Yazar "Abualigah, Laith" seçeneğine göre listele

Listeleniyor 1 - 4 / 4
  • Küçük Resim
    Öğe
    Augmented grasshopper optimization algorithm by differential evolution: a power scheduling application in smart homes
    (Springer, 2021) Ziadeh, Ahmad; Abualigah, Laith; Abd Elaziz, Mohamed; Batur Şahin, Canan; Almazroi, Abdulwahab Ali; Omari, Mahmoud
    With the increasing number of electricity consumers, production, distribution, and consumption problems of produced energy have appeared. This paper proposed an optimization method to reduce the peak demand using smart grid capabilities. In the proposed method, a hybrid Grasshopper Optimization Algorithm (GOA) with the self-adaptive Differential Evolution (DE) is used, called HGOA. The proposed method takes advantage of the global and local search strategies from Differential Evolution and Grasshopper Optimization Algorithm. Experimental results are applied in two scenarios; the first scenario has universal inputs and several appliances. The second scenario has an expanded number of appliances. The results showed that the proposed method (HGOA) got better power scheduling arrangements and better performance than other comparative algorithms using the classical benchmark functions. Moreover, according to the computational time, it runs in constant execution time as the population is increased. The proposed method got 0.26?% enhancement compared to the other methods. Finally, we found that the proposed HGOA always got better results than the original method in the worst cases and the best cases.
  • Küçük Resim Yok
    Öğe
    Improving Automated Arabic Essay Questions Grading Based on Microsoft Word Dictionary
    (Springer Science and Business Media Deutschland GmbH, 2021) Hailat, Muath; Otair, Mohammed; Abualigah, Laith; Houssein, Essam; Batur Şahin, Canan
    There are three main types of questions: true/false, multiple choice, and essay questions; it is easy to implement automatic grading system (AGS) for multiple choice and true/false questions because the answers are specific compared with essay question answers. Automatic grading system (AGS) was developed to evaluate essay answers using a computer program that solves manual grading process problems like high cost, time-consuming task, increasing number of students, and pressure on teachers. This chapter presents Arabic essay question grading techniques using inner product similarity. The reason behind this is to retrieve students’ answers that more relevance to teachers’ answers. NB (naive Bayes) classifier is used because it is simple to implement and fast. The process starts by preprocessing phase, where tokenization step divides answers for small pieces of tokens. For normalization step, it is used to replace special letter shapes and remove diacritics. Then, stop word removal step removes meaningless and useless words. Finally, stemming process is used to get the stem and root of the words. All the preprocessing phase is meant to be implemented for both student answer and dataset. Then, classifying by naive Bayes classifier to get accurate result also for both students’ answers among with dataset. After that, using Microsoft Word dictionary to compare and get enough synonyms for both students’ answers and model answers in order to have exceptional results. Finally, showing results with the use of inner product similarity then compare the results showed by inner product similarity with human score results so the evaluation among with the efficiency of the proposed technique can be measured using mean absolute error (MAE) and Pearson correlation results (PCR). According to the experimental results, the approach leads to positive results when using MS dictionary and improvement Automated Arabic essay questions grading, where experiment results showed improvement in MAE is 0.041 with enhanced accuracy is 4.65% and PCR is 0.8250. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
  • Küçük Resim
    Öğe
    A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection
    (Springer, 2021) Batur Şahin, Canan; Abualigah, Laith
    The automatic detection of software vulnerabilities is considered a complex and common research problem. It is possible to detect several security vulnerabilities using static analysis (SA) tools, but comparatively high false-positive rates are observed in this case. Existing solutions to this problem depend on human experts to identify functionality, and as a result, several vulnerabilities are often overlooked. This paper introduces a novel approach for effectively and reliably finding vulnerabilities in open-source software programs. In this paper, we are motivated to examine the potential of the clonal selection theory. A novel deep learning-based vulnerability detection model is proposed to define features using the clustering theory of the clonal selection algorithm. To our knowledge, this is the first time we have used deep-learned long-lived team-hacker features to process memories of sequential features and mapping from the entire history of previous inputs to target vectors in theory. With an immune-based feature selection model, the proposed approach aimed to improve static analyses' detection abilities. A real-world SA dataset is used based on three open-source PHP applications. Comparisons are conducted based on using a classification model for all features to measure the proposed feature selection methods' classification improvement. The results demonstrated that the proposed method got significant enhancements, which occurred in the classification accuracy also in the true positive rate.
  • Küçük Resim Yok
    Öğe
    Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features
    (2021) Batur Şahin, Canan; Batur Dinler, Özlem; Abualigah, Laith
    The detection of software vulnerabilities is considered a vital problem in the software security area for a long time. Nowadays, it is challenging to manage software security due to its increased complexity and diversity. So, vulnerability detection applications play a significant part in software development and maintenance. The ability of the forecasting techniques in vulnerability detection is still weak. Thus, one of the efficient defining features methods that have been used to determine the software vulnerabilities is the metaheuristic optimization methods. This paper proposes a novel software vulnerability prediction model based on using a deep learning method and SYMbiotic Genetic algorithm. We are first to apply Diploid Genetic algorithms with deep learning networks on software vulnerability prediction to the best of our knowledge. In this proposed method, a deep SYMbiotic-based genetic algorithm model (DNN-SYMbiotic GAs) is used by learning the phenotyping of dominant-features for software vulnerability prediction problems. The proposed method aimed at increasing the detection abilities of vulnerability patterns with vulnerable components in the software. Comprehensive experiments are conducted on several benchmark datasets; these datasets are taken from Drupal, Moodle, and PHPMyAdmin projects. The obtained results revealed that the proposed method (DNN-SYMbiotic GAs) enhanced vulnerability prediction, which reflects improving software quality prediction.