Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/1624

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Citation - WoS: 11
Citation - Scopus: 19
Artificial Intelligence in Healthcare Competition (teknofest-2021): Stroke Data Set
(AVES, 2022) Koç, U.; Sezer, E.A.; Özkaya, Y.A.; Yarbay, Y.; Taydaş, O.; Ayyıldız, V.A.; Bahadır, Murat
Objective: The artificial intelligence competition in healthcare was organized for the first time at the annual aviation, space, and technology festival (TEKNOFEST), Istanbul/Türkiye, in September 2021. In this article, the data set preparation and competition processes were explained in detail; the anonymized and annotated data set is also provided via official website for further research. Materials and Methods: Data set recorded over the period covering 2019 and 2020 were centrally screened from the e-Pulse and Teleradiology System of the Republic of Türkiye, Ministry of Health using various codes and filtering criteria. The data set was anonymized. The data set was prepared, pooled, curated, and annotated by 7 radiologists. The training data set was shared with the teams via a dedicated file transfer protocol server, which could be accessed using private usernames and passwords given to the teams under a non-disclosure agreement signed by the representative of each team. Results: The competition consisted of 2 stages. In the first stage, teams were given 192 digital imaging and communications in medicine images that belong to 1 of 3 possible categories namely, hemorrhage, ischemic, or non-stroke. Teams were asked to classify each image as either stroke present or absent. In the second stage of the competition, qualifying 36 teams were given 97 digital imaging and communications in medicine images that contained hemorrhage, ischemia, or both lesions. Among the employed methods, Unet and DeepLabv3 were the most frequently observed ones. Conclusion: Artificial intelligence competitions in healthcare offer good opportunities to collect data reflect-ing various cases and problems. Especially, annotated data set by domain experts is more valuable. © 2022, AVES. All rights reserved.
Atıksu Arıtma Tesislerinde Mikrokirleticilerinizlenmesi ve Kontrolü
(2019) Kara, Meryem; Nas, Bilgehan; Argun, Mehmet Emin; Yel, Esra; Dinç, Saliha; Koyuncu, Serdar
Yerüstü ve yeraltı sularında su kalitesinin belirlenmesi için AB Su Çerçeve Direktifindeki 45 öncelikli maddede yer alan MK?lerin izlenmesi yönünde araştırmalar hızlanmıştır. Su kütlelerinde MK?lerin önemli kaynaklarından biri atıksu arıtma tesisleri (AAT)?dir. Bu projede, küçük, orta ve büyük ölçekli 3 yerleşim yerinde 3 farklı arıtma prosesinde MK?ler 1 yıl süreyle izlenmiş ve proseslerin MK?leri giderme performansları ortaya konulmuştur. MK gruplarından; pestisit (Kloropirifos, Atrazin, Klorfenvinfos), fitalat ester (BBP, DEHP, DnOP), yüzey aktif madde (Oktil fenol, Nonil fenol), PAH (Benzo[b]fluoranten, Benzo[k]fluoranten, Benzo(a)piren, Indeno[1,2,3-cd]piren, Benzo[g,h,i]perilen, Fluoranten, Antrasen, Naftalin), VOC (Diklorometan, Benzen, 1.2-dikloroetan), ilaç etken madde (Diklofenak, Karbamazepin, 17-beta-estradiyol, 17-alfa-etinilestradiol) ve ağır metallerden (Cd, Pb, Hg, Ni) toplam 27 MK izlenmiştir. Konya (İleri biyolojik arıtma), Ereğli (Anaerobik ve fakültatif stabilizasyon), Zincirlikuyu (Yapay sulak alan) AAT?lerinden, atıksu geri kazanım tesisinden, pilot ölçekli ultrafiltrasyon (UF), nanofiltrasyon (NF) ve ters osmoz (RO) ünitelerinden oluşan membran tesisten her numune alma döneminde 17 su, 6 çamur numunesi alınarak GC/MS, LC/MSMS ve ICP/MS?de MK ölçülmüştür. Tesislerin ve arıtma proseslerinin MK verileri arasındaki ilişkilerin anlamlılığının istatistiksel olarak ortaya konulabilmesi için saçılma diyagramları, kutu diyagramlar, varyans analizi ve Korelasyon hesaplamaları yapılmıştır. Atıksularda en yüksek konsantrasyonda tespit edilen MK?ler; sırası ile Ni, DEHP, Nonil fenol, Naftalin, Pb ve Diklofenak?dır. VOC?ler en düşük konsantrasyondadır ve görülme sıklığı çok düşüktür. Her üç AAT?de arıtılmış atıksuda; Ni, Pb, Hg, DEHP, Nonil fenol, Naftalin, Diklofenak, Karbamazepin hariç olmak üzere diğer MK?ler 100 ng/L?den düşüktür. %80?in üzerinde verimle arıtılan MK?ler Konya AAT?de Oktil fenol (%93,4), Nonil fenol (%92,7), Atrazin (%92,1), Naftalin (%91,5) ve DEHP (%89,3); Ereğli AAT?de Atrazin (%83,8)?dir. Zincirlikuyu AAT?de hiçbir parametrede %80?den fazla giderim gerçekleşmemiştir. Konya AAT?de Karbamazepin ve Ni, Zincirlikuyu AAT?de Nonil fenol negatif kütle dengesi görülen ve çıkış suyunda konsantrasyonları artan MK?lerdir. Membran proseslerden, UF?in MK gideriminde etkin olmadığı, NF?in 17 MK?de %50?den fazla giderim sağladığı görülmüştür. RO, NF?den sonra bazı MK?lerde ilave giderim sağlamıştır.
Citation - WoS: 3
Citation - Scopus: 5
Covid-19 Isolation Control Proposal Via Uav and Ugv for Crowded Indoor Environments: Assistive Robots in the Shopping Malls
(Frontiers Media Sa, 2022) Aslan, Muhammet Fatih; Hasikin, Khairunnisa; Yusefi, Abdullah; Durdu, Akif; Sabancı, Kadir; Azizan, Muhammad Mokhzaini
Artificial intelligence researchers conducted different studies to reduce the spread of COVID-19. Unlike other studies, this paper isn't for early infection diagnosis, but for preventing the transmission of COVID-19 in social environments. Among the studies on this is regarding social distancing, as this method is proven to prevent COVID-19 to be transmitted from one to another. In the study, Robot Operating System (ROS) simulates a shopping mall using Gazebo, and customers are monitored by Turtlebot and Unmanned Aerial Vehicle (UAV, DJI Tello). Through frames analysis captured by Turtlebot, a particular person is identified and followed at the shopping mall. Turtlebot is a wheeled robot that follows people without contact and is used as a shopping cart. Therefore, a customer doesn't touch the shopping cart that someone else comes into contact with, and also makes his/her shopping easier. The UAV detects people from above and determines the distance between people. In this way, a warning system can be created by detecting places where social distance is neglected. Histogram of Oriented-Gradients (HOG)-Support Vector Machine (SVM) is applied by Turtlebot to detect humans, and Kalman-Filter is used for human tracking. SegNet is performed for semantically detecting people and measuring distance via UAV. This paper proposes a new robotic study to prevent the infection and proved that this system is feasible.
Evaluation of Most Visited Web Sites in Turkey in Aspects of Structure and Security
(2018) Daşdemir, Atakan; Örnek, Mustafa Nevzat; Örnek, Humar Kahramanlı
Applications on World Wide Web have made our daily lives easier with their basic and fast access, neglecting time and place, they have become indispensable. It made Web applications a popular target for malevolent users and increased web security risk. In this study web penetration test which is indispensable for web security and threating risks for web security are mentioned. In Turkey, 60 of the most visited sites were identified in five different categories scanned as an ordinary user to consider a safety assessment of the general situation of the websites. For the review, large sites in news sites, e-commerce, government, universities and other categories have been selected that are thought to have strong security infrastructure. The knowledge about these sites such as used technologies and infrastructure which considers as vulnerability of sites and can be obtained by the ordinal person who uses penetration tests has been investigated in this study. As a result of the research, operating system information and web server information from 62% and 87% of the reviewed sites were identified respectively. Medium and low degree vulnerabilities were found in all scanned websites. With the vulnerability screening tests, weakness map revealed and information about the most identified weaknesses was given
Havucun Boy ve Çap Verileri Kullanılarak Hacminin Hesaplanması için Matematiksel Model Geliştirilmesi
(2019) Örnek, Mustafa Nevzat; Kahramanlı Örnek, Humar
Havuç, dünyada patatesten sonra en çok üretimi yapılan sebzedir. Türkiye’de havucun en çok yetiştirildiği bölge Konya iline bağlı Kaşınhanı’dır. Bu nedenle çalışmada uygulama amacı ile Kaşınhanı’nda üretilen havuçlar seçilmiştir. Toplam 464 adet Nantes türü havuç kullanılmıştır. Havuçların boyu, 5 santimetre ara ile çapları ve hacimleri ölçülmüştür. Daha sonra sunulan yöntem ile havuçların hacimleri hesaplanmış ve gerçek hacimlerle karşılaştırılmıştır. Tüm havuçlar için hesaplanan hacim ile ölçülen hacim arasındaki R2 değeri 0,9 olarak bulunmuştur. Ölçülen ve hesaplanan değerler arasında korelasyon doğrusunun eğimi 1,06 olmuştur ki, bu da ideal değere çok yakındır.
Citation - WoS: 3
Citation - Scopus: 5
Improving Artificial Algae Algorithm Performance by Predicting Candidate Solution Quality
(PERGAMON-ELSEVIER SCIENCE LTD, 2020) Yibre, Abdulkerim Mohammed; Koçer, Barış
The success of optimization algorithms is most of the time directly proportional to the number of fitness evaluations. However, not all fitness evaluations lead to successful fitness updates. Besides, the maximum number of fitness evaluations is limited and also balance of exploration and exploitation is still challenging. Best possible solution should be found in a reasonable time. Surely it can be said more fitness evaluation takes more time. Since methods are tested under fixed numbers of maximum fitness evaluation and the duration of each fitness evaluation of a problem may vary depending on the characteristic of the problem, finding best result with fewer fitness evaluations is challenging in optimization algorithms. For that reason in this study, we proposed a new method that predicts the quality of a candidate solution before evaluation of its fitness employing Gaussian-based Naive Bayes probabilistic model. If the candidate solution is predicted to generate good result then that solution is evaluated by the objective function. Otherwise new candidate solution is created as usual. The primary purpose of the proposed method is improving the performance of AAA and at the same time preventing unnecessary fitness evaluation. The proposed method is evaluated using standard benchmark functions and CEC'05 test suite. The obtained results suggests that the new method outperformed the basic AAA and other state-of-the-art meta-heuristic algorithms with fewer fitness evaluations. Thus, the new method can be extended to cost sensitive industrial problems. (c) 2020 Elsevier Ltd. All rights reserved.
Citation - WoS: 103
L-Hsab: a Levantine Twitter Dataset for Hate Speech and Abusive Language
(ASSOC COMPUTATIONAL LINGUISTICS-ACL, 2019) Mulki, Hala; Haddad, Hatem; Ali, Chedi Bechikh; Alshabani, Halima
Hate speech and abusive language have become a common phenomenon on Arabic social media. Automatic hate speech and abusive detection systems can facilitate the prohibition of toxic textual contents. The complexity, informality and ambiguity of the Arabic dialects hindered the provision of the needed resources for Arabic abusive/hate speech detection research. In this paper, we introduce the first publicly-available Levantine Hate Speech and Abusive (L-HSAB) Twitter dataset with the objective to be a benchmark dataset for automatic detection of online Levantine toxic contents. We, further, provide a detailed review of the data collection steps and how we design the annotation guidelines such that a reliable dataset annotation is guaranteed. This has been later emphasized through the comprehensive evaluation of the annotations as the annotation agreement metrics of Cohen's Kappa (k) and Krippendorff's alpha (alpha) indicated the consistency of the annotations.
Citation - WoS: 8
Citation - Scopus: 10
Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features
(Elsevier Sci Ltd, 2022) Balcı, Mehmet; Taşdemir, Şakir; Özmen, Güzin; Gölcük, Adem
Sleep-disordered breathing is a disease that many people experience unconsciously and can have very serious consequences that can result in death. Therefore, it is extremely important to analyze the data obtained from the patient during sleep. It has become inevitable to use computer technologies in the diagnosis or treatment of many diseases in the medical field. Especially, advanced software using artificial intelligence methods in the diagnosis and decision-making processes of physicians is becoming increasingly widespread. In this study, we aimed to classify the sleep-disordered breathing type by using machine learning techniques utilizing time and time- fre-quency domain features. We used Pressure Flow, ECG, Pressure Snore, SpO2, Pulse and Thorax data from among the polysomnography records of 19 patients. We employed digital signal processing methods for six types of physiological data and obtained a total of 35 features using different feature extraction methods for five different classes (Normal, Hypopnea, Obstructive Apnea, Mixed Apnea, Central Apnea). Finally, we applied machine learning algorithms (Artificial Neural Network, Support Vector Machine, Random Forest, Naive Bayes, K Nearest Neighborhood, Decision Tree and Logistic Regression) on 5-class and 35-feature data sets. We used10 fold cross validation to verify the classification success. Our main contribution to the literature is that we developed a classification system to score all four different types of sleep-disordered breathing simultaneously by using 6 types of PSG data. As a five-class scoring result, the Random Forest (RF) algorithm showed the highest success with 76.3 % classification accuracy. When Hypopnea was excluded from the evaluation, classification accuracy increased to 86.6% for three Apnea-type disorders. Our proposed method provided 89.7% accuracy for the diagnosis of Obstructive Apnea by the RF classifier. The results show that time and time-frequency domain features are distinctive in Sleep-disordered breathing scoring, which is a very difficult process for physicians and a diagnostic support system can be design by evaluating many PSG data simultaneously.
Citation - Scopus: 7
Multi-View Cnn With Mlp for Diagnosing Tuberculosis Patients Using Ct Scans and Clinically Relevant Metadata
(CEUR-WS, 2019) Mossa, A.A.; Yibre, A.M.; Çevik, U.
We propose a hybrid approach of multi-view convolutional neural networks with Multi-Layer Perceptron to generate an automatic medical CT report and evaluation of the severity stage of Tuberculosis patients, trained and evaluated on 335 chest 3D CT images and available metadata provided by Im-ageCLEF2019 organizers for the participants of tuberculosis computation track. Transfer learning and data augmentation techniques were applied to avoid over fitting and enhance performance of the model. Our multi-view CNN approach comprises the decomposition of the 3D CT image into 2D axial, coronal and sagittal slices and converting them to PNG format as preliminary to training. At the first stage, coronal and sagittal slices were used to train the CNN classifier using pre-trained AlexNet. In the second stage, MLPs were trained using features extracted during stage one alongside with the provided metadata. Our results ranked 6th and 4th ,with an AUC of 0.763 in predicting whether the severity stage is High or Low, and mean AUC of 0.707 in detecting whether left and right lungs are affected or not, detecting the absence or presence of calcifications, caverns, pleurisy and lung capacity decrease, respectively. © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Citation - WoS: 3
Citation - Scopus: 4
Optimization of Electricity Generation Parameters With Microbial Fuel Cell Using the Response Surface Method
(Springer Heidelberg, 2022) Neşeli, Süleyman; Dinçer, Kevser; Taşdemir, Şakir; Hayder, Mustafa Akram
Due to an ever-increasing population and developments in technology, the demand for energy has been increasing daily. In order to meet this demand, renewable and alternative energy sources that do not harm the environment are often recommended. One of these alternative sources can be obtained from human wastewater and is known as the Microbial Fuel Cell (MFC). This study aimed to estimate the electricity production performance of MFC, using Nafion 117 membrane with 10 x 10 and 11 x 11 cm(2) area by both active and sediment sludges. The responses obtained from 16 physical experiments performed according to the L-16(2(4) x 2(2)) Taguchi orthogonal index were analyzed by the response surface method (RSM). According to the analysis, it was determined that the quadratic polynomial equations created for the estimation of the reaction values of the active and sediment sludge had validity over 90%. The ANOVA analysis applied to determine parameter ethicalities also showed that the most effective parameter on the reactions from the sludge types considered was the resistance at different rates. Thus, the results suggest that the orthogonal design-based RSM model is an effective tool that provides key information for optimizing energy harvesting from MFC technology and saves time concerning experimental work.
Citation - Scopus: 8
Performance Comparison of Extreme Learning Machines and Other Machine Learning Methods on Wbcd Data Set
(Institute of Electrical and Electronics Engineers Inc., 2021) Keskin, O.S.; Durdu, A.; Aslan, M.F.; Yusefi, A.
Breast cancer is one of the most common forms of cancer among women in our country and the world. Artificial intelligence studies are growing in order to reduce the mortality and early diagnosis needed for appropriate treatment. The Excessive Learning Machines (ELM) method, one of the machine learning approaches, is applied to the Wisconsin Breast Cancer Diagnostic (WBCD) dataset in this study, and the findings are compared to those of other machine learning methods. For this purpose, the same dataset is also classified using Multi-Layer Perceptron (MLP), Sequential Minimum Optimization (SMO), Decision Tree Learning (J48), Naive Bayes (NB), and K-Nearest Neighbor (KNN) methods. According to the results of the study, the ELM approach is more successful than other approaches on the WBCD dataset. It's also worth noting that as the number of neurons in the ELM grows, so does the learning ability of the network. However, after a certain number of neurons have passed, test performance begins to decline sharply. Finally, the ELM's performance is compared to the results of other studies in the literature. © 2021 IEEE.
Retinal Hastalıkların Teşhisi için Optik Koherans Tomografi Görüntülerinin Derin Öğrenme Metotları ile Sınıflandırılması
(2023) Urmamen Hafiza Esra; Koçer Sabri
Retina, görmeyi sağlayan ışığa ve renklere duyarlı ağ tabakasıdır. Retinadaki bozulmalar insanların yaşam kalitesini düşürmektedir. Retinada meydana gelen hasarlar körlüğe varan ciddi sorunlara sebep olabilmekte ve retinada kalıcı hasarlar meydana gelebilmektedir.Retinal hastalıkların tedavisinde gelişen teknoloji ile birlikte bilgisayarlı tanı sistemlerinin kullanımı oldukça yaygınlaşmıştır. Erken teşhis ve tedavi edilmesi retina da oluşabilecek kalıcı hasarları ve hastaların görme yetisini kaybetmesini önlemektedir Teknolojinin ilerlemesiyle birlikte fotoğraf makineleri ve bilgisayarlı tanı sistemleri oldukça yaygın kullanılmaya başlanmıştır. OCT cihazları kullanılarak elde edilen retinal görüntüler uzmanların daha doğru ve erken teşhis koymalarını sağlamaktadır. Bu çalışmada, retinal hastalıkların sınıflandırılması için transfer öğrenme yöntemlerinden InceptionV3, Xception ve önerilen Evrişimsel Sinir Ağı (ESA) modeli karşılaştırılmıştır.Xception ağında %95.36 oranında doğruluk değerine, Inception ağında ise %98.2 oranında doğruluk oranı elde edilmiştir. Önerin ESA mimarisinde %97.51 oranında doğruluk oranı elde edilmiştir. Önerilen mimari hastalık bazında diyabet ve normal hastalıkların sınıflandırılmasında diğer yöntemlerden daha başarılı sonuçlar elde etmiştir.
Citation - WoS: 24
Citation - Scopus: 33
Semen Quality Predictive Model Using Feed Forwarded Neural Network Trained by Learning-Based Artificial Algae Algorithm
(ELSEVIER - DIVISION REED ELSEVIER INDIA PVT LTD, 2021) Yibre, Abdulkerim Mohammed; Koçer, Barış
Recent scientific studies have noted that the seminal quality of males is significantly decreasing due to lifestyle and environmental factors. Clinical diagnosis of sperm quality is one important aspect of identifying the potential of semen for the occurrence of pregnancy. Due to the advances in machine learning algorithms, especially the reliable and high classification accuracy of neural network in health related problems, it is becoming possible to predict seminal quality from lifestyle data. In this respect, a few attempts were made in predicting seminal quality. These studies were conducted using imbalanced data sets, where the performance outcomes tend to be biased towards the majority class. Other studies implemented the gradient descent technique for training the neural network. The gradient descent is a local training technique that is prone to get stuck to local minima. On the contrary, meta-heuristic algorithms enable searching solutions both locally and globally. Therefore, in this study, Artificial Algae Algorithm that is improved using a Learning-Based fitness evaluation method is proposed for training Feed Forward Neural Network (FFNN). In addition, the SMOTE data balancing method was employed to balance normal and abnormal instances. Experimental analyses were carried out to evaluate the predictive accuracy of the FFNN trained using Learning-Based Artificial Algae Algorithm (FFNN-LBAAA). The results were compared with well-known machine learning algorithms, namely: Multi-layer Perceptron Neural Network (MLP), Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) algorithms. The proposed approach showed superior performance in discriminating normal and abnormal semen quality instances over the other compared algorithms. (C) 2020 Karabuk University. Publishing services by Elsevier B.V.
Citation - WoS: 42
Citation - Scopus: 74
T-Hsab: a Tunisian Hate Speech and Abusive Dataset
(SPRINGER INTERNATIONAL PUBLISHING AG, 2019) Haddad, Hatem; Mulki, Hala; Oueslati, Asma
Since the Jasmine Revolution at 2011, Tunisia has entered a new era of ultimate freedom of expression with a full access into social media. This has been associated with an unrestricted spread of toxic contents such as Abusive and Hate speech. Considering the psychological harm, let alone the potential hate crimes that might be caused by these toxic contents, automatic Abusive and Hate speech detection systems become a mandatory. This evokes the need for Tunisian benchmark datasets required to evaluate Abusive and Hate speech detection models. Being an underrepresented dialect, no previous Abusive or Hate speech datasets were provided for the Tunisian dialect. In this paper, we introduce the first publicly-available Tunisian Hate and Abusive speech (T-HSAB) dataset with the objective to be a benchmark dataset for automatic detection of online Tunisian toxic contents. We provide a detailed review of the data collection steps and how we design the annotation guidelines such that a reliable dataset annotation is guaranteed. This was later emphasized through the comprehensive evaluation of the annotations as the annotation agreement metrics of Cohen's Kappa (k) and Krippendorff's alpha (alpha) indicated the consistency of the annotations.
Citation - Scopus: 11
A Tutorial: Mobile Robotics, Slam, Bayesian Filter, Keyframe Bundle Adjustment and Ros Applications
(Springer Science and Business Media Deutschland GmbH, 2021) Aslan, Muhammet Fatih; Durdu, Akif; Yusefi, A.; Sabancı, Kadir; Sungur, C.
Autonomous mobile robots, an important research topic today, are often developed for smart industrial environments where they interact with humans. For autonomous movement of a mobile robot in an unknown environment, mobile robots must solve three main problems; localization, mapping and path planning. Robust path planning depends on successful localization and mapping. Both problems can be overcome with Simultaneous Localization and Mapping (SLAM) techniques. Since sequential sensor information is required for SLAM, eliminating these sensor noises is crucial for the next measurement and prediction. Recursive Bayesian filter is a statistical method used for sequential state prediction. Therefore, it is an essential method for the autonomous mobile robots and SLAM techniques. This study deals with the relationship between SLAM and Bayes methods for autonomous robots. Additionally, keyframe Bundle Adjustment (BA) based SLAM, which includes state-of-art methods, is also investigated. SLAM is an active research area and new algorithms are constantly being developed to increase accuracy rates, so new researchers need to understand this issue with ease. This study is a detailed and easily understandable resource for new SLAM researchers. ROS (Robot Operating System)-based SLAM applications are also given for better understanding. In this way, the reader obtains the theoretical basis and application experience to develop alternative methods related to SLAM. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Citation - WoS: 3
Citation - Scopus: 5
Weight Optimization of Hybrid Composite Laminate Using Learning-Oriented Artificial Algae Algorithm
(SPRINGER INTERNATIONAL PUBLISHING AG, 2020) Yibre, Abdulkerim Mohammed; Koçer, Barış; Esleman, Esmael Adem; Önal, Gürol
The optimal design of parameters is vital for the effective use of hybrid composite laminated structures. This is due to a highly dependent property of laminated composite structures strength on its fiber orientation, stacking sequence and the number of ply in each laminate. The main aim of this study is to apply Learning-Oriented Artificial Algae Algorithm for optimization of the weight of rectangular hybrid composite laminated plate subjected to compressive in-plane loading. The design parameters are number of plies and stacking sequence of the laminate. The critical buckling factor is the constraint of the optimization process. The parameters of the hybrid composite plate are optimized using Learning-Oriented Artificial Algae Algorithm with the aim of minimizing weight.The performance of the algorithm was compared with previous studies that employed the GA and ACO algorithms. The Learning-Oriented method is integrated to reduce the number of functions evaluated and in turn reducing computational cost. The results showed that Learning-Oriented Artificial Algae Algorithm outperformed GA and ACO, and hence can be successfully applied in the optimization of laminated composite structures.

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