Confusion Matrix-Based Supervised Classification Using Microwave SIR-C SAR Satellite Dataset

Sayyad, S. B.; Shaikh, Mudassar; Pandit, Anand; Sonawane, Dattatraya; Anpat, S M

doi:10.1007/978-981-16-0493-5_16

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…The following equations are used to calculate the metrics. The explanation of the variables used in the calculation of these equations is as follows: false positive (FP), true positive (TP), false negative (FN), and true negative (TN) (Diker et al, 2019; Sayyad et al, 2020; Tümen and Ergen, 2020)…”

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Finding complement of inefficient feature clusters obtained by metaheuristic optimization algorithms to detect rock mineral types

Çalışkan

2023

Transactions of the Institute of Measurement and Control

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Minerals are inorganic substances that formed homogeneously by the combination of chemicals that comprise the Earth’s crust and mines are the commercially valuable natural minerals. Since minerals form as a result of chemical interactions in different natural environments, each may have different physical properties. Manually classifying minerals with the naked eye results in distinguishing issues, economic losses, and performance problems. In this study, a deep learning–based hybrid method for distinguishing seven mineral types is proposed. Deep learning models are used to combine the features extracted from residual blocks. The most inefficient features in the feature set were chosen using metaheuristic optimization. The complement rule was then used to combine inefficient features from the feature set into clusters. Consequently, efficient features were obtained, with an overall classification accuracy of 96.14%. The use of the complement rule method in the data set, along with the optimization methods, was shown to improve classification performance. This study is expected to help improve the efficiency and speed of mineral classification.

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Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Finding complement of inefficient feature clusters obtained by metaheuristic optimization algorithms to detect rock mineral types

Çalışkan

2023

Transactions of the Institute of Measurement and Control

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“…Explanation of variables used in equations is expressed as follows; TP for true-positive values, TF for true-negative values, FP for false-positive values, and FN for false-negative values. 34 Se = TP TP + FN , (12)…”

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Detecting attacks on IoT devices with probabilistic Bayesian neural networks and hunger games search optimization approaches

Toğaçar

2021

Trans Emerging Tel Tech

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Technologies that enable physical objects to communicate with each other are attracting a lot of interest day by day. Internet of Things (IoT) is a communication network that is used to connect with technological devices and systems.The growth of IoT networks has led to malicious targeting of these systems by third parties. To prevent such malicious purposes, network security should be at the forefront. Recently, artificial intelligence-based approaches have been used in security domains. Two accessible datasets were used in this study. The datasets consist of simulated bot IoT network traffic data containing distributed denial-of-service (DDoS) attack types. In the proposed approach, probabilistic Bayesian neural networks and normal Bayesian neural networks are used as the deep learning models. All features of the dataset were trained using two models. Then, the datasets were optimized using hunger games search (HGS) method and the most efficient parameters of the datasets were selected and retrained using deep learning models. In the analysis performed using the proposed approach, an overall accuracy of 100% was obtained for the dataset used in the first experiment and 99.99% for the dataset used in the second experiment. It was also observed that metaheuristic optimization helps the proposed approach to save time in detecting DDoS attacks.

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“…Metrik değerlerinin hesaplanmasında Denklem 3 ile Denklem 7 arasındaki formüller kullanıldı. Bu denklemlerde kullanılan değişkenler; doğru pozitif (DP), doğru negatif (DN), yanlış pozitif (YP), yanlış negatif (YN) anlamına gelmektedir [21][22][23][24]. [25], yarı denetimli kendinden etiketli algoritma yöntemini kullanarak cinsiyet tespit başarısını %98.42'ye yükseltmiştir.…”

Section: Bulgularunclassified

Gender Determination in Human Voice Signals using Synaptic Efficacy Function-based Leaky Integrate and Fire Neuron Model

Tümen

2022

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Günümüzdeki teknolojik gelişmeler, insanların bir ses sinyalinden konuşmacının cinsiyetini belirlemesi mümkün kılmıştır. Frekans türleri, spektral ve entropi gibi sayısal nitelikli veriler ses sinyallerinin akustik bilgilerini oluşturmaktadır. Son zamanlarda, yüksek başarı oranlarına sahip yapay zekâ tabanlı öğrenme modelleri çeşitli alanlarda ilgi görmeye başladı. Ses sinyalleri üzerinde derin öğrenme modelleri ile ilgili birçok çalışma bulunmaktadır. Bu çalışmada, derin öğrenme modellerinden esinlenerek tasarlanmış ve farklı bir mimari yapısı olan ani sivri uçlu sinir ağları kullanılmıştır. Çalışmada kullanılan veri kümesi, insan konuşmalarını ve seslerini içeren akustik bilgiye dayalı parametrelerden oluşmaktadır. Belirlenen veri seti kullanılarak ani sivri uçlu sinir ağı modeli eğitilmiş ve cinsiyet tespitinin gerçekleştirilmesi sağlanmıştır. Önermiş olduğumuz bu çalışmada sonuç olarak, sınıflandırma sürecinde %98,84 genel doğruluk başarısı elde edilmiştir. Bu çalışmada gerçekleştirilen deneysel analizler ile ani sivri uçlu sinir ağı modelinin başarılı bir şekilde çalıştırıldığı, yüksek başarımlar elde edildiği gözlemlenmiştir.

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Confusion Matrix-Based Supervised Classification Using Microwave SIR-C SAR Satellite Dataset

Cited by 8 publications

References 8 publications

Finding complement of inefficient feature clusters obtained by metaheuristic optimization algorithms to detect rock mineral types

Finding complement of inefficient feature clusters obtained by metaheuristic optimization algorithms to detect rock mineral types

Detecting attacks on IoT devices with probabilistic Bayesian neural networks and hunger games search optimization approaches

Gender Determination in Human Voice Signals using Synaptic Efficacy Function-based Leaky Integrate and Fire Neuron Model

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