International Journal of Advanced Networking and Applications

Hadiza Lawal Abba ¹, Abubakar Roko ², Aminu B. Muhammad ², Abdulgafar Usman ³, Abba Almu ²

Affiliations
1 Kofar kaura layout kastina, State, NG
2 Department of Computer Science, Usmanu Danfodiyo University, Sokoto, NG
3 Ministry of Finance Economic and Development, Damaturu, Yobe State, NG

Abstract

Even though there are various source code plagiarism detection approaches, most of them are only concerned with lexical similarities attack with an assumption that plagiarism is only conducted by students who are not proficient in programming. However, plagiarism is often conducted not only due to student incapability but also because of bad time management. Thus, semantic similarity attacks should be detected and evaluated. This research proposes a source code semantic similarity detection approach that can detect most source code similarities by representing the source code into an Abstract Syntax Tree (AST) and evaluating similarity using a Siamese neural network. Since AST is a language-dependent feature, the SOCO dataset is selected which consists of C++ program codes. Based on the evaluation, it can be concluded that our approach is more effective than most of the existing systems for detecting source code plagiarism. The proposed strategy was implemented and an experimental study based on the AI-SOCO dataset revealed that the proposed similarity measure achieved better performance for the recommendation system in terms of precision, recall, and f1 score by 15%, 10%, and 22% respectively in the 100,000 datasets. In the future, it is suggested that the system can be improved by detecting inter-language source code similarity.

Keywords

Source Code, Lexical plagiarism, Semantic neural network.

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Abubakar Roko ¹, Aminu Lawal ¹, Aminu B. Muhammad ¹, Abdulgafar Usman ¹

Affiliations
1 Department of Computer Science, UsmanuDanfodiyo University, Sokoto., NG

Abstract

In content-based 3D object retrieval, searching for a query object in an extensive database is essential. The existing retrieval algorithms adopt the naïve search algorithm in searching for queryobjects. This approach leads to a high cost of search and retrieval that needs to be addressed. In this research, we introduced an algorithm that calculates each cluster’s representative, that is, a 3D object that has the least dissimilarity on average to each object in the cluster. This is to improve the overall retrieval performance of [1]. We first compute the optimal hierarchical level of the database using a dendrogram, and then calculate the total number of clusters in the database. Afterwards, we calculate the feature descriptor of each cluster. When a user chooses a query object, our system then compares the feature descriptor of the query object with each of the cluster ’s representation and search the cluster with the smallest distance to the query, thereby improving the querysearching and improving the system. The proposed system was implemented, and the system's performance was evaluated against the benchmark datasets. This revealed that the execution time was reduced by 21% and increased Precision and Recall by 30.7% and 33.1%, respectively. In the future, it is suggested that the technique be improved by incorporating different machine learning algorithms and comparing the results.

Keywords

3D Object Retrieval, Hierarchical Clustering, Cluster Representative.

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Abdulgafar Usman ¹, Abubakar Roko ¹, Aminu B. Muhammad ¹, Abba Almu ¹

Affiliations
1 Department of Computer Science, Usmanu Danfodiyo University, Sokoto., NG

Abstract

Recommender systems (Rs) are widely used to provide recommendations for a collection of items or products that may be of interest to user or a group of users. Because of its superior performance, Content-Based Filtering (CBF) is one of the approaches that are commonly utilized in real-world Rs using Time-Frequency and Inverse Document Frequency (TF-IDF) to calculate document similarities. However, it computesdocument similarity directly in the word-count space. We propose a user-based collaborative filtering (UBCF) method to solve the problem of limited in content analysis which leads to a low prediction rate for large vocabulary. In this study, we present an algorithm that utilises Euclidean distance similarity function, to solve the identified problem. Th e performance of the proposed scheme was evaluated against the benchmark scheme usingdifferent performance metrics. The proposed scheme was implemented and an experimentally tested by using the benchmark datasets (Amazon review datasets). The results revealed that, the proposed scheme achieved better performance than the existing recommender system in terms of Root Mean Square Error (RMSE) which reduces the errors by 29% and also increase the Precision and Recall by 51.4%, and 55.8% respectively in the 1 million datasets.

Keywords

Recommender System, Content-Based, Collaborative Filtering, Personalized Recommendations, Similarity Function.

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