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  Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering
Number of Followers: 0  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1303-6009 - ISSN (Online) 2618-6462
Published by Ankara University Homepage  [5 journals]
  • Classification of human activities by smart device measurements

    • Authors: Mürüvvet KALKAN; Yilmaz AR
      Abstract: The prevalence of activity detectors in users’ personal mobile devices has been incorporated into an increasing interest in research into physical function recognition (HAR - Human Activity Recognition). With this research interest, different enterprises developed HAR systems working with measurement devices and still work on this subject. Although many HAR systems have been developed, there are still concrete practical limits. This situation is improved with modern techniques such as machine learning. A properly trained machine learning model predicts human activity from measured data. The data was measured at certain time intervals by sensors on smartphones. These different machine learning architectures were trained on sensor data that detected human activities, and their accuracy was calculated. A HAR system that predicts human activity is constructed separately with five approaches. KNN, Random Forest, Decision Tree, MLP and Gaussian Naive Bayes algorithms were used, and KNN produced the most accurate results.
      PubDate: Fri, 29 Dec 2023 00:00:00 +030
       
  • An optimized artificial neural network for estimating design effort of
           jigs and fixtures used in aviation industry

    • Authors: Umut Nazmi AKTAN; Mehmet DİKMEN
      Abstract: This paper investigates the usefulness of the machine learning methods to predict the design effort of jigs and fixtures used in the aviation industry. Reaching the best possible result by determining the ideal machine learning model to obtain the best estimate and the most appropriate set of inputs and parameters forms the basis of this study. To that end, most popular machine learning models that can be used for regression are combined with various data encoding methods. The best combination is optimized as well. The results showed that an optimized Artificial Neural Network architecture with binary encoding applied to the input data can be applied satisfactorily in the aviation industry for the solution of the given problem.
      PubDate: Fri, 29 Dec 2023 00:00:00 +030
       
  • Disease detection in bean leaves using deep learning

    • Authors: Soydan SERTTAŞ; Emine DENİZ
      Abstract: The care and health of agricultural plants, which are the primary source for people to eat healthily, are essential. Disease detection in plants is one of the critical elements of smart agriculture. In parallel with the development of artificial intelligence, advancements in smart agriculture are also progressing. The development of deep learning techniques positively affects smart farming practices. Today, using deep learning and computer vision techniques, various plant diseases can be detected from images such as photographs. In this research, deep learning techniques were used to detect and diagnose bean leaf diseases. Healthy and diseased bean leaf images were used to train the convolutional neural network (CNN) model, which is one of the deep learning techniques. Transfer learning was applied to CNN models to detect plant diseases with the difference of related works. A transfer learning-based strategy to identify various diseases in plant varieties is demonstrated using leaf images of healthy and diseased plants from the Bean dataset. With the proposed method, 1295 images were studied. The results show that our technique successfully identified disease status in bean leaf images, achieving an accuracy of 98.33% with the ResNet50 model.
      PubDate: Fri, 29 Dec 2023 00:00:00 +030
       
  • Comparative analysis of mature tomato detection by feature extraction and
           machine learning for autonomous greenhouse robots

    • Authors: Hakki Alparslan ILGIN; Fevzi Anıl AYDEMİR, Berkay CEDİMOĞLU, Muhammet Nurullah AYDIN, Turkey-hasan SİLLELİ
      Abstract: Accurate detection of tomatoes grown in greenhouses is important for timely harvesting. In this way, it is ensured that mature tomatoes are collected by distinguishing them from the unripe ones. Insufficient light, occlusion, and overlapping adversely affect the detection of mature tomatoes. In addition, it is time consuming for people to detect mature tomatoes at certain periods in large greenhouses. For these reasons, high-performance automatic detection of tomatoes by greenhouse robots has become an increasingly studied area today. In this paper, two feature extraction methods, histogram of oriented gradients (HOG) and local binary patterns (LBP), which are effective in object recognition, and two important and commonly used classifiers of machine learning, support vector machines (SVM) and k-nearest neighbor (kNN), are comparatively used to detect and count tomatoes. The HOG and LBP features are classified separately and together by SVM or kNN, and the success of each case are compared. Performance of the detection is improved by eliminating false positive results at the postprocessing stage using color information.
      PubDate: Fri, 29 Dec 2023 00:00:00 +030
       
  • A novel alternative in wireless and passive sensing: the bended nested
           split-ring resonator

    • Authors: Burak ÖZBEY
      Abstract: In this paper, a new split-ring resonator variant, called the bended nested split-ring resonator (B-NSRR) is introduced. B-NSRR is a modified version of the nested split-ring resonator (NSRR) geometry, which has been successfully utilized in sensing of various physical quantities such as strain, displacement and moisture content due to its superior sensitivity, resolution and compactness in comparison to more traditional structures such as SRR and electrical SRR (ESRR). The B-NSRR geometry is demonstrated to allow an even more compact structure, while retaining the high sensitivity level of the NSRR. The performances obtained by the SRR, ESRR, NSRR and B-NSRR geometries are compared for displacement and moisture content sensing applications. Simulations are carried out to validate the findings, where modified versions of SRR-based structures are employed as displacement sensors and a comparison is made between their performances. Owing to its compactness and high sensitivity, it is shown that the B-NSRR is a reasonable alternative to available geometries in various sensing applications.
      PubDate: Fri, 29 Dec 2023 00:00:00 +030
       
  • On the geometric phases in entangled states

    • Authors: Melik Emirhan TUNALIOĞLU; Hasan Özgür ÇILDIROĞLU, Ali Ulvi YILMAZER
      Abstract: Correlation relations for the spin measurements on a pair of entangled particles scattered by the two separate arms of interferometers in hybrid setups of different types are investigated. Concurrence, entanglement of formation, quantum fidelity, Bures distance are used to clarify how the geometric phase affects the initial bipartite state. This affect causes a quantum interference due to the movement of charged particles in regions where electromagnetic fields are not present. We shown that in some cases the geometric phase information is carried over to the final bipartite entangled state.
      PubDate: Fri, 29 Dec 2023 00:00:00 +030
       
  • ML based prediction of COVID-19 diagnosis using statistical tests

    • Authors: Şifa ÖZSARI; Fatma Zehra ORTAK, Mehmet Serdar GÜZEL, Mükerrem Bahar BAŞKIR, Gazi Erkan BOSTANCI
      Abstract: The first case of the novel Coronavirus disease (COVID-19), which is a respiratory disease, was seen in Wuhan city of China, in December 2019. From there, it spread to many countries and significantly affected human life. Deep learning, which is a very popular method today, is also widely used in the field of healthcare. In this study, it was aimed to determine the most suitable Deep Learning (DL) model for diagnosis of COVID-19. A popular public data set, which consists of 2482 scans was employed to select the best DL model. The success of the models was evaluated by using different performance evaluation metrics such as accuracy, sensitivity, specificity, precision, F1 score, kappa and AUC. According to the experimental results, it has been observed that DenseNet models, AdaGrad and NADAM optimizers are effective and successful. Also, whether there are statistically significant differences in each performance measure/score of the architectures by the optimizers was observed with statistical tests.
      PubDate: Fri, 29 Dec 2023 00:00:00 +030
       
 
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  Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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