Graphs Resemblance based Software Birthmarks through Data Mining for Piracy Control

Сарвар, Сохаил; Кайум, Зия Уль; Сафьян, Мухаммад; Икбал, Муддессар; Махмуд, Ясир

doi:10.15514/ispras-2019-31(2)-12

Cited by 5 publications

(2 citation statements)

References 22 publications

(45 reference statements)

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“…Data mining aims to uncover patterns and other valuable information by sorting through large data sets [12]. As a discipline of computer science, its techniques include statistics, online analysis processing, intelligence retrieval, machine learning, expert system, and pattern recognition [13,14]. Typical data mining methods include decision tree, artificial neural network (ANN), support vector machine (SVM), and naive Bayes classifier.…”

Section: Data Miningmentioning

confidence: 99%

Learning Behavior Based on Data Mining Technology

Wang

Zhang

Guan

2022

Security and Communication Networks

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With the release of the Education Informatization 2.0 Action Plan and the rapid development of learning analysis technology, educational data mining becomes a new research direction. Data mining can improve teachers’ teaching methods and students’ learning skills by acquiring information hidden in the educational data. Based on the learning behavior data of college students, this paper uses BP neural network, a data mining method, to predict their comprehensive evaluation results. The results show that there is a close relationship between students’ learning behavior and their comprehensive scores. In addition, models of naive Bayes, logistic regression, and decision tree are established for verification and comparison. Compared with other models, BP neural network model has higher prediction accuracy and better performance. It can serve as an important basis to improve students’ learning methods and teachers’ teaching methods.

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Section: Data Miningmentioning

confidence: 99%

Learning Behavior Based on Data Mining Technology

Wang

Zhang

Guan

2022

Security and Communication Networks

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“…Обзор литературыВ этом разделе представлен краткий исследовательский обзор методов изучения и распознавания видов человеческой деятельности. В последнее время был проведен всесторонний анализ методов изучения видов деятельности наряду с методами моделирования сенсоров на основе машинного зрения и обучения, распознавания изображений и видео, а также мобильного распознавания активности с использованием сенсоров[32][33][34][35]. В работе[9] AR на основе данных комбинируется с методами на основе знаний для обработки неполных данных, поступающих от сенсоров.…”

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Machine Learning based Activity learning for Behavioral Contexts in Internet of Things

Сафьян¹,

Сарвар²,

Кайюм³

et al. 2021

Proceedings of ISP RAS

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Ontology based activity learning models play a vital role in diverse fields of Internet of Things (IoT) such as smart homes, smart hospitals or smart communities etc. The prevalent challenges with ontological models are their static nature and inability of self-evolution. The models cannot be completed at once and smart home inhabitants cannot be restricted to limit their activities. Also, inhabitants are not predictable in nature and may perform “Activities of Daily Life (ADL)” not listed in ontological model. This gives rise to the need of developing an integrated framework based on unified conceptual backbone (i.e. activity ontologies), addressing the lifecycle of activity recognition and producing behavioral models according to inhabitant’s routine. In this paper, an ontology evolution process has been proposed that learns particular activities from existing set of activities in daily life (ADL). It learns new activities that have not been identified by the recognition model, adds new properties with existing activities and learns inhabitant’s newest behavior of performing activities through Artificial Neural Network (ANN). The better degree of true positivity is evidence of activity recognition with detection of noisy sensor data. Effectiveness of proposed approach is evident from improved rate of activity learning, activity detection and ontology evolution.

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Clone detection in student programs based on lexical analysis of source codes

Половикова

Ivanova

2020

J. Phys.: Conf. Ser.

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This article presents the development of automatic clone detection for verification students programs - the Detector of Clones. The Levenshtein Distance is used for the initial assessment of the similarity of the two token sequences which is calculated by the Wagner-Fisher algorithm. Verification of two programs is carried out by the fingerprint method and by the winnowing method. To improve the results of plagiarism assessment, an additional calculation was added to the implementation of the winnowing method: the detector compares not only the labels themselves, but also the sequences near the matching labels. The detector of clones is formed for the cycle of disciplines Object Oriented Programming, Object Oriented Analysis and Design on the Moodle platform. These tools support C-like languages (C++ and C#). In general, the work of the detector undoubtedly made it possible to increase the consistency in verification of student programs.

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Graphs Resemblance based Software Birthmarks through Data Mining for Piracy Control

Cited by 5 publications

References 22 publications

Learning Behavior Based on Data Mining Technology

Learning Behavior Based on Data Mining Technology

Machine Learning based Activity learning for Behavioral Contexts in Internet of Things

Clone detection in student programs based on lexical analysis of source codes

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