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Öğe An atom search optimization approach for IIR system identification(Taylor & Francis Inc, 2023) Ekinci, Serdar; Budak, Cafer; Izci, Davut; Gider, VeyselFiltering, or digital signal processing, is a significant and fundamental requirement in fields such as signal systems and computers. The process of designing optimal digital filters is difficult, which has led researchers to design filters using emerging evolutionary computations. Metaheuristics have emerged as the most promising tool for solving optimization problems, with excellent development and improvement. However, it has not been clear how to select the best performing metaheuristic to design an optimal digital filter. In this paper, a digital infinite impulse response (IIR) filter is constructed using the atom search optimization (ASO) algorithm impressed by the physical motion of atoms in nature based on molecular dynamics. The simulation results obtained are extensively compared with the results of other optimization algorithms such as moth flame optimization, gravitational search algorithm and artificial bee colony optimization. ASO was found to have the highest percentage of improvement. Furthermore, eight cases are analyzed across four numerical filter instances with the same degree and four with reduced degree, and the results are validated by outperforming several different algorithm-based approaches in the literature. The stability analysis on the basis of pole zero diagrams further cements the efficacy of the ASO for IIR system identification problem.Öğe Daily solar radiation prediction using LSTM neural networks(Institute of Electrical and Electronics Engineers Inc., 2022) Gider, Veysel; Budak, Cafer; İzci, Davut; Ekinci, SerdarThe integration of solar energy with the smart grids and existing infrastructure makes it a cost-effective and environmentally-friendly solution to address the growing energy need. To make use of the potential of solar energy, several challenges such as the stability of generated energy and the supply-demand imbalance must be overcome. In this regard, an accurate forecast model for global solar radiation (GSR) can be useful for power generation planning and system reliability. The GSR estimate is regarded as the most significant and critical element in defining solar system characteristics, thus, it is crucial in predicting the generated energy. This work, therefore, employs long-short-term memory (LSTM) as a deep learning method to successfully estimate solar irradiance and capture the stochastic fluctuations. In this respect, the measurement data (from year 2021) obtained from the station installed in Dicle University (Turkey), Science and Technology Application and Research Centre (DUBTAM) were used, and the efficiency of the proposed method was evaluated. © 2022 IEEE.Öğe Determining similarities of COVID-19-lung cancer drugs and affinity binding mode analysis by graph neural network-based GEFA method(Taylor & Francis, 2021) Budak, Cafer; Mençik, Vasfiye; Gider, VeyselCOVID-19 is a worldwide health crisis seriously endangering the arsenal of antiviral and antibiotic drugs. It is urgent to find an effective antiviral drug against pandemic caused by the severe acute respiratory syndrome (Sars-Cov-2), which increases global health concerns. As it can be expensive and time-consuming to develop specific antiviral drugs, reuse of FDA-approved drugs that provide an opportunity to rapidly distribute effective therapeutics can allow to provide treatments with known preclinical, pharmacokinetic, pharmacodynamic and toxicity profiles that can quickly enter in clinical trials. In this study, using the structural information of molecules and proteins, a list of repurposed drug candidates was prepared again with the graph neural network-based GEFA model. The data set from the public databases DrugBank and PubChem were used for analysis. Using the Tanimoto/jaccard similarity analysis, a list of similar drugs was prepared by comparing the drugs used in the treatment of COVID-19 with the drugs used in the treatment of other diseases. The resultant drugs were compared with the drugs used in lung cancer and repurposed drugs were obtained again by calculating the binding strength between a drug and a target. The kinase inhibitors (erlotinib, lapatinib, vandetanib, pazopanib, cediranib, dasatinib, linifanib and tozasertib) obtained from the study can be used as an alternative for the treatment of COVID-19, as a combination of blocking agents (gefitinib, osimertinib, fedratinib, baricitinib, imatinib, sunitinib and ponatinib) such as ABL2, ABL1, EGFR, AAK1, FLT3 and JAK1, or antiviral therapies (ribavirin, ritonavir-lopinavir and remdesivir).Öğe Drug Solubility Prediction: A Comparative Analysis of GNN, MLP, and Traditional Machine Learning Algorithms(Gazi University, 2024) Gider, Veysel; Budak, CaferThe effective development and design of pharmaceuticals hold fundamental importance in the fields of medicine and the pharmaceutical industry. In this process, the accurate prediction of drug molecule solubility is a critical factor influencing the bioavailability, pharmacokinetics, and toxicity of drugs. Traditionally, mathematical equations based on chemical and physical properties have been used for drug solubility prediction. However, in recent years, with the advancement of artificial intelligence and machine learning techniques, new approaches have been developed in this field. This study evaluated different modeling approaches consisting of Graph Neural Networks (GNN), Multilayer Perceptron (MLP), and traditional Machine Learning (ML) algorithms. The Random Forest (RF) model stands out as the optimal performer, manifesting superior efficacy through the attainment of minimal error rates. It attains a Root Mean Square Error (RMSE) value of 1.2145, a Mean Absolute Error (MAE) value of 0.9221, and an R-squared (R2) value of 0.6575. In contrast, GNN model displays comparatively suboptimal performance, as evidenced by an RMSE value of 1.8389, an MAE value of 1.4684, and an R2 value of 0.2147. These values suggest that the predictions of this model contain higher errors compared to other models, and its explanatory power is lower. These findings highlight the performance differences among different modeling approaches in drug solubility prediction. The RF model is shown to be more effective than other methods, while the GNN model performs less effectively. This information provides valuable insights into which model should be preferred in pharmaceutical design and development processes.Öğe Grafik sinir ağları ile ilaç keşfi(Dicle Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Gider, Veysel; Budak, Caferİlaç keşfi, yüksek maliyetler, düşük başarı oranları ve uzun süreçler nedeniyle zorlu ve karmaşık bir alandır. COVID-19 pandemisi gibi küresel sağlık krizleri, bu sürecin daha hızlı, etkili ve yenilikçi çözümler gerektirdiğini ortaya koymuştur. Bu tez çalışması, grafik sinir ağları (GSA) kullanarak ilaç keşfi süreçlerini hızlandırmayı ve maliyetleri azaltmayı amaçlamaktadır. GSA'lar, düğümler ve kenarlarla ifade edilen grafik yapılarının analizine olanak tanıyan güçlü algoritmalardır. Bu algoritmalar, moleküler yapıları ve etkileşimleri modelleyerek ilaç-protein bağlanma tahmini, ilaç benzerliği analizi, ilaç iskeleleri çıkarma ve ilaç yan etkilerinin tahmini gibi çeşitli uygulamalarda etkili bir şekilde kullanılabilir. Özellikle ilaçların yeniden kullanımı stratejisi, mevcut onaylı ilaçların yeni tedavi alanlarına adapte edilmesi veya farklı hastalıkların tedavisinde kullanılması anlamına gelir. Bu strateji, pandemi gibi acil durumlarda hızlı ve etkili çözümler sunma potansiyeline sahiptir. GSA'lar, bu yeniden kullanım sürecinde önemli bir rol oynayarak, mevcut ilaçların yeni hedefler için uygunluğunu hızlı ve doğru bir şekilde belirleyebilir. GSA'nın bilim dünyasındaki ilerleyişi, özellikle Türkiye'de öncü çalışmalar yapılmasını sağlamıştır. Öncü çalışmalardan biri olma özelliği taşıyan bu tez çalışmasında, DrugBank ve PubChem gibi kaynaklardan elde edilen moleküler yapılar, GSA modelleri ile analiz edilmiş ve ilaç-protein etkileşimleri tahmin edilmiştir. Kullanılan yöntemler arasında atom çifti benzerlik analizi, Tanimoto benzerliği ve moleküler parmak izi teknikleri bulunmaktadır. Ayrıca kinaz inhibitörleri üzerinde de yoğunlaşılmıştır. Kinaz inhibitörleri, hedef proteinlerle etkileşime girerek aktivitelerini inhibe eden bileşiklerdir ve kanser tedavisinde kritik bir rol oynar. Bulgular, GSA'ların COVID-19, Ebola gibi acil durumlarda ilaç keşfi ve yeniden kullanımındaki etkinliğini göstermiştir. GSA'ların ilaç keşfi süreçlerini hızlandırma ve maliyetleri azaltma potansiyelini ortaya koymaktadır. GSA'ların ilaç keşfi ve geliştirme süreçlerinde daha geniş kapsamlı araştırmalarda kullanılmasının ve bu alandaki teknolojik gelişmelerin yakından takip edilmesinin önemi vurgulanmaktadır.Öğe Instruction of molecular structure similarity and scaffolds of drugs under investigation in ebola virus treatment by atom-pair and graph network: A combination of favipiravir and molnupiravir(Elsevier Ltd, 2022) Gider, Veysel; Budak, CaferThe virus that causes Ebola is fatal. Although many researchers have attempted to contain this deadly infection, the fatality rate remains high. The atom-pair fingerprint technique was used to compare drugs suggested for the treatment of Ebola or those that are currently being tested in clinical settings. Subsequently, using scaffold network graph (SNG) methods, the molecular and structural scaffolds of the drugs chosen based on these similar results were created, and the drug structures were examined. Public databases (PubChem and DrugBank) and literature regarding Ebola treatment were used in the analysis. Graphical representations of the molecular architecture and core structures of the drugs with the highest similarity to Food and Drug Administration (FDA)-approved drugs were produced using the SNG method. The combination of molnupiravir, the first licensed oral medication candidate for COVID-19, and favipiravir, employed in other viral outbreaks, should be further researched for treating Ebola, as observed in our study. We also believe that chemists will benefit from understanding the core structure(s) of medication molecules effective against the Ebola virus, their inhibitors, and the chemical structure similarities of existing pharmaceuticals utilized to build alternative drugs or drug combinations.Öğe LSTM based forecasting of the next day’s values of ionospheric total electron content (TEC) as an earthquake precursor signal(Springer Science and Business Media Deutschland GmbH, 2023) Budak, Cafer; Gider, VeyselThe sudden vibrations that occur due to the fractures in the Earth’s crust, spreading in waves and shaking the Earth’s surface, are natural disaster that causes significant loss of life and property. To take measures against these destructive effects, it is important to be able to forecast the occurrence time of an earthquake in advance. However, although earthquake experts can forecast which fault line the next earthquake may occur on by monitoring the movements in the fault lines, they cannot accurately forecast the exact timing. Detection of earthquake precursor signals a few days before the earthquake has become an increasingly popular field of interest. Strong correlations have been observed between earthquakes and ionospheric parameters in recent years. Total Electron Content (TEC) is an important parameter that can be affected by seismic activity in ionospheric studies and has been investigated as a potential earthquake precursor by many researchers. It has been observed that earthquakes cause significant disturbances and changes in TEC values, which are one of the ionospheric parameters. The ability to identify earthquake precursor signals before an earthquake occurs is critically important for earthquake detection. We evaluate the performance of the proposed approach using GPS-TEC data obtained from numerous ground-based GPS stations in earthquake-prone regions of Turkey, Italy, Japan, and China. In this study, TEC values in six different regions where earthquakes with Mw > 5.6 occurred were forecasted one day before the earthquake using LSTM. The results showed that the LSTM model achieved an R-square (R2) value of at least 0.9982 and the root mean square error (RMSE) value of at most 0.2302 for all experimental earthquake days used. The proposed approach may be useful for monitoring ionospheric anomalies and potentially developing an early warning system for earthquakes.Öğe PID controller design for DFIG-based wind turbine via reptile search algorithm(Institute of Electrical and Electronics Engineers Inc., 2022) İzci, Davut; Ekinci, Serdar; Budak, Cafer; Gider, Veysel; 0000-0001-8359-0875This paper presents a new design procedure for a doubly fed induction generator (DFIG) based wind energy conversion (WEC) system in a wind turbine (WT) using a proportional-integral-derivative (PID) controller and a recent metaheuristic approach known as reptile search algorithm (RSA). As the control scheme has a significant role on the efficiency and reliability of DFIG-based WEC system, we aim to propose the RSA tuned PID controller as the most efficient approach to operate this system. To demonstrate the efficiency and reliability of the proposed design method, previously reported design schemes such as gravitational search algorithm, bacterial foraging optimization and particle swarm optimization based PID controller approaches were used for comparisons. The obtained results showed that the proposed reptile search algorithm tuned PID controller with 6th order transfer function model of doubly fed induction generator enhances the transient performance considerably compared to other reported design approaches for wind energy conversion system. © 2022 IEEE.
