ◽

10.3390/electronics10141611 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1611

Author(s):

María Cora Urdaneta-Ponte ◽

Amaia Mendez-Zorrilla ◽

Ibon Oleagordia-Ruiz

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Formal Education ◽

Research Work ◽

Hybrid Approach ◽

Recommendation Systems ◽

Educational Resources ◽

Future Research ◽

Collaborative Approach ◽

Developmental Approach

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

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Traditional, phytochemical, and biological aspects of Rosa alba L.: a systematic review

Future Journal of Pharmaceutical Sciences ◽

10.1186/s43094-020-00132-z ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Anju Verma ◽

Rajni Srivastava ◽

Pankaj Kumar Sonar ◽

Ramprakash Yadav

Keyword(s):

Systematic Review ◽

Biological Activities ◽

Chemical Constituents ◽

Research Work ◽

Biological Properties ◽

Medicinal And Aromatic Plants ◽

Research Papers ◽

Northwest Africa ◽

The Family ◽

White Rose

Abstract Background Rosa alba L. belongs to the family Rosaceae. This species is widely cultivated in Europe, Asia, North America, and Northwest Africa due to its fragrance, ornamental, and medicinal values. It is commonly known as white oil-bearing rose, white rose, white rose of York, backyard rose, and sufaid gulab. Main text Rosa alba L. has many biological properties like antioxidant, antimicrobial, antifungal, antifertility, teratogenic, memory enhancing, cytotoxic, and genotoxic activities. The essential oil of Rosa alba L. possesses good antimicrobial activity and consists of many chemical constituents like- citronellol, geraniol, nerol, linalool, citral, carvacrol, eugenol, etc. Conclusion This article briefly reviews the cultivation, traditional uses, phytochemistry, and biological activities of Rosa alba L. Many research papers have been published on the proposed plant and still, there is a very vast scope of research on it. Therefore, this review will be very fruitful for those scientists who are doing or plan to do research work on this plant. All the scientific findings written in this review are explored from Google web, Google Scholar, PubMed, ScienceDirect, Medicinal and Aromatic Plants Abstracts (MAPA), and SciFinder. To date, it is the first systematic review article of such kind, on this plant.

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The effect of unilateral and bilateral laparoscopic surgery for endometriosis on Anti-Mullerian Hormone (AMH) level after 3 and 6 months: a systematic review and meta-analysis

Health and Quality of Life Outcomes ◽

10.1186/s12955-020-01561-3 ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Anisodowleh Nankali ◽

Mohsen Kazeminia ◽

Parnian Kord Jamshidi ◽

Shamarina Shohaimi ◽

Nader Salari ◽

...

Keyword(s):

Systematic Review ◽

Laparoscopic Surgery ◽

Growth Factors ◽

Web Of Science ◽

Meta Analysis ◽

Research Work ◽

Mean Difference ◽

Transient Growth ◽

Common Causes ◽

Standardized Mean Difference

Abstract Background Endometriosis is one of the most common causes of infertility. The causes of the disease and its definitive treatments are still unclear. Moreover, Anti-Mullerian Hormone (AMH) is a glycoprotein dimer that is a member of the transient growth factors family. This research work aimed to identify the effect of unilateral and bilateral laparoscopic surgery for endometriosis on AMH levels after 3 months, and 6 months, using meta-analysis. Methods In this study, the articles published in national and international databases of SID, MagIran, IranMedex, IranDoc, Cochrane, Embase, Science Direct, Scopus, PubMed, and Web of Science (ISI) were searched to find electronically published studies between 2010 and 2019. The heterogeneous index between studies was determined using the I2 index. Results In this meta-analysis and systematic review, 19 articles were eligible for inclusion in the study. The standardized mean difference was obtained in examining of unilateral laparoscopic surgery for endometriosis (before intervention 2.8 ± 0.11, and after 3 months 2.05 ± 0.13; and before intervention 3.1 ± 0.46 and after 6 months 2.08 ± 0.31), and in examining bilateral laparoscopic surgery for endometriosis examination (before intervention 2.0 ± 08.08, and after 3 months 1.1 ± 0.1; and before intervention 2.9 ± 0.23 and after 6 months 1.4 ± 0.19). Conclusion The results of this study demonstrate that unilateral and bilateral laparoscopic surgery for endometriosis is effective on AMH levels, and the level decreases in both comparisons.

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Simulation of Thermo-Mechanical Models for Hot Formed Parts by Numerical Experiments

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.663.668 ◽

2014 ◽

Vol 663 ◽

pp. 668-674

Author(s):

Azman Senin ◽

Zulkifli Mohd Nopiah ◽

Muhammad Jamhuri Jamaludin ◽

Ahmad Zakaria

Keyword(s):

Finite Element ◽

Finite Element Model ◽

Prediction Accuracy ◽

Research Work ◽

Deformation Characteristic ◽

Element Model ◽

Forming Limit ◽

Prototype Model ◽

New Production ◽

Element Analysis

The Finite-Element Analysis (FEA) is a prediction methodology that facilitates product designers produced the part design with manufacturing focused. With the similar advantages, manufacturing engineers are capable of build the first actual car model from the new production Draw Die. This approach has eliminated the requirement to manufacture the prototype model from soft tool parts and soft tool press die. However, the prediction accuracy of FEA is a major topic of research work in automotive sector's practitioners and academia as current accuracy level is anticipated at 60%. The objective of works is to assess the prediction accuracy on deformation results from mass production stamped parts. The Finite-element model is developed from the CAD data of the production tools. Subsequently, finite-element model for production tools is discretized with shell elements to avoid computation errors in the simulation process. The sheet blank material with 1.5 mm and 2.0 mm thickness is discredited by shell (2D modeling) and solid elements (3D modeling) respectively. The input parameters for the simulation model for both elements are attained from the actual setup at Press Machine and Production Tool. The analysis of deformation and plastic strain are performed for various setup parameters. Finally, the deformation characteristic such as Forming Limit Diagram (FLD) and thinning are compared for all simulated models.

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Learning Non-Taxonomic Relations of Ontologies

International Journal on Semantic Web and Information Systems ◽

10.4018/ijswis.2021010105 ◽

2021 ◽

Vol 17 (1) ◽

pp. 97-122

Author(s):

Mohamed Hassan Mohamed Ali ◽

Said Fathalla ◽

Mohamed Kholief ◽

Yasser Fouad Hassan

Keyword(s):

Systematic Review ◽

State Of The Art ◽

Research Work ◽

Semantic Knowledge ◽

Future Research ◽

Automatic Extraction ◽

Ontology Learning ◽

Comprehensive Understanding ◽

Specific Domain ◽

Taxonomic Relations

Ontologies, as semantic knowledge representation, have a crucial role in various information systems. The main pitfall of manually building ontologies is effort and time-consuming. Ontology learning is a key solution. Learning Non-Taxonomic Relationships of Ontologies (LNTRO) is the process of automatic/semi-automatic extraction of all possible relationships between concepts in a specific domain, except the hierarchal relations. Most of the research works focused on the extraction of concepts and taxonomic relations in the ontology learning process. This article presents the results of a systematic review of the state-of-the-art approaches for LNTRO. Sixteen approaches have been described and qualitatively analyzed. The solutions they provide are discussed along with their respective positive and negative aspects. The goal is to provide researchers in this area a comprehensive understanding of the drawbacks of the existing work, thereby encouraging further improvement of the research work in this area. Furthermore, this article proposes a set of recommendations for future research.

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Comparison of measures of collaborative filtering recommender systems: rating prediction accuracy versus usage prediction accuracy

2017 International Conference on Innovations in Control, Communication and Information Systems (ICICCI) ◽

10.1109/iciccis.2017.8660946 ◽

2017 ◽

Author(s):

Rohit ◽

Anil Kumar Singh

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Prediction Accuracy ◽

Rating Prediction

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The prevalence of stress, anxiety and depression within front-line healthcare workers caring for COVID-19 patients: a systematic review and meta-regression

Human Resources for Health ◽

10.1186/s12960-020-00544-1 ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Nader Salari ◽

Habibolah Khazaie ◽

Amin Hosseinian-Far ◽

Behnam Khaledi-Paveh ◽

Mohsen Kazeminia ◽

...

Keyword(s):

Systematic Review ◽

Sample Size ◽

Healthcare Workers ◽

Meta Analysis ◽

Research Work ◽

Hospital Staff ◽

Anxiety And Depression ◽

Front Line ◽

Prevalence Of Depression ◽

Meta Regression

Abstract Background Stress, anxiety, and depression are some of the most important research and practice challenges for psychologists, psychiatrists, and behavioral scientists. Due to the importance of issue and the lack of general statistics on these disorders among the Hospital staff treating the COVID-19 patients, this study aims to systematically review and determine the prevalence of stress, anxiety and depression within front-line healthcare workers caring for COVID-19 patients. Methods In this research work, the systematic review, meta-analysis and meta-regression approaches are used to approximate the prevalence of stress, anxiety and depression within front-line healthcare workers caring for COVID-19 patients. The keywords of prevalence, anxiety, stress, depression, psychopathy, mental illness, mental disorder, doctor, physician, nurse, hospital staff, 2019-nCoV, COVID-19, SARS-CoV-2 and Coronaviruses were used for searching the SID, MagIran, IranMedex, IranDoc, ScienceDirect, Embase, Scopus, PubMed, Web of Science (ISI) and Google Scholar databases. The search process was conducted in December 2019 to June 2020. In order to amalgamate and analyze the reported results within the collected studies, the random effects model is used. The heterogeneity of the studies is assessed using the I2 index. Lastly, the data analysis is performed within the Comprehensive Meta-Analysis software. Results Of the 29 studies with a total sample size of 22,380, 21 papers have reported the prevalence of depression, 23 have reported the prevalence of anxiety, and 9 studies have reported the prevalence of stress. The prevalence of depression is 24.3% (18% CI 18.2–31.6%), the prevalence of anxiety is 25.8% (95% CI 20.5–31.9%), and the prevalence of stress is 45% (95% CI 24.3–67.5%) among the hospitals’ Hospital staff caring for the COVID-19 patients. According to the results of meta-regression analysis, with increasing the sample size, the prevalence of depression and anxiety decreased, and this was statistically significant (P < 0.05), however, the prevalence of stress increased with increasing the sample size, yet this was not statistically significant (P = 0.829). Conclusion The results of this study clearly demonstrate that the prevalence of stress, anxiety and depression within front-line healthcare workers caring for COVID-19 patients is high. Therefore, the health policy-makers should take measures to control and prevent mental disorders in the Hospital staff.

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