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Articles 1 - 15 of 15
Full-Text Articles in Engineering
Classification Of Oil Products According To The Nomenclature Of Goods, Kurbankul Mavlankulovych Karimkulov Profesor, Azoda Abdurahmanova
Classification Of Oil Products According To The Nomenclature Of Goods, Kurbankul Mavlankulovych Karimkulov Profesor, Azoda Abdurahmanova
Technical science and innovation
The article analyzes types of vegetable oils. Simple methods of determining their quality using liquid gas chromatography have been developed. Recommendations for improving the classification of foreign economic activity based on commodity nomenclature were developed and recommended for customs operations. “Fats and oils of animal, vegetable or microbiological origin and products of their breakdown; prepared edible fats; waxes of animal or vegetable origin” was called. Animal fats, including pork, beef, sheep, goat, fish and oils of animals such as marine mammals are classified according to the Nomenclature of Foreign Economic Activities of the Republic of Uzbekistan in commodity headings 1501-1506 …
Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni
Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni
Turkish Journal of Electrical Engineering and Computer Sciences
Deep convolutional neural networks can fully use the intrinsic relationship between features and improve the separability of hyperspectral images, which has received extensive in recent years. However, the need for a large number of labelled samples to train deep network models limits the application of such methods. The idea of transfer learning is introduced into remote sensing image classification to reduce the need for the number of labelled samples. In particular, the situation in which each class in the target picture only has one labelled sample is investigated. In the target domain, the number of training samples is enlarged by …
Deep Feature Extraction, Dimensionality Reduction, And Classification Of Medical Images Using Combined Deep Learning Architectures, Autoencoder, And Multiple Machine Learning Models, Ahmet Hi̇dayet Ki̇raz, Fatime Oumar Djibrillah, Mehmet Emi̇n Yüksel
Deep Feature Extraction, Dimensionality Reduction, And Classification Of Medical Images Using Combined Deep Learning Architectures, Autoencoder, And Multiple Machine Learning Models, Ahmet Hi̇dayet Ki̇raz, Fatime Oumar Djibrillah, Mehmet Emi̇n Yüksel
Turkish Journal of Electrical Engineering and Computer Sciences
Accurate analysis and classification of medical images are essential factors in clinical decision-making and patient care. A novel comparative approach for medical image classification is proposed in this study. This new approach involves several steps: deep feature extraction, which extracts the informative features from medical images; concatenation, which concatenates the extracted deep features to form a robust feature vector; dimensionality reduction with autoencoder, which reduces the dimensionality of the feature vector by transforming it into a different feature space with a lower dimension; and finally, these features obtained from all these steps were fed into multiple machine learning classifiers (SVM, …
Classification Of Chronic Pain Using Fmri Data: Unveiling Brain Activity Patterns For Diagnosis, Rejula V, Anitha J, Belfin Robinson
Classification Of Chronic Pain Using Fmri Data: Unveiling Brain Activity Patterns For Diagnosis, Rejula V, Anitha J, Belfin Robinson
Turkish Journal of Electrical Engineering and Computer Sciences
Millions of people throughout the world suffer from the complicated and crippling condition of chronic pain. It can be brought on by several underlying disorders or injuries and is defined by chronic pain that lasts for a period exceeding three months. To better understand the brain processes behind pain and create prediction models for pain-related outcomes, machine learning is a potent technology that may be applied in Functional magnetic resonance imaging (fMRI) chronic pain research. Data (fMRI and T1-weighted images) from 76 participants has been included (30 chronic pain and 46 healthy controls). The raw data were preprocessed using fMRIprep …
Stepwise Dynamic Nearest Neighbor (Sdnn): A New Algorithm For Classification, Deni̇z Karabaş, Derya Bi̇rant, Peli̇n Yildirim Taşer
Stepwise Dynamic Nearest Neighbor (Sdnn): A New Algorithm For Classification, Deni̇z Karabaş, Derya Bi̇rant, Peli̇n Yildirim Taşer
Turkish Journal of Electrical Engineering and Computer Sciences
Although the standard k-nearest neighbor (KNN) algorithm has been used widely for classification in many different fields, it suffers from various limitations that abate its classification ability, such as being influenced by the distribution of instances, ignoring distances between the test instance and its neighbors during classification, and building a single/weak learner. This paper proposes a novel algorithm, called stepwise dynamic nearest neighbor (SDNN), which can effectively handle these problems. Instead of using a fixed parameter k like KNN, it uses a dynamic neighborhood strategy according to the data distribution and implements a new voting mechanism, called stepwise voting. Experimental …
A Machine Learning Approach For Dyslexia Detection Using Turkish Audio Records, Tuğberk Taş, Muhammed Abdullah Bülbül, Abas Haşi̇moğlu, Yavuz Meral, Yasi̇n Çalişkan, Gunay Budagova, Mücahi̇d Kutlu
A Machine Learning Approach For Dyslexia Detection Using Turkish Audio Records, Tuğberk Taş, Muhammed Abdullah Bülbül, Abas Haşi̇moğlu, Yavuz Meral, Yasi̇n Çalişkan, Gunay Budagova, Mücahi̇d Kutlu
Turkish Journal of Electrical Engineering and Computer Sciences
Dyslexia is a learning disorder, characterized by impairment in the ability to read, spell, and decode letters. It is vital to detect dyslexia in earlier stages to reduce its effects. However, diagnosing dyslexia is a time-consuming and costly process. In this paper, we propose a machine-learning model that predicts whether a Turkish-speaking child has dyslexia using his/her audio records. Therefore, our model can be easily used by smart phones and work as a warning system such that children who are likely to be dyslexic according to our model can seek an examination by experts. In order to train and evaluate, …
Compatibility Of Clique Clustering Algorithm With Dimensionality Reduction, Ug ̆Ur Madran, Duygu Soyog ̆Lu
Compatibility Of Clique Clustering Algorithm With Dimensionality Reduction, Ug ̆Ur Madran, Duygu Soyog ̆Lu
Applied Mathematics & Information Sciences
In our previous work, we introduced a clustering algorithm based on clique formation. Cliques, the obtained clusters, are constructed by choosing the most dense complete subgraphs by using similarity values between instances. The clique algorithm successfully reduces the number of instances in a data set without substantially changing the accuracy rate. In this current work, we focused on reducing the number of features. For this purpose, the effect of the clique clustering algorithm on dimensionality reduction has been analyzed. We propose a novel algorithm for support vector machine classification by combining these two techniques and applying different strategies by differentiating …
The Morphology Analysis Of Soil In Remote Sensing Image Processing, Mirzayan Mirzaaxmedovich Kamilov, Mirzaakbar Xakkulmirzayevich Hudayberdiev, Bobomurod Mamitjonovich Tojiboev
The Morphology Analysis Of Soil In Remote Sensing Image Processing, Mirzayan Mirzaaxmedovich Kamilov, Mirzaakbar Xakkulmirzayevich Hudayberdiev, Bobomurod Mamitjonovich Tojiboev
Chemical Technology, Control and Management
This article analyzed various techniques used in satellite image processing to analyze soil morphology. Analysis of soil morphology using satellite imagery plays a crucial role in soil science research, Land Management, and environmental monitoring. It provides an economical and efficient means of studying large-scale soil variability, providing information on land sustainable use, resource management and soil conservation decisions.
Risk Assessment Approaches In Banking Sector –A Survey, Mona Sharaf, Shimaa Mohamed Ouf, Amira M. Idrees Ami
Risk Assessment Approaches In Banking Sector –A Survey, Mona Sharaf, Shimaa Mohamed Ouf, Amira M. Idrees Ami
Future Computing and Informatics Journal
Prediction analysis is a method that makes predictions based on the data currently available. Bank loans come with a lot of risks to both the bank and the borrowers. One of the most exciting and important areas of research is data mining, which aims to extract information from vast amounts of accumulated data sets. The loan process is one of the key processes for the banking industry, and this paper examines various prior studies that used data mining techniques to extract all served entities and attributes necessary for analytical purposes, categorize these attributes, and forecast the future of their business …
A Practical Framework For Early Detection Of Diabetes Using Ensemble Machine Learning Models, Qusay Saihood, Emrullah Sonuç
A Practical Framework For Early Detection Of Diabetes Using Ensemble Machine Learning Models, Qusay Saihood, Emrullah Sonuç
Turkish Journal of Electrical Engineering and Computer Sciences
The diagnosis of diabetes, a prevalent global health condition, is crucial for preventing severe complications. In recent years, there has been a growing effort to develop intelligent diagnostic systems for diabetes utilizing machine learning (ML) algorithms. Despite these efforts, achieving high accuracy rates using such systems remains a significant challenge. Recent advancements in ensemble ML methods offer promising opportunities for early detection of diabetes, as they are known to be faster and more cost-effective than traditional approaches. Therefore, this study proposes a practical framework for diagnosing diabetes that involves three stages. The data preprocessing stage encompasses several crucial tasks, including …
User Classification Based On Mouse Dynamic Authentication Using K-Nearest Neighbor, Didih Rizki Chandranegara, Anzilludin Ashari, Zamah Sari, Hardianto Wibowo, Wildan Suharso
User Classification Based On Mouse Dynamic Authentication Using K-Nearest Neighbor, Didih Rizki Chandranegara, Anzilludin Ashari, Zamah Sari, Hardianto Wibowo, Wildan Suharso
Makara Journal of Technology
Mouse dynamics authentication is a method for identifying a person by analyzing the unique pattern or rhythm of their mouse movement. Owing to its distinctive properties, such mouse movements can be used as the basis for security. The development of technology is followed by the urge to keep private data safe from hackers. Therefore, increasing the accuracy of user classification and reducing the false acceptance rate (FAR) are necessary to improve data security. In this study, we propose to combine the K-nearest neighbor method and simple random sampling and obtain a sample from a dataset to improve the classification of …
A Novel Insect And Pest Identification Model Based On A Weighted Multipath Convolutional Neural Network And Generative Adversarial Network, Vinita Abhishek Gupta, M.V. Padmavati, Ravi R. Saxena, Raunak Kumar Tamrakar
A Novel Insect And Pest Identification Model Based On A Weighted Multipath Convolutional Neural Network And Generative Adversarial Network, Vinita Abhishek Gupta, M.V. Padmavati, Ravi R. Saxena, Raunak Kumar Tamrakar
Karbala International Journal of Modern Science
Timely identification of insects and their management play a significant role in sustainable agriculture development. The proposed hybrid model integrates a weighted multipath convolutional neural network and generative adversarial network to identify insects efficiently. To address the shortcomings of single-path networks, this novel model takes input from numerous iterations of the same image to learn more specific features. To avoid redundancy produced due to multipath, weights have been assigned to each path. For Xie2 dataset, the model shows 3.75%, 2.74%, 1.54%, 1.76%, 1.76%, 2.74 %, and 2.14% performance improvement from AlexNet, ResNet50, ResNet101, GoogleNet, VGG-16, VGG-19, and simple CNN respectively. …
Early Diagnosis Of Pancreatic Cancer By Machine Learning Methods Using Urine Biomarker Combinations, İrem Acer, Firat Orhan Bulucu, Semra İçer, Fatma Lati̇foğlu
Early Diagnosis Of Pancreatic Cancer By Machine Learning Methods Using Urine Biomarker Combinations, İrem Acer, Firat Orhan Bulucu, Semra İçer, Fatma Lati̇foğlu
Turkish Journal of Electrical Engineering and Computer Sciences
The most common type of pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC), which accounts for the vast majority of pancreatic cancers. The five-year survival rate for PDAC due to late diagnosis is 9%. Early diagnosed PDAC patients survive longer than patients diagnosed at a more advanced stage. Biomarkers can play an essential role in the early detection of PDAC to assist the health professional. Machine learning and deep learning methods are used with biomarkers obtained in recent studies for diagnostic purposes. In order to increase the survival rates of PDAC patients, early diagnosis of the disease with a noninvasive test …
Deep Learning-Based Classification Of Chaotic Systems Over Phase Portraits, Sezgi̇n Kaçar, Süleyman Uzun, Burak Aricioğlu
Deep Learning-Based Classification Of Chaotic Systems Over Phase Portraits, Sezgi̇n Kaçar, Süleyman Uzun, Burak Aricioğlu
Turkish Journal of Electrical Engineering and Computer Sciences
This study performed a deep learning-based classification of chaotic systems over their phase portraits. To the best of the authors' knowledge, such classification studies over phase portraits have not been conducted in the literature. To that end, a dataset consisting of the phase portraits of the most known two chaotic systems, namely Lorenz and Chen, is generated for different values of the parameters, initial conditions, step size, and time length. Then, a classification with high accuracy is carried out employing transfer learning methods. The transfer learning methods used in the study are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet101, DenseNet201, ShuffleNet, and …
Early Liver Disease Diagnosis Using Grey Wolf Optimization Algorithm, Abrar Rizk M. Khedr, Ahmed I. Saleh, Ahmed S. Samra, Eman Abdelhalim
Early Liver Disease Diagnosis Using Grey Wolf Optimization Algorithm, Abrar Rizk M. Khedr, Ahmed I. Saleh, Ahmed S. Samra, Eman Abdelhalim
Mansoura Engineering Journal
Every year, millions of people around the world experience health issues due to liver disease, and It is also being a major global cause of mortality. A variety of factors, such as obesity and hepatitis infection, can harm the liver and contribute to these disorders. However, diagnosis of chronic liver disease is often an expensive and complex process, and early detection of liver disease poses challenges because of its elusive symptoms that can often lead to delayed diagnosis. We employed machine learning for this study, to anticipate individuals with liver diseases before symptoms appear. To attain the best accuracy, we …