Navigation through Citation Network Based on Content Similarity Using Cosine Similarity Algorithm

2016 ◽  
Vol 9 (5) ◽  
pp. 9-20 ◽  
Author(s):  
Abdul Ahad ◽  
Muhammad Fayaz ◽  
Abdul Salam Shah
Author(s):  
Kithsiri Jayakodi ◽  
Madhushi Bandara ◽  
Indika Perera ◽  
Dulani Meedeniya

Assessment usually plays an indispensable role in the education and it is the prime indicator of student learning achievement. Exam questions are the main form of assessment used in learning. Setting appropriate exam questions to achieve the desired outcome of the course is a challenging work for the examiner. Therefore this research is mainly focused to categorize the exam questions automatically into its learning levels using Bloom’s taxonomy. Natural Language Processing (NLP) techniques such as tokenization, stop word removal, lemmatization and tagging were used before generating the rule set to be used for this classification. WordNet similarity algorithms with NLTK and cosine similarity algorithm were developed to generate a unique set of rules to identify the question category and the weight for each exam question according to Bloom’s taxonomy. These derived rules make it easy to analyze the exam questions. Evaluators can redesign their exam papers based on the outcome of the evaluation process. A sample of examination questions of the Department of Computing and Information Systems, Wayamba University, Sri Lanka was used for the evaluation; weight assignment was done based on the total value generated from both WordNet algorithm and the cosine algorithm. Identified question categories were confirmed by a domain expert. The generated rule set indicated over 70% accuracy.


2020 ◽  
Vol 4 (3) ◽  
pp. 48
Author(s):  
Muhammad Habibi ◽  
Puji Winar Cahyo

One of the problems related to journal publishing is the process of categorizing entry into journals according to the field of science. A large number of journal documents included in a journal editorial makes it difficult to categorize so that the process of plotting to reviewers requires a long process. The review process in a journal must be done planning according to the expertise of the reviewer, to produce a quality journal. This study aims to create a classification model that can classify journals automatically using the Cosine Similarity algorithm and Support Vector Machine in the classification process and using the TF-IDF weighting method. The object of this research is abstract in scientific journals. The journals will be classified according to the reviewer's field of expertise. Based on the experimental results, the Support Vector Machine method produces better performance accuracy than the Cosine Similarity method. The results of the calculation of the value of precision, recall, and f-score are known that the Support Vector Machine method produces better amounts, in line with the accuracy value.


2021 ◽  
Vol 5 (4) ◽  
pp. 529
Author(s):  
Fajar Agung Nugroho ◽  
Fajar Septian ◽  
Dimas Abisono Pungkastyo ◽  
Joko Riyanto

Research and community service activities are the obligations of a lecturer that must be carried out from part of the Tri Dharma of Higher Education in addition to teaching, where research activities should have a level of innovation in the form of development or discovery of something new, but with a large number of lecturers, this results in research activities. and community service has many similarities with previous activities. At the Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM), Pamulang University, experiencing several problems in the management of research and community service activities, namely the absence of a system used to manage research and community service activities and data related to the track record of research and community service activities that have been carried out by 2,613 lecturers who impact on the difficulty in finding data, efficiency of storage space and more importantly is the number of similar proposals in the research itself. The research carried out aims to develop an information system that can process research and community service activities and detect similarities in content by applying the Cosine Similarity algorithm, so that it can overcome existing problems. The system development method uses a waterfall. From the results of making the system that has been carried out, it shows that the system is capable of processing activities in the field of research and community service carried out by lecturers, supporting storage, and facilitating screening of proposals for research and community service activities that will be approved.


2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Xiurui Zhu ◽  
Shisheng Su ◽  
Mingzhu Fu ◽  
Junyuan Liu ◽  
Lingxiang Zhu ◽  
...  

2020 ◽  
Vol 9 (1) ◽  
pp. 1344-1349

In this data-driven world, AI is being used in almost all the tasks to automate processes and make human life more comfortable. One such industry where Artificial Intelligence (AI) importance is growing is the recruiting industry. This paper aims to propose a new and a better method to match the most suitable talent to jobs, which has been incorporated using two methods – suggesting top resumes to a job opening from a talent pool to a recruiter, recommending top jobs which match to a candidate based on the candidate's resume. Natural Language Processing techniques have been used in implementing this approach – Named Entity Recognition (NER), Word embedding model, and Cosine similarity using which a resume and job will be matched. The NER model is used to extract useful entities from documents, which is enhanced by the word2vec model by making the system more generic and the similarity is calculated using the cosine similarity algorithm.


2021 ◽  
Vol 5 (2) ◽  
pp. 106-114
Author(s):  
Muhamad Aldi Rifai ◽  
Indra Gita Anugrah

The activity of writing scientific articles by academics at universities is one of the activities that is often carried out, but when writing scientific articles problems arise regarding the difficulty of finding ideas, literature studies, and reference sources that you want to use as references when writing. Sometimes when searching on a search engine, we have trouble finding the right document, because usually, the keywords we are looking for are not in the title section but another part of the structure. Since most search engines only match titles, other structures are usually excluded from matching. So that the search results that we do sometimes don't match what we want. In addition, usually, each scientific article has many language differences in its structure as found in the abstract section. To detect similarities through the structure of scientific articles, an algorithm is used, namely weighted tree similarity, and to detect language using the N-gram algorithm, then the cosine similarity algorithm can be used to check the level of similarity in keyword text with text in scientific articles.


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