Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Bhamidipati, Kishore; Yaşar, Ali; Taşpınar, Yavuz Selim; Kursun, Ramazan; Çinar, İ̇lkay; Shankar, Venkatesh Gauri; Köklü, Murat; Ofori, Isaac

doi:10.1155/2022/2062944

Cited by 29 publications

(15 citation statements)

References 47 publications

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“…The nodes interact with each other and share information. Each node receives input and performs some operations on it before transmitting [ 33 ]. These operations are performed by a nonlinear mathematical function called the activation function.…”

Section: Methodsmentioning

confidence: 99%

Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types

Feyzioğlu

Taşpınar

2023

Sensors

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Ensuring safe food supplies has recently become a serious problem all over the world. Controlling the quality, spoilage, and standing time for products with a short shelf life is a quite difficult problem. However, electronic noses can make all these controls possible. In this study, which aims to develop a different approach to the solution of this problem, electronic nose data obtained from 12 different beef cuts were classified. In the dataset, there are four classes (1: excellent, 2: good, 3: acceptable, and 4: spoiled) indicating beef quality. The classifications were performed separately for each cut and all cut shapes. The ANOVA method was used to determine the active features in the dataset with data for 12 features. The same classification processes were carried out by using the three active features selected by the ANOVA method. Three different machine learning methods, Artificial Neural Network, K Nearest Neighbor, and Logistic Regression, which are frequently used in the literature, were used in classifications. In the experimental studies, a classification accuracy of 100% was obtained as a result of the classification performed with ANN using the data obtained by combining all the tables in the dataset.

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Section: Methodsmentioning

confidence: 99%

Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types

Feyzioğlu

Taşpınar

2023

Sensors

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“…It is possible to see the decisions that affect the result in this algorithm. For this reason, it is a popular solution used in threat detection studies [29,35].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Deep Learning based Malware Detection for Android Systems: A Comparative Analysis

2023

Teh. vjesn.

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Nowadays, cyber attackers focus on Android, which is the most popular open-source operating system, as main target by applying some malicious software (malware) to access users' private information, control the device, or harm end-users. To detect Android malware, security experts have offered some learning-based models. In this study, we developed an Android malware detection system that uses different machine\deep learning models by performing both dynamic analyses, in which suspected malware is executed in a safe environment for observing its behaviours, and static analysis, which examines a malware file without any execution on the Android device. The benefits and weaknesses of these models and analyses are described in detail in this comparative study, and directions for future studies are drawn. Experimental results showed that the proposed models gave better results than those in the literature, with 0.988 accuracy for LSTM on static analysis and 0.953 accuracy for CNN-LSTM on dynamic analysis.

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“…For example, it can be used to predict whether a patient has a disease or whether a customer will buy a product. SVM is also effective for high-dimensional datasets and can be used to solve classification and regression problems [20].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Detection of Defects in Rolled Stainless Steel Plates by Machine Learning Models

Feyzioğlu

Taşpınar

2023

International Journal of Applied Mathematics Electronics and Computers

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Iron metal is the most widely used metal type. This metal, which is used in countless sectors, is processed in different ways and turned into steel. Since steel has a brittle structure compared to iron, defects may occur in the plates during the rolling process. Detection of these defects at the production stage is of great importance in terms of commercial and safety. Machine learning methods can be used in such problems for fast and high accuracy detection. For this purpose, using a dataset obtained from stainless steel surface defects in this study, classification processes were carried out to detect defects with four different machine learning methods. Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) algorithms were used for classification processes. The highest classification accuracy was obtained from the 79.44% RF model. Correlation analysis was performed in order to analyze the effects of the features in the dataset on the classification results. It is thought that the classification accuracy of the proposed models is satisfactory for this challenging problem, but needs to be upgraded.

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Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Cited by 29 publications

References 47 publications

Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types

Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types

Deep Learning based Malware Detection for Android Systems: A Comparative Analysis

Detection of Defects in Rolled Stainless Steel Plates by Machine Learning Models

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