Babaoğlu, İsmail

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Job Title:Prof. Dr.
Email Address:ibabaoglu@ktun.edu.tr
Main Affiliation:10.01. Department of Computer Engineering
Status: Current Staff
Website: https://www.ktun.edu.tr/tr/Universite/PersonelBilgi/?prsnl=5rcJJX9j1H20jiZJt1eE4/YVSnnw5naQJljTdlbkMME0qUoZNfb+fR4pQufvz+W0E4M6wT7nJTN95Bc+nVnilQ==
ORCID:ORCID Profile0000-0002-2503-1482
Scopus ID:Scopus Profile23097339300
YÖK Akademik: 16B6809DBF80272B
Google Scholar:Google Scholar ProfileYRPDUZUAAAAJ
Web of Science ID:Web of Science ProfileCEJ-4371-2022
Name Variants:
Babaoglu, İsmail Babaoğlu, İsmai̇l
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Now showing 1 - 10 of 22
  • Article
    Bit-Level Quad-Block Shuffling and Sequential Summing Dispersing Image Encryption Based on Hyperchaotic 2D Euler Pi Crossed Sine Map
    (Academic Press Inc. Elsevier Science, 2026-04-01) Kocak, Omer; Erkan, Ugur; Babaoglu, Ismail
    Chaos-based image encryption methods strongly depend on the complexity and dynamic behavior of chaotic maps to achieve effective permutation and diffusion. In this study, a novel two-dimensional Euler Pi Crossed Sine (2D-EPICS) chaotic map is introduced, which exhibits hyperchaotic dynamics, wide chaotic ranges, and high sensitivity to initial conditions. The chaotic properties of the proposed map are rigorously analyzed using bifurcation diagrams, phase trajectories, Lyapunov exponents, and multiple entropy measures, including sample entropy, permutation entropy, Kolmogorov entropy, and C0 complexity, confirming its strong nonlinear behavior and unpredictability. Building upon this chaotic foundation, the Bit-Level Quad-Block Shuffling and Sequential Summing Dispersing Image Encryption (BQSSSD-IE) scheme is then developed. The encryption process consists of a bit-level permutation stage based on quadruple pixel blocks, followed by a bidirectional diffusion stage achieved through cumulative row-wise and column-wise summations, both driven by sequences generated from the 2D-EPICS map. Extensive security analyses and comparative evaluations demonstrate that the proposed method provides high entropy, low pixel correlation, strong resistance against statistical, differential, noise, and cropping attacks, and competitive computational efficiency. The enhanced dynamic behavior of the 2D-EPICS map significantly strengthens the overall confusion and diffusion capabilities of the encryption scheme, making BQSSSD-IE suitable for secure and real-time image protection applications.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 4
    Syntax-Ignorant N-Gram Embeddings for Dialectal Arabic Sentiment Analysis
    (CAMBRIDGE UNIV PRESS, 2020-03-16) Mulki, Hala; Haddad, Hatem; Gridach, Mourad; Babaoglu, İsmail
    Arabic sentiment analysis models have recently employed compositional paragraph or sentence embedding features to represent the informal Arabic dialectal content. These embeddings are mostly composed via ordered, syntax-aware composition functions and learned within deep neural network architectures. With the differences in the syntactic structure and words' order among the Arabic dialects, a sentiment analysis system developed for one dialect might not be efficient for the others. Here we present syntax-ignorant, sentiment-specific n-gram embeddings for sentiment analysis of several Arabic dialects. The novelty of the proposed model is illustrated through its features and architecture. In the proposed model, the sentiment is expressed by embeddings, composed via the unordered additive composition function and learned within a shallow neural architecture. To evaluate the generated embeddings, they were compared with the state-of-the art word/paragraph embeddings. This involved investigating their efficiency, as expressive sentiment features, based on the visualisation maps constructed for our n-gram embeddings and word2vec/doc2vec. In addition, using several Eastern/Western Arabic datasets of single-dialect and multi-dialectal contents, the ability of our embeddings to recognise the sentiment was investigated against word/paragraph embeddings-based models. This comparison was performed within both shallow and deep neural network architectures and with two unordered composition functions employed. The results revealed that the introduced syntax-ignorant embeddings could represent single and combinations of different dialects efficiently, as our shallow sentiment analysis model, trained with the proposed n-gram embeddings, could outperform the word2vec/doc2vec models and rival deep neural architectures consuming, remarkably, less training time.
  • Article
    Citation - WoS: 15
    Citation - Scopus: 17
    Discrete Tree Seed Algorithm for Urban Land Readjustment
    (Pergamon-Elsevier Science Ltd, 2022-06-01) Koç, İsmail; Atay, Yılmaz; Babaoğlu, İsmail
    Land readjustment and redistribution (LR) is an important approach used to realize development plans by converting rural lands to urban land and also providing urban infrastructure. The LR problem, which is a complex challenging real-world problem, is a discrete optimization problem because its structure is similar to TSP (Traveling Salesman Problem) and scheduling problems which are combinatorial optimization problems. Since classical mathematical methods are insufficient for solving NP (Nondeterministic Polynomial) optimization problems due to time limitations, meta-heuristic optimization algorithms are commonly utilized for solving these kinds of problems. In this paper, meta-heuristic algorithms including genetic, particle swarm, differential evolution, artificial bee, and tree seed algorithms are utilized for solving LR problems. The stated meta-heuristic algorithms are used by applying spatial-based crossover and mutation operators depending upon the LR problem on each algorithm. Moreover, a synthetic dataset is used to ensure that the quality of the solution obtained is acceptable to everyone, to prove an optimal solution easily. By utilizing the suggested spatial-based crossover and mutation operators, finding the ideal solution is aimed using the synthetic dataset. In addition, five different modifications on TSA (Tree-Seed Algorithm) are performed and used to solve LR problems. All the modified versions of TSA are carried out only by changing the mechanism of seed reproduction. The novel TSA approaches are respectively named as tcTSA (tournament current), tbTSA (tournament best), pbTSA (personal-best based), t2TSA (double tournament), and elTSA (elitism based). In the experimental studies, the hybrid approach, which includes the crossover and mutation operators, is successfully applied in all of the algorithms under equal conditions for a fair comparison. According to experimental results performed using the dataset, it can be clearly stated that especially t2TSA outperforms all the algorithms in terms of performance and time.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 4
    Approaches To Automated Land Subdivision Using Binary Search Algorithm in Zoning Applications
    (Ice Publishing, 2022-03-01) Koç, İsmail; Çay, Tayfun; Babaoğlu, İsmail
    The planned development of urban areas depends on zoning applications. Although zoning practices are performed using different techniques, the parcelling operations that shape the future view of the city are the same. Preparing the parcelling plans is an important step that has a direct impact on ownership structure and reallocation. Parcelling operations are traditionally handled manually by a technician. This is a serious problem in terms of time and cost. In this study, by taking the zoning legislation, the production of a pre-land subdivision plan has been automatically performed for a region of Konya, which is one of the major cities in Turkey. The parcelling processes have been performed in three different ways: the first parcelling technique is parcelling with edge values, the second is parcelling with area values and the third is parcelling using both edge and area values together. For the entire parcelling process, the area of the parcel has been calculated using the Gauss method. Moreover, to effectively determine the boundaries and to calculate the parcel area in the parcelling process, the binary search technique has been used in all the methods. The experimental results show that the parcelling operations were carried out very quickly and successfully.
  • Doctoral Thesis
    İmar Uygulamalarında Dağıtım ve Parselasyon İşlemlerinin Yapay Zeka Optimizasyon Algoritmaları Kullanılarak Gerçekleştirilmesi
    (Konya Teknik Üniversitesi, 2020) Koç, İsmail; Babaoğlu, İsmail
    İmar uygulamalarındaki dağıtım ve parselasyon (LR – Land redistribution and readjustment) problemleri, birçok farklı kriterleri içerisinde barındıran karmaşık ve zor gerçek dünya problemleridir. Bu problemlerin çözülebilmesi için öncelikle ön parselasyon işlemlerinin başarıyla gerçekleştirilmesi gerekmektedir. Problemin çözümü için oluşturulan başlangıç popülasyonunda rastgele aday çözüm üretilirken veya çözümler üzerinde değişiklik yapılırken her aşamada parsellerin alanı güncellenmekte ve buna bağlı olarak da her bir parselin konumunun bir başka deyişle sınır noktalarının yeniden güncellenmesi gerekmektedir. Bu tez çalışmasında, imar mevzuatı ve örnek bir imar planı dikkate alınarak, Konya'nın bir bölgesi üzerinde ön parselasyon işlemi üç farklı şekilde otomatik olarak gerçekleştirilmektedir. Birincisi kenar uzunluklarına göre parselasyon, ikincisi alan değerlerine göre parselasyon ve üçüncüsü hem kenar hem de alan değerleri birlikte kullanılarak gerçekleştirilen parselasyon. Tüm bu parselasyon işlemleri için parselin alanı Gauss yöntemi kullanılarak hesaplanmaktadır. Ayrıca parselasyon işlemlerinde her bir parselin kenar noktalarını doğru bir şekilde belirlemek ve alanını hesaplamak için tüm yöntemlerde ikili arama tekniği kullanılmaktadır. Deneysel sonuçlar, ön parselasyon işlemlerinin çok hızlı ve başarılı bir şekilde gerçekleştirildiğini göstermektedir. LR problemleri yapısı itibariyle çizelgeleme ve gezgin satıcı problemlerine benzerlik gösteren ayrık optimizasyon problemleridir. Ayrıca bu problemler eş zamanlı olarak çözülmesi gereken çok fazla kritere sahiptir. Bundan dolayı bu problemlerin çözümü klasik yöntemler yerine yapay zeka optimizasyon algoritmaları gerektirmektedir. Bu problemleri çözmek için ilk olarak, algoritmaların doğru karar vermesini ve aday çözümler arasında çözümleri objektif olarak değerlendirilmesini sağlayacak bir amaç fonksiyona ihtiyaç duyulmaktadır. LR problemlerinin çözümünde temel kıstas olacak bu amaç fonksiyon sayesinde, dağıtım ve parselasyon planlarının kalitesi herhangi bir uzmana ihtiyaç duyulmadan değerlendirilebilmesi ve karşılaştırılabilmesi sağlanmaktadır. Bu tez çalışmasında tüm kriterleri dikkate alan bir amaç fonksiyon önerilmektedir. Ayrıca bu çalışmada, literatürdeki çaprazlama tekniklerinden farklı olarak, klasik ve zeki Parsel Tabanlı çaprazlama operatörleri olarak adlandırılan iki farklı harita tabanlı çaprazlama operatörü önerilmektedir. Önerilen amaç fonksiyonu ve çaprazlama operatörleri yardımıyla bu tez çalışmasında önerilen ayrık yapay arı koloni (ABC – artificial bee colony), diferansiyel gelişim (DE – differential evolution), genetik (GA – genetic algorithm), parçacık sürü optimizasyonu (PSO – particle swarm optimization) ve ağaç tohum (TSA – tree seed algorithm) algoritmaları gerçek bir proje alanı üzerinde uygulanmaktadır. Deneysel çalışmalardaki sonuçlar manuel olarak elde edilen resmi sonuçlarla karşılaştırılmaktadır. Bunların yanı sıra, geliştirilen uzman sistem sayesinde imar adalarındaki boşluk ve taşan alanlar tamamen ortadan kaldırılarak algoritmalardan elde edilen çözümler gerçek dünyada doğrudan kullanılabilir hale getirilmektedir. Deneysel sonuçlar incelendiğinde, yapay zeka algoritmaların elde ettiği tüm sonuçların hem çözüm kalitesi hem de hız açısından manuel olarak elde edilen resmi sonuçlardan çok daha etkili olduğu açıkça görülmektedir. Ayrıca PSO algoritmasının diğer algoritmalara göre çok daha etkili ve kararlı olduğu görülmektedir. Buna ilaveten önerilen zeki parsel tabanlı çaprazlama operatörünün klasik parsel tabanlı çaprazlama operatörüne göre çok daha etkin sonuçlar elde ettiği görülmektedir. İmar uygulamalarında gerçek dünya problemlerinin çok karmaşık bir yapıya sahip olmasından dolayı problemin uygunluk değeri tam olarak bilinememektedir. Bu yüzden LR problemlerinde test amaçlı kullanılmak üzere sentetik bir veri seti önerilmektedir. Önerilen veri setindeki problemlerin en iyi çözümü kesin olarak bilinmektedir. Bu veri seti parsel sayısına göre 20, 40, 60, 80 ve 100 olmak üzere 5 farklı problemden oluşmaktadır. Her bir problem seti parsel başına düşen malik sayısı bakımından 1, 2, 3 ve 4 olarak 4 farklı problem içermektedir. Bu nedenle, veri seti toplamda 20 farklı problemden oluşmaktadır. Sentetik veri seti kullanılarak gerçekleştirilen deneysel sonuçlar incelendiğinde, GA yönteminin hem hız hem de performans açısından en etkili algoritma olduğu görülmektedir. ABC birkaç problemde GA'dan daha iyi sonuçlara sahip olsa da ABC performans açısından GA yönteminden sonra en başarılı ikinci algoritmadır. Fakat zaman açısından ABC, neredeyse GA kadar başarılı bir algoritmadır. Diğer yandan, DE, PSO ve TSA algoritmalarının sonuçları, çözüm kalitesi açısından birbirine benzemektedir. Sonuç olarak, deneysel çalışmalar, GA yönteminin hız, performans ve kararlılık açısından LR problemlerinin çözümünde en etkili teknik olduğunu açıkça göstermektedir.
  • Doctoral Thesis
    İki boyutlu kaotik sistem üretecinin geliştirilmesi ve blok tabanlı görüntü şifreleme algoritmasının tasarımı
    (2026) Koçak, Ömer; Babaoğlu, İsmail; Erkan, Uğur
    The design of chaotic models in the literature is generally developed through nonlinear functions or polynomial equations. In this methodology, transcendental irrational numbers such as Euler's number (e) and Pi (π) are frequently utilized to enrich the dynamic complexity and topological properties of the chaotic system. The success of modern chaotic systems in fields such as secure communication and cryptography is directly dependent on the high level of randomness and structural complexity established via adaptable seed functions. It is a well-recognized phenomenon that transcendental numbers contribute significantly to the performance and complexity of chaotic systems. In this thesis, a novel Two-Dimensional Apéry Chaotic Generator (2D-ACG) based on the Apéry constant is proposed. The 2D-ACG possesses the capability to generate diverse chaotic systems by utilizing classical seed functions. The effectiveness of the system has been demonstrated on maps derived from function pairs such as Cos–Cos, Sin–Sin, and Cos–Sin, and validated using metrics including Lyapunov exponent (LE), sample entropy (SE), correlation dimension (CD), Kolmogorov entropy (KE), and the C0 test. Comparisons with other 2D systems in the literature and microcontroller-based hardware implementations reveal that the 2D-ACG possesses high-performance and robust chaotic characteristics due to its enhanced diversity and complex structure derived from the Apéry constant. In chaos-based image encryption systems, the structural complexity and dynamic characteristics of the employed chaotic map, alongside the effectiveness of permutation and diffusion mechanisms, constitute the fundamental elements determining cryptographic robustness. A significant portion of existing chaotic maps in the literature exhibit poor dynamic properties, resulting in limited permutation (shuffling) and diffusion capabilities. To overcome these limitations, a novel Two-Dimensional Euler Pi Crossed Sine (2D-EPICS) chaotic map has been developed, offering hyper-chaotic dynamics, wide chaotic ranges, and high unpredictability. The 2D-EPICS map is designed to demonstrate strong hyper-chaotic behavior across a broad and continuous parameter space; its chaotic performance has been comprehensively analyzed through bifurcation diagrams, phase trajectories, and multiple entropy measures. Built upon this chaotic foundation, the Bit-Level Quad-Block Shuffling and Sequential Summing Dispersing Image Encryption (BQSSSD-IE) scheme incorporates a two-stage bit-level permutation and a unique cumulative diffusion process. During the permutation stage, pixels are shuffled both at the bit level using quadruple blocks and individually on a column basis. In the diffusion stage, a 'cumulative sequential summing diffusion' mechanism—driven by chaotic sequences generated by 2D-EPICS—is introduced to the literature, where rows and columns are sequentially summed in both directions. Security analyses reveal that the proposed method achieves near-ideal information entropy and minimized pixel correlation, while exhibiting superior resistance against statistical, differential, and noise-based attacks. The obtained results and competitive computational efficiency confirm that the 2D-EPICS-based BQSSSD-IE scheme provides an ideal solution for real-time and high-security image protection applications.
  • Article
    Derin Öğrenme Ve Makine Öğrenimi İle Ekg Sinyali Sınıflandırması: İki Boyutlu Çerçevelerde Qrs Komplekslerinin Temsili Ve Smote İle Veri Dengeleme
    (2026-03-03) Tezel, Gülay; Babaoğlu, İsmail; Öner, Mehmet Reşat
    This study aims to support reliable ECG signal interpretation by reducing human-dependent variability through computer-aided analysis methods. Machine learning and deep learning methods were employed to examine 2D ECG representations and Synthetic Minority Over-Sampling Technique (SMOTE)-based balancing in ECG classification. Unlike existing ECG classification studies that typically address signal representation and class imbalance separately, this study jointly investigates the interaction between two-dimensional QRS representation and SMOTE-based data balancing within a unified experimental framework, thereby providing a systematic analysis of their combined impact on classification performance. Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and K-Nearest Neighbors (KNN) algorithms were implemented and comparatively analyzed. ECG beats from record 108 of the MIT-BIH Arrhythmia dataset were represented in a vision-based form for classification. To address severe class imbalance, SMOTE was applied only to the training data, and its effect on two-dimensional ECG representations was explicitly examined. Normal and Abnormal heartbeats were classified using a stratified 5-fold cross-validation strategy. Experimental results demonstrated that the CNN model achieved the most successful performance after applying SMOTE, reaching a weighted average F1-score of 99.82% ± 0.002, highlighting the combined effectiveness of two-dimensional QRS representation and data balancing in improving automated ECG classification.
  • Article
    Citation - WoS: 25
    Citation - Scopus: 36
    Through-Wall Radar Classification of Human Posture Using Convolutional Neural Networks
    (HINDAWI LTD, 2019-03-31) Kılıç, Alper; Babaoğlu, İsmail; Babalık, Ahmet; Arslan, Ahmet
    Through-wall detection and classification are highly desirable for surveillance, security, and military applications in areas that cannot be sensed using conventional measures. In the domain of these applications, a key challenge is an ability not only to sense the presence of individuals behind the wall but also to classify their actions and postures. Researchers have applied ultrawideband (UWB) radars to penetrate wall materials and make intelligent decisions about the contents of rooms and buildings. As a form of UWB radar, stepped frequency continuous wave (SFCW) radars have been preferred due to their advantages. On the other hand, the success of classification with deep learning methods in different problems is remarkable. Since the radar signals contain valuable information about the objects behind the wall, the use of deep learning techniques for classification purposes will give a different direction to the research. This paper focuses on the classification of the human posture behind the wall using through-wall radar signals and a convolutional neural network (CNN). The SFCW radar is used to collect radar signals reflected from the human target behind the wall. These signals are employed to classify the presence of the human and the human posture whether he/she is standing or sitting by using CNN. The proposed approach achieves remarkable and successful results without the need for detailed preprocessing operations and long-term data used in the traditional approaches.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 4
    FUZZY ADAPTIVE WHALE OPTIMIZATION ALGORITHM FOR NUMERIC OPTIMIZATION
    (UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH, 2021-04-30) Kaya, Ersin; Kılıç, Alper; Babaoğlu, İsmail; Babalık, Ahmet
    Meta-heuristic approaches are used as a powerful tool for solving numeric optimization problems. Since these problems are deeply concerned with their diversified characteristics, investigation of the utilization of algorithms is significant for the researchers. Whale optimization algorithm (WOA) is one of the novel meta-heuristic algorithms employed for solving numeric optimization problems. WOA deals with exploitation and exploration of the search space in three stages, and in every stage, all dimensions of the candidate solutions are updated. The drawback of this update scheme is to lead the convergence of the algorithm to stack. Some known meta-heuristic approaches treat this issue by updating one or a predetermined number of dimensions in their update scheme. To improve the exploitation behavior of WOA, a fuzzy logic controller (FLC) based adaptive WOA (FAWOA) is suggested in this study. An FLC realizes the update scheme of WOA, and the proposed FLC determines the rate of the change in terms of dimension. The suggested FAWOA is evaluated using 23 well-known benchmark problems and compared with some other meta-heuristic approaches. Considering the benchmark problems, FAWOA achieves best results on 11 problem and only differential evaluation algorithm achieve best results on 10 problems. The rest of the algorithms couldn't achieve the best results on not more than 5 problems. Besides, according to the Friedman and average ranking tests, FAWOA is the first ranked algorithm for solving the benchmark problems. Evaluation results show that the suggested FAWOA approach outperforms the other algorithms as well as the WOA in most of the benchmark problems.
  • Master Thesis
    Nft'lerin Video Filigranlamada Kullanımı
    (Konya Teknik Üniversitesi, 2023) Camara, Ibrahima Sory; Babaoğlu, İsmai̇l
    Bu çalışmada, NFT uygulamaları için özel olarak tasarlanmış yeni bir video filigran algoritması sunulmaktadır. Önerilen algoritma, video içeriğine filigran yerleştirmek amacıyla tasarlanmış ve ardından filigranın bütünlüğünü ve direncini değerlendirmek için çeşitli saldırılara karşı test edilmiştir. Önerilen algoritmanın performansı, sıkıştırma, geometrik, zamanlı, filtreleme, gürültü ekleme ve kuantizasyon saldırıları gibi kapsamlı bir saldırı setine karşı değerlendirilmiştir. Sonuçlar, algoritmanın farklı saldırılara karşı değişen direnç seviyeleri gösterdiğini ve filigranın sağlamlığını etkileyen bazı zayıf noktaların olduğunu göstermektedir. Özellikle, H.264 (aynı ayarlarla) ve Huffyuv gibi kayıplı ve kayıpsız sıkıştırma yöntemlerine ve Wiener filtresi gibi frekans alanı filtreleme saldırılarına karşı başarılıdır. Bu çalışmanın bulguları, NFT uygulamaları için daha sağlam video filigran tekniklerinin geliştirilmesi için değerli içgörüler sunmaktadır. Önerilen algoritma, özellikle NFT'ler bağlamında dijital dünyada video içeriğini güvence altına almak için umut verici bir potansiyele sahiptir. NFT önce oluşturulur, ardından meta verileri eklenir, benzersiz jeton URI'si belirlenir ve NFT OpenSea platformuna yüklenir. Daha sonra belirli bir NFT'nin URL (Tekdüzen Kaynak Bulucu)'si seçilen bir videonun karelerinin içine gömülür. Video sahibini belirlemek için, kareler videodan çıkarılır ve URL'ler karelerden çıkarılır, ardından birbiriyle karşılaştırılarak mükemmel bozulmamış URL elde edilir. Bu URL bizi OpenSea'deki NFT'ye yönlendirir ve orada tüm bilgiler saklanır.

Research Topics

Domains
Physical Sciences
Fields
Computer ScienceChemistry
Subfields
Artificial IntelligenceComputer Vision and Pattern RecognitionAnalytical Chemistry
Specific Research Areas
Metaheuristic Optimization Algorithms Research
Sentiment Analysis and Opinion Mining
Topic Modeling
Advanced Steganography and Watermarking Techniques
Spectroscopy and Chemometric Analyses

Sustainable Development Goals

GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
3
Research Products
SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
2
Research Products
LIFE ON LAND15
LIFE ON LAND
1
Research Products
QUALITY EDUCATION4
QUALITY EDUCATION
1
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
Documents

41

Citations

911

h-index

15

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Documents

40

Citations

646

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Publication Collaboration

Affiliation Name Count
Selçuk University 26
Konya Technical University 15
Université Libre de Bruxelles 3
University of Carthage 3
National Institute of Applied Science and Technology 3
1 / 4
Data obtained from OpenAlex
JournalCount
2015 2nd International Conference on Information Science and Control Engineering1
APPLIED SOFT COMPUTING1
Digital Signal Processing1
Engineering Applications of Artificial Intelligence1
EXPERT SYSTEMS WITH APPLICATIONS1
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Scholarly Output

22

Articles

14

Views / Downloads

68/105

Supervised MSc Theses

3

Supervised PhD Theses

3

WoS Citation Count

79

Scopus Citation Count

108

Patents

0

Projects

0

WoS Citations per Publication

3.59

Scopus Citations per Publication

4.91

Open Access Source

10

Supervised Theses

6

Scopus Quartile Distribution

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