An optimum ANN-based breast cancer diagnosis: Bridging gaps between ANN learning and decision-making goals

Jafari-Marandi, Ruholla; Davarzani, Samaneh; Gharibdousti, Maryam Soltanpour; Smith, Brian

doi:10.1016/j.asoc.2018.07.060

Cited by 76 publications

(15 citation statements)

References 45 publications

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“…In each epoch of training, each member of the train set was exposed to the network and the weights on the neuron connections changed such that the expectation of the network moves closer to the actual target (back-propagation). After the network has undergone enough epochs of training such that there is not a significant difference between the expectation of the network and the actual target values, the network is said to have been trained [54]. An epoch size of 1000 was used and the program iterated until convergence was achieved.…”

Section: B Artificial Neural Network Model Developmentmentioning

confidence: 99%

Path Loss Predictions in the VHF and UHF Bands Within Urban Environments: Experimental Investigation of Empirical, Heuristics and Geospatial Models

et al. 2019

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Deep knowledge of how radio waves behave in a practical wireless channel is required for effective planning and deployment of radio access networks in urban environments. Empirical propagation models are popular for their simplicity, but they are prone to introduce high prediction errors. Different heuristic methods and geospatial approaches have been developed to further reduce path loss prediction error. However, the efficacy of these new techniques in built-up areas should be experimentally verified. In this paper, the efficiencies of empirical, heuristic, and geospatial methods for signal fading predictions in the very high frequency (VHF) and ultra-high frequency (UHF) bands in typical urban environments are evaluated and analyzed. Electromagnetic field strength measurements are performed at different test locations within four selected cities in Nigeria. The data collected are used to develop path loss models based on artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and Kriging techniques. The prediction results of the developed models are compared with those of selected empirical models and field measured data. Apart from Egli and ECC-33, the root mean squared error (RMSE) produced by all other models under investigation are considered acceptable. Specifically, the ANN and ANFIS models yielded the lowest prediction errors. However, the empirical models have the lowest standard deviation errors across all the bands. The findings of this study will help radio network engineers to achieve efficient radio coverage estimation; determine the optimal base station location; make a proper frequency allocation; select the most suitable antenna; and perform interference feasibility studies.INDEX TERMS ANFIS, artificial neural networks, backpropagation, path loss, Kriging, radio propagation. I. INTRODUCTIONA study of the characteristics of radio waves in different propagation environments is needed for an effective network planning, and for the deployment of wireless communication systems [1], [2]. The magnitude and direction of electromagnetic waves in a practical wireless channel is usuallyThe associate editor coordinating the review of this manuscript and approving it for publication was Mauro Tucci.random and highly unpredictable [3]. Meanwhile, a good understanding of this phenomenon is needed to guarantee good Quality of Service (QoS) and high data transmission rate in radio access networks.The efficiency of a wireless communication system depends on the physical constituents of the propagation environment. The presence of buildings, mountains, bill boards, foliage, vehicles and other physical objects in a practical propagation environment usually obstructs the direct

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Section: B Artificial Neural Network Model Developmentmentioning

confidence: 99%

Path Loss Predictions in the VHF and UHF Bands Within Urban Environments: Experimental Investigation of Empirical, Heuristics and Geospatial Models

et al. 2019

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“…Beberapa metode machine learning yang banyak dipilih untuk menyelesaikan kasus prediksi kanker payudara ini terutama algoritma klasifikasi diantaranya Artificial Neural Network (Jafari-Marandi, Davarzani, Soltanpour Gharibdousti, & Smith, 2018), Deep learning (Levine et al, 2019), Neural Network (Ellmann et al, 2019), dan Support Vector Machine (Bustamam et al, 2019;Singh, 2019;Tapak et al, 2018) bahkan Fuzzy Logic (Nilashi, Ibrahim, Ahmadi, & Shahmoradi, 2017).…”

Section: Forward Selection Pada Support Vector Machine Untuk Memprediksi Kanker Payudara Hani Harafaniunclassified

Forward Selection pada Support Vector Machine untuk Memprediksi Kanker Payudara

Harafani¹

2020

Infortech

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Kanker payudara merupakan masalah kesehatan yang serius, sehingga deteksi dini dari kanker payudara dapat berperan penting dalam perencanaan pengobatan. Pada penelitian ini Support Vector Machine dengan kernel (dot, polynomial, RBF) dan forward selection diterapkan. Perbandingan akurasi SVM tanpa forward selection dengan menggunakan forward selection menunjukkan selisih yang besar. Hasil penelitian menunjukkan SVM(RBF)+FS unggul dengan akurasi 85,38% dibandingkan dengan SVM(Polynomial & dot), selain itu SVM(RBF)+FS juga unggul dibandingkan algoritma machine learning lainnya dalam memprediksi dataset kanker payudara Coimbra.

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“…Artificial Neural Network (ANN) [28] is an algorithm developed from the motivation of the human brain works. Typically, the ANN architecture composes of three parts including the input layer, hidden layer, and an output layer.…”

Section: B the State-of-the-art Machine Learningmentioning

confidence: 99%

A Comparative Study of Learning Techniques with Convolutional Neural Network Based on HPC-Workload Dataset

Banjongkan¹,

Pongsena²,

Kerdprasop³

et al. 2020

IJMLC

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High-Performance Computing or HPC is a computer system that has high computing power. The HPC supports various computational domains. A huge amount of jobs from a large group of users prefer to complete their jobs in this kind of system. Therefore, managing the jobs or job scheduling is very important since it involves the overall system efficiency. The analysis of an HPC-workload log file is a solution to improve system efficiency. Because some information may appear in the log file, this information can help the system scheduler to make an appropriate decision for job scheduling in the HPC system. This research proposed predictive models for predicting the job status at the finishing state in the HPC system. The model can be used as a tool for monitoring the jobs in the HPC system. We develop and build the three models including HPC-CNN, HPC-AlexNet, and HPC-VGG16 based on the two different learning techniques, which comprise Initial and Transfer Learning of Convolutional Neural Network based on the HPC-work load dataset. Moreover, the three state-of-the-art Machine Learning methods: Classification and Regression Tree (CART), Artificial Neural Network (ANN), and Support Vector Machine (SVM) are used as the baseline models for performance comparison. The results show that the model that performs the best predictive performance is the proposed HPC-CNN model. It archives 76.48% accuracy of the prediction followed with the CART model (75.60%), while the SVM model performs lowest the accuracy at 66.80%.

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An optimum ANN-based breast cancer diagnosis: Bridging gaps between ANN learning and decision-making goals

Cited by 76 publications

References 45 publications

Path Loss Predictions in the VHF and UHF Bands Within Urban Environments: Experimental Investigation of Empirical, Heuristics and Geospatial Models

Path Loss Predictions in the VHF and UHF Bands Within Urban Environments: Experimental Investigation of Empirical, Heuristics and Geospatial Models

Forward Selection pada Support Vector Machine untuk Memprediksi Kanker Payudara

A Comparative Study of Learning Techniques with Convolutional Neural Network Based on HPC-Workload Dataset

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