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1

ANN Modelling on Vulnerabilities Detection in Code Smells-Associated Android Applications

Gupta Aakanshi, Sharma Deepanshu, Phulli Kritika

Foundations of Computing and Decision Sciences

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2022

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Vol. 47, No. 1

3--26

EN

There has been a lot of software design concerns in recent years that come under the code smell. Android Applications Developments experiences more security issues related to code smells that lead to vulnerabilities in software. This research focuses on the vulnerability detection in Android applications which consists of code smells. A multi-layer perceptron-based ANN model is generated for detection of software vulnerabilities and has a precision value of 74.7% and 79.6% accuracy with 2 hidden layers. The focus is laid on 1390 Android classes and involves association mining of the software vulnerabilities with android code smells using APRIORI algorithm. The generated ANN model The findings represent that Member Ignoring Method (MIM) code smell shows an association with Bean Member Serialization (BMS) vulnerability having 86% confidence level and 0.48 support value. An algorithm has also been proposed that would help developers in detecting software vulnerability in the smelly source code of an android applications at early stages of development.

2

Set representation for rule-generation algorithms

Kharkongor Carynthia, Nath Bhabesh

Computer Science

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2022

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T. 23 (2)

205--225

EN

The task of mining association rules has become one of the most widely used discovery pattern methods in knowledge discovery in databases (KDD). One such task is to represent an item set in the memory. The representation of the item set largely depends on the type of data structure that is used for storing them. Computing the process of mining an association rule impacts the memory and time requirements of the item set. With the constant increase of the dimensionality of data and data sets, mining such a large volume of data sets will be difficult since all of these item sets cannot be placed in the main memory. As the representation of an item set greatly affects the efficiency of the rule-mining association, a compact and compressed representation of the item set is needed. In this paper, a set representation is introduced that is more memory- and cost-efficient. Bitmap representation takes 1 byte for an element, but a set representation uses 1 bit. The set representation is being incorporated in the Apriori algorithm. Set representation is also being tested for different rule-generation algorithms. The complexities of these different rule-generation algorithms that use set representation are being compared in terms of memory and time of execution.

3

Finding frequent items: novel method for improving Apriori algorithm

Karimtabar Noorollah, Fard Mohammad Javad Shayegan

Computer Science

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2022

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T. 23 (2)

161--177

EN

In this paper, we use an intelligent method for improving the Apriori algorithm in order to extract frequent itemsets. PAA (the proposed Apriori algorithm) pursues two goals: first, it is not necessary to take only one data item at each step – in fact, all possible combinations of items can be generated at each step; and second, we can scan only some transactions instead of scanning all of the transactions to obtain a frequent itemset. For performance evaluation, we conducted three experiments with the traditional Apriori, BitTableFI, TDM-MFI, and MDC-Apriori algorithms. The results exhibited that the algorithm execution time was significantly reduced due to the significant reduction in the number of transaction scans to obtain the itemset. As in the first experiment, the time that was spent to generate frequent items underwent a reduction of 52% as compared to the algorithm in the first experiment. In the second experiment, the amount of time that was spent was equal to 65%, while in the third experiment, it was equal to 46%.

4

Application of apriori algorithm in the lamination process in yacht production

Niksa-Rynkiewicz Tacjana, Landowski Michal, Szalewski Paweł

Polish Maritime Research

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2020

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nr 3

59--70

EN

The article specifies the dependence of defects occurring in the lamination process in the production of yachts. Despite great knowledge about their genesis, they cannot be completely eliminated. Authentic data obtained through cooperation with one of the Polish yacht shipyards during the years 2013–2017 were used for the analysis. To perform a simulation, the sample size was observed in 1450 samples, consisting of 6 models of yachts with closed and open deck. Finding the dependence of the occurrence of specific defects will allow for faster procedures and more effective quality control, which will contribute to lower costs. The use of new methods based on artificial intelligence related to Big Data allows for easier observation of dependencies in the complex structure of data from yacht production. The association rules were defined using the algorithm Apriori and define interdependent defects. A number of dependencies were found for the occurrence of production defects not obvious to technologists, but occurring with a high probability of coexistence. The presented research results may allow the planning process of production tasks to be improved.

5

Application of data mining techniques to find relationships between the dishes offered by a restaurant for the elaboration of combos based on the preferences of the diners

Vazquez Rosa Maria, Bonilla Edmundo, Sanchez Eduardo, Atriano Oscar, Berruecos Cinthya

Applied Computer Science

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2019

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Vol. 15, no 2

73--88

EN

Currently, blended food has been a common menu item in fast food restaurants. The sales of the fast-food industry grow thanks to several sales strategies, including the “combos”, so, specialty, regional, family and buffet restaurants are even joining combos’ promotions. This research paper presents the implementation of a system that will serve as support to elaborate combos according to the preferences of the diners using data mining techniques to find relationships between the different dishes that are offered in a restaurant. The software resulting from this research is being used by the mobile application Food Express, with which it communicates through webservices. References

6

Apriori-Based Rule Generation in Incomplete Information Databases and Non-Deterministic Information Systems

Sakai H., Wu M., Nakata M.

Fundamenta Informaticae

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2014

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Vol. 130, nr 3

343--376

EN

This paper discusses issues related to incomplete information databases and considers a logical framework for rule generation. In our approach, a rule is an implication satisfying specified constraints. The term incomplete information databases covers many types of inexact data, such as non-deterministic information, data with missing values, incomplete information or interval valued data. In the paper, we start by defining certain and possible rules based on non-deterministic information. We use their mathematical properties to solve computational problems related to rule generation. Then, we reconsider the NIS-Apriori algorithm which generates a given implication if and only if it is either a certain rule or a possible rule satisfying the constraints. In this sense, NIS-Apriori is logically sound and complete. In this paper, we pay a special attention to soundness and completeness of the considered algorithmic framework, which is not necessarily obvious when switching from exact to inexact data sets. Moreover, we analyze different types of non-deterministic information corresponding to different types of the underlying attributes, i.e., value sets for qualitative attributes and intervals for quantitative attributes, and we discuss various approaches to construction of descriptors related to particular attributes within the rules' premises. An improved implementation of NIS-Apriori and some demonstrations of an experimental application of our approach to data sets taken from the UCI machine learning repository are also presented. Last but not least, we show simplified proofs of some of our theoretical results.

7

Notes on defining fuzzy sets in the created inference system with probabilistic-fuzzy knowledge base

Błaszczyk K.

Zeszyty Naukowe. Elektryka / Politechnika Opolska

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2010

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z. 63

9-10

EN

In the paper two methods of defining fuzzy sets are considered: the method of standard membership functions and grades of memberships in disjoint intervals of variables space. An empirical example is discussed. The Root Mean Squared Error and the time of rules generation are compared.

8

Minimal Decision Rules Based on the Apriori Algorithm

Fernandez M. C., Menasalvas E., Marban O., Pena J. M., Millan S.

International Journal of Applied Mathematics and Computer Science

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2001

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Vol. 11, no 3

691-704

EN

Based on rough set theory many algorithms for rules extraction from data have been proposed. Decision rules can be obtained directly from a database. Some condition values may be unnecessary in a decision rule produced directly from the database. Such values can then be eliminated to create a more comprehensible (minimal) rule. Most of the algorithms that have been proposed to calculate minimal rules are based on rough set theory or machine learning. In our approach, in a post-processing stage, we apply the Apriori algorithm to reduce the decision rules obtained through rough sets. The set of dependencies thus obtained will help us discover irrelevant attribute values.