Search Results - (( developing interactive bat algorithm ) OR ( java detection learning algorithm ))
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1
Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm
Published 2008“…The prototype system, known as Java Plagiarism Detection System (JPDS) implements the Greedy-String-Tiling algorithm to detect similarities among tokens in a Java source code files. …”
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2
TBat: A Novel Strategy for Minimization of T-Way Interaction Test Suite Based on the Particle Swarm Optimization and the Bat Algorithm
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3
Taguchi?s T-method with Normalization-Based Binary Bat Algorithm
Published 2025“…Therefore, a variable selection technique using a swarm-based Binary Bat algorithm is proposed. Specifically, a normalization-based Binary Bat algorithm is used, where discretization of continuous solution into binary form is performed using a normalization equation. …”
Conference paper -
4
Benchmarking of Bat-inspired Interaction Testing Strategy
Published 2016“…In this article, we benchmarked the bat-inspired testing strategy (BTS) for uniform strength interaction against some of the available t-way testing tools and strategies. …”
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5
A bat-inspired testing strategy for generating constraints pairwise test suite
Published 2018“…Recently, the growing interests of adopting optimization algorithms as a backbone search engine for the new and upcoming search based software testing is increasing for newly developed test suite generation methods. …”
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6
A bat-inspired t-way strategy for mixed-strength test suite generation
Published 2017“…BTS is the first t-way strategy that adopts the Bat-inspired algorithm as its core implementation and adopts the Hamming distance as the final selection criteria to enhance the exploration of new solution. …”
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7
Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Features selection process can be considered a problem of global combinatorial optimization in machine learning. Genetic algorithm GA had been adopted to perform features selection method; however, this method could not deliver an acceptable detection rate, lower accuracy, and higher false alarm rates. …”
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8
An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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9
A malware analysis and detection system for mobile devices / Ali Feizollah
Published 2017“…We then used feature selection algorithms and deep learning algorithms to build a detection model. …”
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10
The implications for ahybrid detection technique against malicious sqlattacks on web applications
Published 2025“…The outcome of this study will add to the body of knowledge the most important and recent proposed solutions to mitigate SQL injection attack, in particular those based on machine learning algorithm…”
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11
Filter-Wrapper Methods For Gene Selection In Cancer Classification
Published 2018“…This study proposes new hybrid filter-wrapper methods based on Maximum Relevancy Minimum Redundancy (MRMR) as a filter approach and adapted bat-inspired algorithm (BA) as a wrapper approach. …”
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12
A fuzzy adaptive teaching learning-based optimization strategy for generating mixed strength t-way test suites
Published 2019“…Many test data generation strategies based on meta-heuristic algorithms such as Simulated Annealing (SA), Tabu Search (TS), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Harmony Search (HS), Cuckoo Search (CS), Bat Algorithm (BA) and Bees Algorithm have been developed in recent years. …”
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13
Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions
Published 2017“…Future work should be focusing on data collection of the E-Nose sensors and the improvement of the learning algorithm robustness towards environmental noise during data acquisition, such as evaporation and contamination of odor samples.…”
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