Temat: CNN - Prolib Integro

Skocz do pozycji: 1.

Tytuł:: Histopathology image classification using hybrid parallel structured DEEP-CNN models
Autorzy:: Dsouza, Kevin Joy
Ansari, Zahid Ahmed
Tematy:: breast cancer CNN
loss
accuracy
precision
confusion matrix; Pokaż więcej
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Powiązania:: https://bibliotekanauki.pl/articles/2097423.pdf Link otwiera się w nowym oknie
Opis:: The healthcare industry is one of the many out there that could majorly benefit from advancement in the technology it utilizes. Artificial intelligence (AI) technologies are especially integral and specifically deep learning (DL); a highly useful data-driven technology. It is applied in a variety of different methods but it mainly depends on the structure of the available data. However, with varying applications, this technology produces data in different contexts with particular connotations. Reports which are the images of scans play a great role in identifying the existence of the disease in a patient. Further, the automation in processing these images using technology like CNN-based models makes it highly efficient in reducing human errors otherwise resulting in large data. Hence this study presents a hybrid deep learning architecture to classify the histopathology images to identify the presence of cancer in a patient. Further, the proposed models are parallelized using the TensorFlow-GPU framework to accelerate the training of these deep CNN (Convolution Neural Networks) archi-tectures. This study uses the transfer learning technique during training and early stopping criteria are used to avoid overfitting during the training phase. these models use LSTM parallel layer imposed in the model to experiment with four considered architectures such as MobileNet, VGG16, and ResNet with 101 and 152 layers. The experimental results produced by these hybrid models show that the capability of Hybrid ResNet101 and Hybrid ResNet152 architectures are highly suitable with an accuracy of 90% and 92%. Finally, this study concludes that the proposed Hybrid ResNet-152 architecture is highly efficient in classifying the histopathology images. The proposed study has conducted a well-focused and detailed experimental study which will further help researchers to understand the deep CNN architectures to be applied in application development.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: CNN and LSTM for the classification of parkinsons disease based on the GTCC and MFCC
Autorzy:: Boualoulou, Nouhaila
Drissi, Taoufiq Belhoussine
Nsiri, Benayad
Tematy:: Parkinson's disease
voice signal
GTCC
MFCC
DWT
EMD
CNN and LSTM; Pokaż więcej
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Powiązania:: https://bibliotekanauki.pl/articles/30148250.pdf Link otwiera się w nowym oknie
Opis:: Parkinson's disease is a recognizable clinical syndrome with a variety of causes and clinical presentations; it represents a rapidly growing neurodegenerative disorder. Since about 90 percent of Parkinson's disease sufferers have some form of early speech impairment, recent studies on tele diagnosis of Parkinson's disease have focused on the recognition of voice impairments from vowel phonations or the subjects' discourse. This paper presents a new approach for Parkinson's disease detection from speech sounds that are based on CNN and LSTM and uses two categories of characteristics. These are Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Cepstral Coefficients (GTCC) obtained from noise-removed speech signals with comparative EMD-DWT and DWT-EMD analysis. The proposed model is divided into three stages. In the first step, noise is removed from the signals using the EMD-DWT and DWT-EMD methods. In the second step, the GTCC and MFCC are extracted from the enhanced audio signals. The classification process is carried out in the third step by feeding these features into the LSTM and CNN models, which are designed to define sequential information from the extracted features. The experiments are performed using PC-GITA and Sakar datasets and 10-fold cross validation method, the highest classification accuracy for the Sakar dataset reached 100% for both EMD-DWT-GTCC-CNN and DWT-EMD-GTCC-CNN, and for the PC-GITA dataset, the accuracy is reached 100% for EMD-DWT-GTCC-CNN and 96.55% for DWT-EMD-GTCC-CNN. The results of this study indicate that the characteristics of GTCC are more appropriate and accurate for the assessment of PD than MFCC.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: A robust ensemble model for spoken language recognition
Autorzy:: Woods, Nancy
Babatunde, Gideon
Tematy:: spoken language recognition
computer vision
image recognition
CNN
rozpoznawanie języka mówionego
widzenie komputerowe
rozpoznawanie obrazu; Pokaż więcej
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Powiązania:: https://bibliotekanauki.pl/articles/118275.pdf Link otwiera się w nowym oknie
Opis:: The identity of a language being spoken has been tackled over the years via statistical models on audio samples. A drawback of these approaches is the unavailability of phonetically transcribed data for all languages. This work proposes an approach based on image classification that utilized image representations of audio samples. Our model used Neural Networks and deep learning algorithms to analyse and classify three languages. The input to our network is a Spectrogram that was processed through the networks to extract local visual and temporal features for language prediction. From the model, we achieved 95.56 % accuracy on the test samples from the 3 languages.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: An overview of deep learning techniques for short-term electricity load forecasting
Autorzy:: Adewuyi, Saheed
Aina, Segun
Uzunuigbe, Moses
Lawal, Aderonke
Oluwaranti, Adeniran
Tematy:: Short-term Load Forecasting
Deep Learning Architectures
RNN
LSTM
CNN
SAE
prognozowanie obciążenia krótkoterminowego
architektura głębokiego uczenia; Pokaż więcej
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Powiązania:: https://bibliotekanauki.pl/articles/117932.pdf Link otwiera się w nowym oknie
Opis:: This paper presents an overview of some Deep Learning (DL) techniques applicable to forecasting electricity consumptions, especially in the short-term horizon. The paper introduced key parts of four DL architectures including the RNN, LSTM, CNN and SAE, which are recently adopted in implementing Short-term (electricity) Load Forecasting problems. It further presented a model approach for solving such problems. The eventual implication of the study is to present an insightful direction about concepts of the DL methods for forecasting electricity loads in the short-term period, especially to a potential researcher in quest of solving similar problems.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: A deep learning model for electricity demand forecasting based on a tropical data
Autorzy:: Adewuyi, Saheed A.
Aina, Segun
Oluwaranti, Adeniran I.
Tematy:: Electricity Demand Forecasting
STLF
Deep Learning Techniques
LSTM
CNN
MLP
prognozowanie zapotrzebowania na energię elektryczną
techniki głębokiego uczenia; Pokaż więcej
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Powiązania:: https://bibliotekanauki.pl/articles/118123.pdf Link otwiera się w nowym oknie
Opis:: Electricity demand forecasting is a term used for prediction of users’ consumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its market deregulation has greatly amplified its essence. Despite numerous studies on the subject using certain classical approaches, there exists an opportunity for exploration of more sophisticated methods such as the deep learning (DL) techniques. Successful researches about DL applications to computer vision, speech recognition, and acoustic computing problems are motivation. However, such researches are not sufficiently exploited for electricity demand forecasting using DL methods. In this paper, we considered specific DL techniques (LSTM, CNN, and MLP) to short-term load forecasting problems, using tropical institutional data obtained from a Transmission Company. We also test how accurate are predictions across the techniques. Our results relatively revealed models appropriateness for the problem.
Dostawca treści:: Biblioteka Nauki

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