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Full-Text Articles in Engineering
Recognition Via Morphological Features Of Tulip By The Algorithms For Calculating Estimates, Mirzayan Kamilov, Mirzaakbar Hudayberdiev
Recognition Via Morphological Features Of Tulip By The Algorithms For Calculating Estimates, Mirzayan Kamilov, Mirzaakbar Hudayberdiev
Chemical Technology, Control and Management
The paper discusses two problem, the firth, possibilities of improving the quality of the recognition algorithm based on partial precedent, by the original pre-training procedures. And second finding the optimal procedure for constructing improved results in some sense, the algorithms for calculating estimates. The peculiarity of this algorithm is that as precedents only such "anchor points" of a pattern that ensuring the following conditions are left: the distance from any point on the training set of i-th pattern to their nearest precedent is less than the distance to the nearest precedent of another pattern. This set of precedents provides unmistakable …
A Study Of Security Problems In Big Data And Their Solutions, Nozima Akhmedova
A Study Of Security Problems In Big Data And Their Solutions, Nozima Akhmedova
Chemical Technology, Control and Management
Statistical data on information security that concerns Big Data and is the most important for enterprises are provided. Based on this data, we studied problems such as the lack of big data practices and protection, the lack of techniques for protecting big data, the lack of standards for protecting big data, the lack of regulation of big data and ecosystems, security problems in Big Data, and proposed several proposals to improve the security of systems that use this technology.
Importance Of Morphological Features In Orthoptera Identification, Kamilov Mirzoyan, Alisher Khamroev, Hudayberdiev Mirzaakbar
Importance Of Morphological Features In Orthoptera Identification, Kamilov Mirzoyan, Alisher Khamroev, Hudayberdiev Mirzaakbar
Chemical Technology, Control and Management
This article analyzes the problems of implementing image recognition methods and algorithms for identifying biological objects. The Orthoptera group was chosen as a biological object. Orthoptera is a taxonomic order of insects that includes grasshoppers, crickets, locusts, and others. Approaches to the formation and identification of features of Orthoptera species and the formation of training and control samples of their samples are proposed. Estimation algorithms (ACE) were chosen as algorithmic support for identifying Orthoptera collections. ACE is based on the principle of partial priority. Approaches to the formation of a training and testing complex based on the data from the …
A Novel Genome Analysis Method With The Entropy-Based Numerical Techniqueusing Pretrained Convolutional Neural Networks, Bi̇hter Daş, Suat Toraman, İbrahi̇m Türkoğlu
A Novel Genome Analysis Method With The Entropy-Based Numerical Techniqueusing Pretrained Convolutional Neural Networks, Bi̇hter Daş, Suat Toraman, İbrahi̇m Türkoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
The identification of DNA sequences as exon and intron is a common problem in genome analysis. The methods used for feature extraction and mapping techniques for the digitization of sequences affect directly the solution of this problem. The existing mapping techniques are not enough to detect coding and noncoding regions in some genomes because the digital representation of each base in a DNA sequence with an integer does not fully reflect the structure of an original DNA sequence. In the entropy-based mapping technique, we could overcome this problem because the technique deepens distinction rates of exon regions, and better reflects …
Detection Of Hand Osteoarthritis From Hand Radiographs Using Convolutionalneural Networks With Transfer Learning, Kemal Üreten, Hasan Erbay, Hadi̇ Hakan Maraş
Detection Of Hand Osteoarthritis From Hand Radiographs Using Convolutionalneural Networks With Transfer Learning, Kemal Üreten, Hasan Erbay, Hadi̇ Hakan Maraş
Turkish Journal of Electrical Engineering and Computer Sciences
Osteoarthritis is the most common type of arthritis. Hand osteoarthritis leads to specific structural changes in the joints, such as asymmetric joint space narrowing and osteophytes (bone spurs). Conventional radiography has traditionally been the primary method of visualizing these structural changes and diagnosing osteoarthritis. We aimed to develop a computerized method that is capable of determining the structural changes seen in radiography of the hand and to assist practitioners in interpreting radiographic changes and diagnosing the disease. In this retrospective study, transfer-learning-based convolutional neural networks were trained on a randomly selected dataset containing 332 radiography images of hands from an …
Dynamically Updated Diversified Ensemble-Based Approach For Handling Concept Drift, Kanu Goel, Shalini Batra
Dynamically Updated Diversified Ensemble-Based Approach For Handling Concept Drift, Kanu Goel, Shalini Batra
Turkish Journal of Electrical Engineering and Computer Sciences
Concept drift is the phenomenon where underlying data distribution changes over time unexpectedly. Examining such drifts and getting insight into the executing processes at that instance of time is a big challenge. Prediction models should be capable of handling drifts in scenarios where statistical properties show abrupt changes. Various strategies exist in the literature to deal with such challenging scenarios but the majority of them are limited to the identification of a particular kind of drift pattern. The proposed approach uses online drift detection in a diversified adaptive setting with pruning techniques to formulate a concept drift handling approach, named …
Sketic: A Machine Learning-Based Digital Circuit Recognition Platform, Mohamamd Abdel Majeed, Tasneem Almousa, Maysaa Alsalman, Abeer Yosef
Sketic: A Machine Learning-Based Digital Circuit Recognition Platform, Mohamamd Abdel Majeed, Tasneem Almousa, Maysaa Alsalman, Abeer Yosef
Turkish Journal of Electrical Engineering and Computer Sciences
In digital system design, digital logic circuit diagrams are built using interconnects and symbolic representations of the basic logic gates. Constructing such diagrams using free sketches is the first step in the design process. After that the circuit schematic or code has to be generated before being able to simulate the design. While most of the mentioned steps are automated using design automation tools, drafting the schematic circuit and then converting it into a valid format that can be simulated are still done manually due to the lack of robust tools that can recognize the free sketches and incorporate them …
A Random Subspace Based Conic Functions Ensemble Classifier, Emre Çi̇men
A Random Subspace Based Conic Functions Ensemble Classifier, Emre Çi̇men
Turkish Journal of Electrical Engineering and Computer Sciences
Classifiers overfit when the data dimensionality ratio to the number of samples is high in a dataset. This problem makes a classification model unreliable. When the overfitting problem occurs, one can achieve high accuracy in the training; however, test accuracy occurs significantly less than training accuracy. The random subspace method is a practical approach to overcome the overfitting problem. In random subspace methods, the classification algorithm selects a random subset of the features and trains a classifier function trained with the selected features. The classification algorithm repeats the process multiple times, and eventually obtains an ensemble of classifier functions. Conic …
Revised Polyhedral Conic Functions Algorithm For Supervised Classification, Gürhan Ceylan, Gürkan Öztürk
Revised Polyhedral Conic Functions Algorithm For Supervised Classification, Gürhan Ceylan, Gürkan Öztürk
Turkish Journal of Electrical Engineering and Computer Sciences
In supervised classification, obtaining nonlinear separating functions from an algorithm is crucial for prediction accuracy. This paper analyzes the polyhedral conic functions (PCF) algorithm that generates nonlinear separating functions by only solving simple subproblems. Then, a revised version of the algorithm is developed that achieves better generalization and fast training while maintaining the simplicity and high prediction accuracy of the original PCF algorithm. This is accomplished by making the following modifications to the subproblem: extension of the objective function with a regularization term, relaxation of a hard constraint set and introduction of a new error term. Experimental results show that …
Fast Texture Classification Of Denoised Sar Image Patches Using Glcm On Spark, Caner Özcan, Kadri̇ Okan Ersoy, İskender Ülgen Oğul
Fast Texture Classification Of Denoised Sar Image Patches Using Glcm On Spark, Caner Özcan, Kadri̇ Okan Ersoy, İskender Ülgen Oğul
Turkish Journal of Electrical Engineering and Computer Sciences
Classification of a synthetic aperture radar (SAR) image is an essential process for SAR image analysis and interpretation. Recent advances in imaging technologies have allowed data sizes to grow, and a large number of applications in many areas have been generated. However, analysis of high-resolution SAR images, such as classification, is a time-consuming process and high-speed algorithms are needed. In this study, classification of high-speed denoised SAR image patches by using Apache Spark clustering framework is presented. Spark is preferred due to its powerful open-source cluster-computing framework with fast, easy-to-use, and in-memory analytics. Classification of SAR images is realized on …