Authors:Yunus Emre KARA; Yadigar GÜLSEVEN SIDIR, Sabit HOROZ Abstract: We have been investigated in different solvent medium to interactions between 4-Ethoxybenzoic acid (4EOBA), 4-Pentylbenzoic acid (4PentBA), and 4-Pentylphenyl 4-Methylbenzoate (4PP4MetB) liquid crystals with CdS, CdSe and ZnS nanoparticles. For this, the new materials composed from LC compound including the solvent and nanoparticle were investigated by use absorbance and fluorescence spectra. Electronic transitions and differences in absorbance and fluorescence spectra were interpreted. The fluorescence of liquid crystals has been defined in the shifts between wavelengths of the fluorescence of nanomaterials. It can be said that it has occurred blue shift at the maximum fluorescence wavelengths of the 4EOBA-CdS-DMSO and 4EOBA-CdSe-DMSO compared to 4EOBA-DMSO. On the other hand, we can say that in the 4EOBA-ZnS-DMSO solution occurs red shift in the fluorescence band, while the peaks seen in the fluorescence band occur in result of interaction of liquid crystals and nanoparticles. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Sertaç YAMAN; Yavuz EROL Abstract: Image processing and embedded systems are used in many applications such as object recognition, robotic applications, autonomous and remote control systems developed for the defense industry, medical applications, face recognition, and vehicle license plate recognition. Many vehicle license number plate detection methods are not effective under vehicle license plate images have a degree of rotation or low resolution images. Thus, we used MVSR normalization algorithm to detect vehicle license plate recognition for better accuracy and lower computational cost. The MVSR normalization algorithm, Mean–Variance-Softmax-Rescale processes respectively is applied for high-accuracy real-time vehicle license plate detection. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Duygu MURAT; Uğur TEKİN Abstract: Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder. Pharmacological treatment is the first option in the treatment of ADHD. Long-term treatment is required, as the course of the disease often continues throughout life. Long-term adherence to treatment in ADHD is important in terms of reducing the risk of substance use, accidents, and risk-taking behaviours. Medication adherence is very important for the successful outcome of the treatment. In this study, we aimed to evaluate the factors that may affect treatment adherence in the Turkish sample. We found that 39.6% of children with ADHD had high medication adherence accordingly Morisky Medication Adherence Scale. Medication adherence of boys and girls were similar. When the education level of mothers was compared, the education level of mothers with high medication adherence group was higher than those with poor medication adherence group, and this difference was statistically significant (p=0.013). We can say that highly educated families who learn that ADHD is a neurodevelopmental disorder and can realize the risks that may arise as a result of being untreated are more careful about drug use. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Mehmet NERGİZ Abstract: Strawberry fruits which are rich in vitamin A and carotenoids offer benefits for maintaining healthy epithelial tissues and promoting maturity and growth. The intensive cultivation and swift maturation of strawberries make them susceptible to premature harvesting, leading to spoilage and financial losses for farmers. This underscores the need for an automated detection method to monitor strawberry development and accurately identify growth phases of fruits. To address this challenge, a dataset called Strawberry-DS, comprising 247 images captured in a greenhouse at the Agricultural Research Center in Giza, Egypt, is utilized in this research. The images of the dataset encompass various viewpoints, including top and angled perspectives, and illustrate six distinct growth phases: "green", “red”, "white", "turning", "early-turning" and "late-turning". This study employs the Yolo-v7 approach for object detection, enabling the recognition and classification of strawberries in different growth phases. The achieved mAP@.5 values for the growth phases are as follows: 0.37 for "green," 0.335 for "white," 0.505 for "early-turning," 1.0 for "turning," 0.337 for "late-turning," and 0.804 for "red". The comprehensive performance outcomes across all classes are as follows: precision at 0.792, recall at 0.575, mAP@.5 at 0.558, and mAP@.5:.95 at 0.46. Notably, these results show the efficacy of the proposed research, both in terms of performance evaluation and visual assessment, even when dealing with distracting scenarios involving imbalanced label distributions and unclear labeling of developmental phases of the fruits. This research article yields advantages such as achieving reasonable and reliable identification of strawberries, even when operating in real-time scenarios which also leads to a decrease in expenses associated with human labor. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Mehmet Kaan KAYA; Burak TASCİ Abstract: Diabetic retinopathy is a significant complication occurring in the retina of the eye as a result of prolonged diabetes. When not detected early, this condition can lead to vision loss. Advanced image processing techniques and artificial intelligence algorithms have enhanced the possibilities of early diagnosis and treatment. This article discusses current advancements in artificial intelligence-based diabetic retinopathy detection and explores future possibilities in this field. In the experimental studies of the article, the Kaggle Aptos 2019 dataset was utilized. This dataset comprises 5 classes and a total of 3662 images. The class distribution is as follows: No DR (No Diabetic Retinopathy): 1805, Mild: 370, Moderate: 999, Severe: 193, Proliferative DR: 295. The study consists of four fundamental stages. These stages are (1) Feature extraction from VGG16 and VGG19 pretrained models, (2) Feature selection using NCA, Relieff, and Chi2, (3) Classification with Support Vector Machine classifier, (4) Iterative Majority Voting. Using the proposed method, a high accuracy of 99.18% is achieved. Furthermore, sensitivity of 100% for the No DR class, sensitivity of 100% for the Moderate class, sensitivity of 98.80% for the Severe class, and an F1-Score of 99.89% for the No DR class are obtained. This study demonstrates the effective utilization of machine learning methods in diabetic retinopathy diagnosis. The experimental results underscore the significant contributions of diabetic retinopathy patients' diagnosis and treatment processes. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Ömer SADAK Abstract: Contamination of drinking water with heavy metals is a serious threat to the global environment and public health. Currently, approximately 20 countries have been reported for arsenic levels present in drinking water that are higher than the EPA guidelines. Arsenic is highly toxic, widely dispersed and found in the earth’s crust. It can be found in inorganic as well as organic compounds in water. Arsenic is released into the environment in a variety of ways, including industrial effluents, pesticides, wood preservative chemicals, combustion of petroleum and coal, and mining operations. Currently, Arsenic is determined using a wide variety of methods that include inductively coupled plasma mass spectrometry (ICPMS), high-performance liquid chromatography (HPLC) with ICPMS and graphite furnace atomic absorption spectrometry (GFAAS). Nevertheless, these methods are slow, expensive and require skilled people to operate. Alternatively, electrochemical sensors have been potentially recognized as a powerful analytical method for the detection of heavy metals at very low concentrations. It also allows on-site and continuous monitoring of heavy metals. A nanocomposite consisting of gold nanoparticles and conducting polymers (polydiallyldimethylammonium chloride (PDDA) and polystyrene sulfonate (PSS)) functionalized graphene was used in this study to detect arsenic, which causes major environmental and health concerns. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Muhammed ÇUBUK Abstract: R&D and innovation activities are among the areas of critical importance for a country's national development and pioneering technological developments. R&D and innovation, which is an important field of study in terms of contributing to the evaluation of technical skills and regional specialization, are concepts that aim to achieve goals such as technological development, creation of new products and services, improvement of existing processes and increase in competitiveness. Accordingly, in this study, which focuses on ranking the R&D and innovation potential of provinces in Turkey, DEMATEL, ARAS and COPRAS methods, which are multi-criteria decision-making methods, were used. The weighting of the 12 criteria was done by DEMATEL method and 81 provinces were ranked by ARAS and COPRAS methods using these weight values. Maps were created according to the scores of the provinces from each method. According to the results obtained, most of the 81 provinces in Turkey showed similar results in both methods. A general evaluation was made according to these results and criteria. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Canan TAŞTİMUR; Erhan AKIN Abstract: Deep learning, which has seen frequent use in recent studies, has helped solve the problem of classifying objects of many different types and properties. Most studies both create and train a convolutional neural network (CNN) from scratch. The time spent training the network is thus wasted. Transfer learning (TL) is used both to prevent the loss of time due to training the dataset and to more effectively classify small datasets. This study performs classification using a dataset containing eighteen types of fastener. Our study contains three different TL scenarios. Two of them use TL with fine-tuning (FT), while the third does so with feature extraction (FE). The study compares the classification performance of eighteen different pre-trained network models (i.e., one or more versions of EfficientNet, DenseNet, InceptionResNetV2, InceptionV3, MobileNet, ResNet50, Xception, and VGGNet) in detail. When compared to other research in the literature, our first and second scenarios provide excellent implementations of TL-FT, while our third scenario, TL-FE, is hybrid and produces better results than the other two. Furthermore, our findings are superior to those of most previous studies. The models with the best results are DenseNet169 with an accuracy of 0.97 in the TL-FT1 scenario, EfficientNetB0 with 0.96 in TL-FT2, and DenseNet169 with 0.995 in TL-FE. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Barış Mert KADIOĞLU; Seçil KARATAY, Yücel ÇETİNCEVİZ, Faruk ERKEN Abstract: In recent years, forest fires can be brought under control in line with the information obtained from Unmanned Aerial Vehicles (UAVs), which play an important role in determining the progression of fires, detecting heat points and determining intervention locations. In this study, if the UAV detects the fire by autonomously positioning in the area where the fire is located, the point where the fire reaches the most intense temperature is determined with the help of the thermal camera, and it is ensured that the fireball is dropped to the target with a 100% success rate. The requirements of the UAV, which will be produced in order to realize this task, such as fast, load-carrying and stable flight are also taken into consideration. In addition to being economical and long-lasting of the materials inside, it will be able to fly efficiently in most weather conditions (foggy, dark, etc.). In the construction of the UAV, a domestic Electronic Speed Controller (ESC) with a unique design is produced to meet the sufficient current. With this acquisition, ESC, which will meet the requirements by sending sufficient current to more than one Brushless DC (BLDC) motor, has been tested on our Radio Controlled (RC) aircraft and included in the project. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Cihan ÖZEL; Cevher Kürşat MACİT, Meral ÖZEL Abstract: In this study, four solid fuel model rockets with conical, parabolic, power and haack series nose cones that can carry 4 kg payload at an average altitude of 3 km were designed in the OpenRocket program. Later, the notched delta fin model was mounted on these designed model rockets. The effects of this fin model on the changes in the speed, stability, acceleration, weight and altitude of the rockets were analyzed numerically in the OpenRocket program. As a result of the analysis, it was determined that the conical nose rocket showed the worst flight performance and the Haack series nose cone rocket model showed the best performance. When used with the notched delta fin of the Haack series model, it was determined that the rocket's altitude increased by 7.67%, and its speed increased by 1.83%, but decreased by 1.2% in mach number, 0.6% in weight, 0.3% in acceleration, and 4.5% in stability. As a result, it was seen that it would be beneficial to consider the nose cone and fin together when evaluating the flight performance of the rocket. The results obtained in the study have shown that the notched delta fin model can be used experimentally in defense industry and model rocket applications and the studies can be advanced. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Burak ŞAHİN; Aytuğ BOYACI Abstract: The rapid increase in data production, thanks to technological developments and scientific research, leads to the development of Machine Learning (ML) and similar new data analysis tools. It was announced that Amazon Web Services (AWS), a cloud service provider, stored 500EB of data in 2021 [1]. ML is an alternative to traditional engineering methods and does not require field knowledge of the problem to obtain a solution. However, the implementation of ML Algorithms can be complex depending on the content of the data set, and expert knowledge is the most important factor to use these algorithms effectively. Various methods have been developed to find a solution to this problem. There are many different areas and problems that machine learning can be applied to. We have limited our research to problems that can be solved using computer vision and AutoML. We have used AutoML and computer vision-based solutions to solve object classification, detection and segmentation problems. Our goal is to develop a platform that will work without the intervention of any expert. Users can load their datasets, choose the method they want, and train their models according to the problem they choose without any other intervention. After the training process is over, they can use their models in real time by transferring them over the platform in real time with their own hardware. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Furkan GÜNDAY Abstract: From past to present, building designs and materials used are developing. Especially against the destructive effects of ground movements and free vibrations on structures, many structural system designs and composite structure designs have been developed. The purpose of the composite structure design is to choose different types of materials according to the structural load-bearing system stress, in short, to choose the most advantageous material type according to the cross-sectional stresses or to eliminate the negative aspects of one material with the positive aspects of another material. It is a known fact that the dynamic performance of steel structure carrier systems is high under the influence of ground movements and free vibrations. However, in cases where the section geometry cannot be changed due to architectural concerns due to architectural design difficulties, there are cases where the rigidity of the structure is not sufficient. In such cases, profiles made of different materials other than steel can be used in order to increase the rigidity of the structure, especially in the columns, which are a very important component of the structural load-bearing elements. Therefore, in this study, the effect of using molybdenum profile instead of steel profile on modal parameters in model steel structure columns was investigated. In the light of the information obtained, a decrease of approximately 23.72 percent was observed in the period value in the 1st free vibration mode of the steel-molybdenum structure model. Thus, it is understood that the rigidity of the model steel structure system increases. In cases where it is not possible to change the architectural design in steel structures, it is recommended to use column profiles as molybdenum profiles instead of steel profiles in order to provide the necessary rigidity and increase rigidity. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Kübra TEKİN; Hasan GÜLER Abstract: EEG signals are noisy signals that allow brain activity to be analyzed. In recent years, it has been seen that current conveyor-based circuits, which have many advantages such as wide bandwidth, high linearity, low power consumption, have been used instead of operational amplifiers in the analysis of these signals. In this study, a second-generation current controlled current conveyor (CCCII+) low pass filter circuit with a cut-off frequency of 100 Hz has been presented for the analysis of EEG signals. The simulation of the circuit was carried out with the Orcad pspice program. In addition, the application circuit of this low-pass filter circuit has been made and oscilloscope images have been obtained for some frequency values. AD844 IC is used as current conveyor in the application circuit. The data of epilepsy patients and normal people taken from the University of Bonn were applied to the CCCII+ low pass filter circuit and the frequency bands of these signals were examined. It has been predicted that if this CCCII+ low pass filter circuit is used in EEG measurements, it will give good results in the diagnosis of neurological diseases such as epilepsy. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Fehmi ASLAN Abstract: Dye-sensitized solar cells (DSSC) are known as 3rd generation solar cells. One of the most important parameters affecting the performance of DSSCs is the thin film thickness that forms the photoanode layer. In this study, we examined how 38, 60 and 76 µm thick TiO2 thin films change dye-sensitized solar cell performance. The highest efficiency (4.73%) was seen in the solar cell with 38 µm thin film thickness. In addition, the mineralogical and morphological analyses of the produced TiO2 nanopowders were performed with X-ray diffraction (XRD) and Scanning electron microscopy (SEM). XRD analyses showed that TiO2 was in the anatase crystal phase. SEM photographs confirmed the formation of microspheres in close contact with each other. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Ahmet BARLIK; Gonca ATEŞ Abstract: Scientific evidence showing the harmful effects of ultraviolet radiation on different ocular tissues has led manufacturers to incorporate UV-blocking monomers into contact lenses. In this study, the spectral and optical properties of contact lenses were analyzed in the ultraviolet and visible light wavelength ranges using the Jasco V-730 UV/VIS spectrophotometer device. The results obtained showed that in the lens samples examined, the light transmittance in the wavelength (550nm) range to which the human eye is most sensitive is over 70% and the maximum value is 72.98% in B contact lenses. The largest cutting edge wavelength value was obtained in the A contact lens as 376 nm. At 550 nm, the absorption spectra were found to be below 0.12. In terms of visual quality, visible light transmittance is expected to be high and ultraviolet light transmittance is expected to be minimal. The degree of damage caused by the amount of ultraviolet light absorption increases. Among the contact lenses with and without ultraviolet-protected monomers, lens A did not transmit the UV-B wavelength region, while lens B transmitted UV-A and UV-B wavelengths. This result showed that the protection of lens A was higher. It is seen that the UV transmittance taken with the phocometer is 45% UV in A lens and 91% UV in B lens. The results obtained by UV/VIS spectrophotometer and phocometer supported each other. The results will contribute to the literature by revealing the importance of UV-protected monomer-containing contact lenses in vision equipment, and by enabling the development and selection of full-protection contact lenses. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Ayşe EREN; Şule İNCİ, Kochar Kh. SALEH, Sevda KIRBAĞ, Kemal GÜVEN Abstract: Helichrysum arenarium (L.) Moench subsp. aucheri is a herbaceous perennial herb belonging to the Asteraceae. This plant has biological activities such as antibacterial, antiviral, anti-inflammatory, antifungal, antiproliferative, antioxidant, and antiradical. In this study, antimicrobial and antioxidant activities of methanol and ethanol extracts of aerial parts of H. arenarium subsp. aucheri were investigated. To determine the antimicrobial activity pathogenic microorganisms Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, Staphylococcus aureus, Bacillus megaterium, Candida glabrata, Candida albicans and Trichophyton sp. Antioxidant activity was determined with total antioxidant value (TAS), total oxidant value (TOS) and 2.2-diphenyl-1-picrylhydrazil (DPPH) radical scavenging capacity. In the results obtained, it was determined that the methanol extract had an antimicrobial effect (9.3 mm) only against C. albicans. It was found that the ethanol extract showed antimicrobial activity at different rates (8.8-20.4 mm) against S. aureus, B. megaterium, C. glabrata, C. albicans and Trichophyton sp. The TAS value of the methanol extract was 3.00 mmol, and the TAS value of the ethanol extract was 3.15 mmol. The TOS value of the methanol extract of the same species was calculated as 6.81 μmol, and the TOS value of the ethanol extract was calculated as 12.64 μmol. The DPPH radical scavenging effects of extracts of goldengrass was found to increase depend on concentrations. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Yılmaz AYDIN; Fatih ÖZKAYNAK Abstract: Chaos-based cryptography research is one of the application areas for chaotic systems. Numerous design studies have been put up that take use of the connection between chaos and cryptography. This study has demonstrated how to exploit this relationship to decrypt cryptography designs. It has been looked at if chaos analysis techniques may be used to analyze cryptography protocols. The effectiveness of random number generators has been evaluated using Lyapunov exponents, a chaos analysis technique. The findings of the investigation demonstrated that Lyapunov exponents can be utilized as a standard in assessing random number generators. The paper highlights the issues with the NIST test suite, a popular method of analysis for assessing the statistical characteristics of random number generators. These issues have been seen to not exist with the new test tool that has been suggested. These findings demonstrate that the suggested strategy can be successfully applied in a variety of future applications. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Betül BEKTAŞ EKİCİ; Saltuk Taha USTAOĞLU Abstract: The detection of physical damage in buildings is a critical task in ensuring the safety and integrity of structures. In this study, the effectiveness of deep learning methods for detecting physical damage in buildings, specifically focusing on cracks, defects, moisture, and undamaged classes was investigated. Transfer learning methods, including VGG16, GoogLeNet, and ResNet50, were used to classify a dataset of 7200 images. The dataset was split into training, validation, and testing sets, and the performance of the models was evaluated by using metrics such as accuracy, precision, recall, and F1-score. Results show that all three models achieved high accuracy on the test set, with VGG16 and ResNet50 outperforming GoogLeNet. Additionally, precision, recall, and F1-score metrics indicate strong performance across all classes, with VGG16 and ResNet50 achieving particularly high scores. It is demonstrated the effectiveness of deep learning methods for physical damage detection in buildings and provides insights into the comparative performance of transfer learning methods. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Dönüş ŞENGÜR Abstract: This paper explores the potential usage of ChatGPT-3, a powerful language model developed by OpenAI, within the MATLAB Statistical Toolbox (ST) for social science research. ChatGPT-3 is a highly advanced model that has shown remarkable performance in a wide range of natural language processing tasks. However, its usage in social science research is still relatively new and has not been widely explored. The main advantage of using ChatGPT-3 in social science research is its ability to process large amounts of unstructured text data, which is becoming increasingly prevalent in social science research. However, there are also some potential disadvantages to using ChatGPT-3, such as its complexity, lack of interpretability, and proprietary nature. This paper aims to provide an overview of the current state of ChatGPT-3 usage in social science research and to discuss the potential advantages and disadvantages of using this model within MATLAB ST. This paper, it is aimed to show how ChatGPT-3 can assist social science researchers in MATLAB ST in the processing of their datasets. Because data analysis can be challenging for social science researchers for several reasons as social science data can often be complex, with multiple variables and multiple levels of analysis. This can make it difficult to analyze and interpret the data in a meaningful way. Therefore, some sample hints, where ChatGPT-3 prompts are used to handle such statistical operations in MATLAB ST, are provided. The comments that ChatGPT-3 gives out are analyzed. It is believed that ChatGPT-3 will be a good assistant for social science researchers in MATLAB ST. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Enes KILINÇ Abstract: This study represents Computational Fluid Dynamics (CFD) analyses to improve the heat transfer on the two sides of a thermoelectric generator (TEG) by utilizing heat sinks to recover the waste heat of hot air. In this respect, the temperature difference between the hot and cold sides of the TEG, the heat transfer rate on the hot and cold sides and the pressure drop between the inlet and outlet of the hot and cold air are investigated for varying hot air inlet temperature and Re number in terms of improving the heat transfer and accordingly the output power of the TEG. According to the numerical results, the maximum temperature difference between the hot and cold sides of the TEG concerning hot air inlet temperature of 600 °C and Re number of 16800 is specified as 418.9 °C and 478.1 °C, respectively. In terms of heat transfer, maximum heat transfer rate on the hot side for hot air inlet temperature of 600 °C and Re number of 16800 is specified as 180.4 W and 205.1 W, respectively, while the maximum heat transfer rate on the cold side is specified as 168.0 W and 192.6 W. The maximum pressure drop occurs as 304.4 Pa for the Re number of 16800. As a result, increasing hot air inlet temperature and Re number yields an increase in the temperature difference, the heat transfer rate on the hot side, and the heat transfer rate on the cold side. Besides, pressure drop increases with increasing Re number. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Murat GENÇ; Ömer ÖZBİLEN Abstract: Penalized linear regression methods are used for the accurate prediction of new observations and to obtain interpretable models. The performance of these methods depends on the properties of the true coefficient vector. The LASSO method is a penalized regression method that can simultaneously perform coefficient shrinkage and variable selection in a continuous process. Depending on the structure of the dataset, different estimators have been proposed to overcome the problems faced by LASSO. The estimation method used in the second stage of the post-LASSO two-stage regression method proposed as an alternative to LASSO has a considerable effect on model performance.In this study, the performance of the post-LASSO is compared with classical penalized regression methods ridge, LASSO, elastic net, adaptive LASSO and Post-LASSO by using different estimation methods in the second stage of the post-LASSO. In addition, the effect of the magnitude and position of the signal values in the real coefficient vector on the performance of the models obtained by these methods is analyzed. The mean squared error and standard deviation of the predictions calculated on the test set are used to compare the prediction performance of the models, while the active set sizes are used to compare their performance in variable selection. According to the findings obtained from the simulation studies, the choice of the second-stage estimator and the structure of the true coefficient vector significantly affect the success of the post-LASSO method compared to other methods. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Ayşe CENGİZ; Derya AVCI Abstract: The high resolution of the image is very important for applications. Publicly available satellite images generally have low resolutions. Since low resolution causes loss of information, the desired performance cannot be achieved depending on the type of problem studied in the field of remote sensing. In such a case, super resolution algorithms are used to render low resolution images high resolution. Super resolution algorithms are used to obtain high resolution images from low resolution images. In studies with satellite images, the use of images enhanced with super resolution is important. Since the resolution of satellite images is low, the success rate in the classification process is low. In this study, super resolution method is proposed to increase the classification performance of satellite images. The attributes of satellite images were extracted using AlexNet, ResNet50, Vgg19 from deep learning architecture. Then the extracted features were then classified into 6 classes by giving input to AlexNet-Softmax, ResNet50-Softmax, Vgg19-Softmax, Support Vector Machine, K-Nearest Neighbor, decision trees and Naive Bayes classification algorithms. Without super resolution and with super resolution feature extraction and classification processes were performed separately. Classification results without super resolution and with super resolution were compared. Improvement in classification performance was observed using super resolution. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Oğuzhan KATAR; Dilek ÖZKAN, Gpt -3, Özal YILDIRIM, U Rajendra ACHARYA Abstract: Artificial intelligence (AI) has helped to obtain accurate, fast, robust results without any human errors.Hence, it has been used in various applications in our daily lives. The Turing test has been afundamental problem that AI systems aim to overcome. Recently developed various natural language problem (NLP) models have shown significant performances. AI language models, used intranslation, digital assistant, and sentiment analysis, have improved the quality of our lives. It canperform scans on thousands of documents in seconds and report them by establishing appropriatesentence structures. Generative pre-trained transformer (GPT)-3 is a popular model developedrecently has been used for many applications. Users of this model have obtained surprising results onvarious applications and shared them on various social media platforms. This study aims to evaluatethe performance of the GPT-3 model in writing an academic article. Hence, we chose the subject ofthe article as tools based on artificial intelligence in academic article writing. The organized querieson GPT-3 created the flow of this article. In this article, we have made an effort to highlight theadvantages and limitations of using GPT-3 for research paper writing. Authors feel that it can be usedas an adjunct tool while writing research papers. PubDate: Fri, 01 Sep 2023 00:00:00 +030
Authors:Tuba GÜLŞEN Abstract: This paper offers several approaches for solving multiplicative second-order linear differential equations with variable exponentials, such as normalization and reduction to Riccati equations. In addition, in this paper, the multiplicative version of the Airy equation, which emerges in fluid mechanics, geophysics, and atomic physics, is solved using the multiplicative power series solution method. PubDate: Fri, 01 Sep 2023 00:00:00 +030
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