Yazar "Abualigah, Laith" seçeneğine göre listele
Listeleniyor 1 - 13 / 13
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe A review of mothflame optimization algorithm: analysis and applications(Elsevier, 2024) Abualigah, Laith; Al-Abadi, Laheeb; Ikotun, Abiodun M.; AL-Saqqar, Faisal; Izci, Davut; Zhang, Peiying; Sumari, PutraThe moth flame optimization (MFO) algorithm, which is inspired by nature, is studied in this study. This optimizer takes its main inspiration from the directional navigation approach used by moths in nature. Moths fly at a steady angle to the moon at night. This is a particularly efficient method for traveling long distances in a straight line. Insects, on the other hand, are trapped in a useless/deadly spiral path around artificial lights. This chapter provides a review of published papers that used the MFO method. © 2024 Elsevier Inc. All rights reserved.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, MahmoudWith 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.Öğe Convolutional Neural Networks: The Power Behind Image Recognition(CRC Press, 2025) Almomani, Mohammad H.; Hanandeh, Essam; Şahin, Canan Batur; Zhang, Peiying; Zuong, Zuzho; Nasayreh, Ahmad; Abualigah, LaithA number of artificial intelligence advancements have emerged that allow computerizes the same critical thinking tasks that were solely more suited to humans such as image recognition of objects etc. One such advancement that is the focus of the paper is the Convolutional Neural Networks. This paper details the structural design, how they perform the tasks assigned and how they function as a contribution to the image recognition area. Additionally, we show through a case study of three CNN models how they can be evaluated in relation to one set of common data effectively and efficiently. The data shows that there was improvement in processing time and accuracy which emphasises the role of CNN in the detailed domains. Some future trends in the CNN development and its usages are presented. © 2025 Laith Abualigah.Öğe Deep Reinforcement Learning: Bridging Learning and Control in Intelligent Systems(CRC Press, 2025) Izci, Davut; Thanh, Hung Vo; Zitar, Raed Abu; Al-Okbi, Nada Khalil; Liu, Zhe; Şahin, Canan Batur; Abualigah, LaithDeep Reinforcement Learning (DRL) has gained popularity as a new approach in artificial intelligence that successfully integrates representation learning through Deep Learning with decision making in Reinforcement Learning. In this work, we investigate basic concepts of DRL design, structural composition, and scope of usage in intelligent systems. More specifically, several benchmark tasks were assigned to test states and actions of DRL algorithms such as Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO). Empirical evaluations illustrate the potential of DRL in dealing with complex tasks. Sample efficiency, stability and interpretability issues of DRL are also reviewed in the paper. Suggestions for further work are concentrated on algorithm increase of robustness, training time and expense decrease as well as enhancement of implementation efficiency in practical environment. © 2025 Laith Abualigah.Öğe Detection of cyber attacks in electric vehicle charging systems using a remaining useful life generative adversarial network(Nature Portfolio, 2025) Tanyildiz, Hayriye; Sahin, Canan Batur; Dinler, Ozlem Batur; Migdady, Hazem; Saleem, Kashif; Smerat, Aseel; Abualigah, LaithCybersecurity attacks targeting electric vehicle supply equipment (EVSE) and the broader electric vehicle (EV) ecosystem have become an escalating concern with the increasing adoption of EVs and the growing connectivity of the infrastructure supporting them. The present research aims to contribute to continuing cybersecurity studies on electric vehicle charging stations. In line with this objective, this study proposes the remaining useful life (RUL) approach to demonstrate the potential impact of estimating the remaining time of a cyber attack on EVSE and what revolutionary changes it can bring to cyber security strategies using a generative adversarial network (GAN). By taking a proactive stance, the manuscript will increase security and reduce the economic and reputational losses associated with cyber incidents. Accurate RUL estimates present valuable information about the status of the EVSE infrastructure. Thus, informed decisions on maintenance and crew scheduling are taken. To test the technique's effectiveness, we assess this approach on attack scenarios, including network and host attacks on the EV charger (Electric Vehicle Supply Equipment-EVSE) in idle and charging states. Furthermore, we assess the prediction results of different deep learning models, such as gated recurrent units (GRUs), long short-term memory (LSTM), recurrent neural networks (RNNs), convolution neural networks (CNNs), multi-layer perceptron (MLP), and dense layer integrated with generative adversarial networks (GANs), using mean absolute error (MAE), root mean square error (RMSE), mean squared error (MSE), and R-squared (R2). Afterward, we compare the error measurements with models, such as hybrid GAN-LSTM, GAN-GRU, GAN-RNN, GAN-CNN, GAN-MLP, and GAN-Dense Layer. The GAN-GRU model exhibits the highest accuracy with the lowest MAE (0.0281). On the contrary, the GAN-CNN model displays the best overall performance concerning error consistency and variance explained. According to the results, integrating GAN into these architectures improves predictive accuracy and the model's ability to identify potential attacks in advance and decreases error rates.Öğe Hybrid Aquila Optimizer with Moth-Flame Optimization Algorithm for Global Optimization(CRC Press, 2022) Abualigah, Laith; Mirjalili, Seyedali; Abd Elaziz, Mohamed; Jia, Heming; Şahin, Canan Batur; Khalifeh, Ala[Abstract Not Available]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, CananThere 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.Öğe Kimlik Hırsızı Web Sitelerinin Farklı DeepLearning4J Modelleri ile Performanslarının Karşılaştırılması(Osman SAĞDIÇ, 2021) Dinler, Özlem Batur; Şahin, Canan Batur; Abualigah, Laithİlk olarak 2019’da Çin’in Wuhan şehrinde görülen yeni tip koranavirüs (COVID-19 ) hastalığı nedeniyle 11 Mart 2020 tarihinde Dünya Sağlık Örgütü (DSÖ) tarafından pandemi ilan edilmiştir. Dünya genelinde hâlâ etkisi devam etmekte olan bu salgın, kısa sürede tüm dünya toplumunun gündelik yaşam aktivitelerini ve alışkanlıklarını hızlı bir şekilde değiştirerek digital ortam uygulamalarına doğru kaydırmıştır. Bu doğrultuda, artan siber saldırı atakları ve yaşanan veri ihlalleri salgın toplumu için büyük bir risk oluşturmuştur. Bu bağlamda, dijital ortam uygulamalarının güvenliği COVID-19 salgını ile çok daha önemli bir sorun haline gelmiştir. Bu sorun özellikle kimlik hırsızı web siteleri üzerinde gözlenmiştir. Web kimlik hırsızlığı, güvenilir kurumları taklit ederek kişilerin ad, soyad, şifre ve kredi kartı numaraları gibi kişisel bilgileri çalma yöntemidir. Bu, bilginin ifşa olmasına ve mali zarara neden olacaktır. Çalışmanın odağı, kimlik hırsızı web sitelerinin tanımlanması amacı ile kullanılan birkaç DeepLearning4j (DL4j) modeline dayanmaktadır. Bununla birlikte, çalışmanın temel amacı, değerlendirme metriklerinin performanslarını iyileştirmek amacı ile DeepLearning4J (DL4J) modellerinin etkinliğini verimli bir şekilde izlemektir.Öğe A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection(Springer, 2021) Batur Şahin, Canan; Abualigah, LaithThe 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.Öğe Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features(2021) Batur Şahin, Canan; Batur Dinler, Özlem; Abualigah, LaithThe 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.Öğe Recurrent Neural Networks and its Applications in Time Series Data(CRC Press, 2025) Nazza, Muhannad Akram; Al-Okbi, Nada Khalil; Şahin, Canan Batur; Hu, Gang; Sumari, Putra; Snasel, Vaclav; Abualigah, LaithRecurrent Neural Networks (RNNs) have been extensively embraced for sequencing analysis and sequential data modelling, especially time series data. This paper delves into the principles and applications of the RNNs concentrically time series analysis. The advantages of time series RNNs, including long short-term memory (LSTM) and Gated recurrent units (GRU), are discussed in detail. Different RNN models have been implemented on both synthetic and real-look time series datasets to assess their performance. The results clearly show RNNs outperformed traditional forecasting and pattern recognition methods. Lastly, the issues related to RNN training, namely, vanishing gradients and overfitting, are briefly addressed, and potential improvements for RNNs are outlined. © 2025 Laith Abualigah.Öğe Risk Faktörlerinin Dağıtık Yazılım Ekip Yapısı Kapsamında İncelenmesi(Osman SAĞDIÇ, 2021) Şahin, Canan Batur; Dinler, Özlem Batur; Abualigah, LaithDağıtık yazılım ekip yapısı, ekip üyelerinin yazılımın yaşam döngüsü boyunca farklı uzak yerleşkelerde bulunmasını sağlayan bir gelişim türünü temsil etmektedir. Bu nedenle, uzak alt ekiplerden oluşan bir ağ oluşturmaktadır. Günümüzde yazılım projelerinin coğrafi olarak uzak ekiplerle geliştirilmesi, yazılım geliştirme ortamlarının esnek olmasını zorunlu kılmaktadır. Bu durumda yazılım yöneticilerinin dağıtık ekip yönetimini iyi organize etmeleri ve yönetmeleri büyük önem taşımaktadır. Ülkeler ve bölgeler genelinde dağıtık ekiplerin eğilimi ile yazılım projeleri sahnesine daha fazla sayıda paydaş getirilmektedir. Risk yönetimi, projenin beklenen sonucunu elde etmenin en önemli güvencesi olmakla birlikte, söz konusu süreçler, projede yer alan paydaşların her sahadan ortak sorumlulukları ve taahhüdü olarak düşünülmelidir. Yazılımın 'kurum içi' geliştirilmesi bir dizi riske yol açmaktadır. Aynı zamanda kısmen başka bir coğrafyada gelişmesi söz konusu risklerin katlanarak artmasına neden olmaktadır. Risklerin yönetimi, projenin erken aşamalarında başlamalı ve projelerin yaşam döngüsü boyunca bir izleme ve takip çalışması şeklinde devam etmelidir. Bu çalışma kapsamın da, dağıtık ekip yapısındaki ana risk faktörlerinin geleneksel proje yöntemi yaklaşımı yerine Yetenek Olgunluk Model Entegrasyonu (CMMI) uyarınca yürütülmek üzere geliştirilen süreç adımları detaylı olarak anlatılmaktadır. Ayrıca bu çalışma, dağıtık yazılım geliştirme ile ilgili risk yönetimi sürecinde yer alan bileşenleri ortaya çıkarmayı amaçlamıştır.Öğe The Intersection of AI and the Internet of Things (IoT): Transforming Data into Intelligence(CRC Press, 2025) Al-Okbi, Nada Khalil; Khodadadi, Nima; Kumar, Manoj; Şahin, Canan Batur; Khishe, Mohammad; Raza, Ali; Abualigah, LaithThe application of Artificial Intelligence (AI)-enabled systems is becoming a trend in decision making impacts revenue and efficiency of the companies while adding value to IoT (Internet of Things). This research paper investigates the elements upholding the synergy between IoT and AI, arguing that their integration advances the development of smart systems that optimize decision making, improve operational processes, and improve user experience. Ample attention is paid to several aspects of AI integration practices: techniques used within various IoT hubs, exposed results of their case application, and archived barriers and prospects of this paradigmatic combination. Such an understanding will enable the relevant actors to create data-driven intelligence that is appropriate for them. © 2025 Laith Abualigah.
