Autonomous Tomato Harvesting Robotic System in Greenhouses: Deep Learning Classification

Solanum lycopersicum or generally known as tomato came from countries of South America and has been growing in many tropical countries and its healthy nutrients in tomato becomes one of the food demand by the locals in Malaysia when their lifestyle shifted to more concern for healthy food. Since export value and production has increased for the past few years, a vast amount of labours considered for the fruit-picking process. Hence, farmers are now preferring to look for automation to replace labour problems and high cost that they are facing. To pick a correct fruit within clusters, a harvesting robot requires guidance so that it can detect a fruit accurately. In this study, a new classification algorithm using deep learning specifically convolution neural network to classify the image is either a tomato or not tomato and next, the image is classified into either a ripe or unripe tomato. Furthermore, there are two classification neural networks which are tomato or not tomato and ripe and unripe tomato. Each network consists of 600 training data and 33 testing data. The accuracies that obtained from network 1 (tomato or not tomato) and network 2 (ripe or unripe tomato) are 76.366% and 98.788% respectively.

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Laplacian networks: bounding indicator function smoothness for neural networks robustness

APSIPA Transactions on Signal and Information Processing ◽

10.1017/atsip.2021.2 ◽

2021 ◽

Vol 10 ◽

Author(s):

Carlos Lassance ◽

Vincent Gripon ◽

Antonio Ortega

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Supervised Learning ◽

Indicator Function ◽

Training Data ◽

Theoretical Justification ◽

The Past ◽

Noisy Examples

For the past few years, deep learning (DL) robustness (i.e. the ability to maintain the same decision when inputs are subject to perturbations) has become a question of paramount importance, in particular in settings where misclassification can have dramatic consequences. To address this question, authors have proposed different approaches, such as adding regularizers or training using noisy examples. In this paper we introduce a regularizer based on the Laplacian of similarity graphs obtained from the representation of training data at each layer of the DL architecture. This regularizer penalizes large changes (across consecutive layers in the architecture) in the distance between examples of different classes, and as such enforces smooth variations of the class boundaries. We provide theoretical justification for this regularizer and demonstrate its effectiveness to improve robustness on classical supervised learning vision datasets for various types of perturbations. We also show it can be combined with existing methods to increase overall robustness.

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Convolutional Neural Network

10.4018/978-1-6684-2408-7.ch077 ◽

2022 ◽

pp. 1559-1575

Author(s):

Mário Pereira Véstias

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Model ◽

Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

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Deep Learning Neural Networks to Predict Serious Complications After Bariatric Surgery: Analysis of Scandinavian Obesity Surgery Registry Data (Preprint)

10.2196/preprints.15992 ◽

2019 ◽

Author(s):

Yang Cao ◽

Scott Montgomery ◽

Johan Ottosson ◽

Erik Näslund ◽

Erik Stenberg

Keyword(s):

Neural Network ◽

Neural Networks ◽

Bariatric Surgery ◽

Deep Learning ◽

Postoperative Complications ◽

Obesity Surgery ◽

Test Data ◽

Registry Data ◽

Training Data ◽

Complications After Bariatric Surgery

BACKGROUND Obesity is one of today’s most visible public health problems worldwide. Although modern bariatric surgery is ostensibly considered safe, serious complications and mortality still occur in some patients. OBJECTIVE This study aimed to explore whether serious postoperative complications of bariatric surgery recorded in a national quality registry can be predicted preoperatively using deep learning methods. METHODS Patients who were registered in the Scandinavian Obesity Surgery Registry (SOReg) between 2010 and 2015 were included in this study. The patients who underwent a bariatric procedure between 2010 and 2014 were used as training data, and those who underwent a bariatric procedure in 2015 were used as test data. Postoperative complications were graded according to the Clavien-Dindo classification, and complications requiring intervention under general anesthesia or resulting in organ failure or death were considered serious. Three supervised deep learning neural networks were applied and compared in our study: multilayer perceptron (MLP), convolutional neural network (CNN), and recurrent neural network (RNN). The synthetic minority oversampling technique (SMOTE) was used to artificially augment the patients with serious complications. The performances of the neural networks were evaluated using accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the receiver operating characteristic curve. RESULTS In total, 37,811 and 6250 patients were used as the training data and test data, with incidence rates of serious complication of 3.2% (1220/37,811) and 3.0% (188/6250), respectively. When trained using the SMOTE data, the MLP appeared to have a desirable performance, with an area under curve (AUC) of 0.84 (95% CI 0.83-0.85). However, its performance was low for the test data, with an AUC of 0.54 (95% CI 0.53-0.55). The performance of CNN was similar to that of MLP. It generated AUCs of 0.79 (95% CI 0.78-0.80) and 0.57 (95% CI 0.59-0.61) for the SMOTE data and test data, respectively. Compared with the MLP and CNN, the RNN showed worse performance, with AUCs of 0.65 (95% CI 0.64-0.66) and 0.55 (95% CI 0.53-0.57) for the SMOTE data and test data, respectively. CONCLUSIONS MLP and CNN showed improved, but limited, ability for predicting the postoperative serious complications after bariatric surgery in the Scandinavian Obesity Surgery Registry data. However, the overfitting issue is still apparent and needs to be overcome by incorporating intra- and perioperative information. CLINICALTRIAL

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Deep Learning Neural Networks to Predict Serious Complications After Bariatric Surgery: Analysis of Scandinavian Obesity Surgery Registry Data

JMIR Medical Informatics ◽

10.2196/15992 ◽

2020 ◽

Vol 8 (5) ◽

pp. e15992 ◽

Cited By ~ 1

Author(s):

Yang Cao ◽

Scott Montgomery ◽

Johan Ottosson ◽

Erik Näslund ◽

Erik Stenberg

Keyword(s):

Neural Network ◽

Neural Networks ◽

Bariatric Surgery ◽

Deep Learning ◽

Postoperative Complications ◽

Obesity Surgery ◽

Test Data ◽

Registry Data ◽

Training Data ◽

Complications After Bariatric Surgery

Background Obesity is one of today’s most visible public health problems worldwide. Although modern bariatric surgery is ostensibly considered safe, serious complications and mortality still occur in some patients. Objective This study aimed to explore whether serious postoperative complications of bariatric surgery recorded in a national quality registry can be predicted preoperatively using deep learning methods. Methods Patients who were registered in the Scandinavian Obesity Surgery Registry (SOReg) between 2010 and 2015 were included in this study. The patients who underwent a bariatric procedure between 2010 and 2014 were used as training data, and those who underwent a bariatric procedure in 2015 were used as test data. Postoperative complications were graded according to the Clavien-Dindo classification, and complications requiring intervention under general anesthesia or resulting in organ failure or death were considered serious. Three supervised deep learning neural networks were applied and compared in our study: multilayer perceptron (MLP), convolutional neural network (CNN), and recurrent neural network (RNN). The synthetic minority oversampling technique (SMOTE) was used to artificially augment the patients with serious complications. The performances of the neural networks were evaluated using accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the receiver operating characteristic curve. Results In total, 37,811 and 6250 patients were used as the training data and test data, with incidence rates of serious complication of 3.2% (1220/37,811) and 3.0% (188/6250), respectively. When trained using the SMOTE data, the MLP appeared to have a desirable performance, with an area under curve (AUC) of 0.84 (95% CI 0.83-0.85). However, its performance was low for the test data, with an AUC of 0.54 (95% CI 0.53-0.55). The performance of CNN was similar to that of MLP. It generated AUCs of 0.79 (95% CI 0.78-0.80) and 0.57 (95% CI 0.59-0.61) for the SMOTE data and test data, respectively. Compared with the MLP and CNN, the RNN showed worse performance, with AUCs of 0.65 (95% CI 0.64-0.66) and 0.55 (95% CI 0.53-0.57) for the SMOTE data and test data, respectively. Conclusions MLP and CNN showed improved, but limited, ability for predicting the postoperative serious complications after bariatric surgery in the Scandinavian Obesity Surgery Registry data. However, the overfitting issue is still apparent and needs to be overcome by incorporating intra- and perioperative information.

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Plastic Gasket Defect Detection Based on Transfer Learning

Scientific Programming ◽

10.1155/2021/5990020 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Xieyi Chen ◽

Dongyun Wang ◽

Jinjun Shao ◽

Jun Fan

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Defect Detection ◽

Data Augmentation ◽

Surface Defects ◽

Visual Detection ◽

Training Data

To automatically detect plastic gasket defects, a set of plastic gasket defect visual detection devices based on GoogLeNet Inception-V2 transfer learning was designed and established in this study. The GoogLeNet Inception-V2 deep convolutional neural network (DCNN) was adopted to extract and classify the defect features of plastic gaskets to solve the problem of their numerous surface defects and difficulty in extracting and classifying the features. Deep learning applications require a large amount of training data to avoid model overfitting, but there are few datasets of plastic gasket defects. To address this issue, data augmentation was applied to our dataset. Finally, the performance of the three convolutional neural networks was comprehensively compared. The results showed that the GoogLeNet Inception-V2 transfer learning model had a better performance in less time. It means it had higher accuracy, reliability, and efficiency on the dataset used in this paper.

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Face Recognition with CNN and Inception Deep Learning Models

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4476.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 1932-1938

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Face Recognition ◽

Convolutional Neural Network ◽

Training Image ◽

Learning Models ◽

Training Images ◽

Testing Data ◽

Facial Images

In this work, deep learning methods are used to classify the facial images. ORL Database is used for the purpose of training the models and for testing. Three kinds of models are developed and their performances are measured. Convolutional Neural Networks (CNN), Convolutional Neural Network Based Inception Model with single training image per class (CNN-INC) and Convolutional Neural Network Based Inception Model with several training images per class (CNN-INC-MEAN) are developed. The ORL database has ten facial images for each person. Five images are used for training purpose and remaining 5 images are used for testing. The five images for the training are chosen randomly so that two sets of training and testing data is generated. The models are trained and tested on the two sets that are drawn from the same population. The results are presented for accuracy of face recognition

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Automatic Defects Segmentation and Identification by Deep Learning Algorithm with Pulsed Thermography: Synthetic and Experimental Data

Big Data and Cognitive Computing ◽

10.3390/bdcc5010009 ◽

2021 ◽

Vol 5 (1) ◽

pp. 9

Author(s):

Qiang Fang ◽

Clemente Ibarra-Castanedo ◽

Xavier Maldague

Keyword(s):

Neural Network ◽

Experimental Data ◽

Neural Networks ◽

Deep Learning ◽

Infrared Thermography ◽

Learning Algorithm ◽

Synthetic Data ◽

Training Data ◽

Experimental Database ◽

The Neural Network

In quality evaluation (QE) of the industrial production field, infrared thermography (IRT) is one of the most crucial techniques used for evaluating composite materials due to the properties of low cost, fast inspection of large surfaces, and safety. The application of deep neural networks tends to be a prominent direction in IRT Non-Destructive Testing (NDT). During the training of the neural network, the Achilles heel is the necessity of a large database. The collection of huge amounts of training data is the high expense task. In NDT with deep learning, synthetic data contributing to training in infrared thermography remains relatively unexplored. In this paper, synthetic data from the standard Finite Element Models are combined with experimental data to build repositories with Mask Region based Convolutional Neural Networks (Mask-RCNN) to strengthen the neural network, learning the essential features of objects of interest and achieving defect segmentation automatically. These results indicate the possibility of adapting inexpensive synthetic data merging with a certain amount of the experimental database for training the neural networks in order to achieve the compelling performance from a limited collection of the annotated experimental data of a real-world practical thermography experiment.

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COMBINATION OF SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE (SMOTE) AND BACKPROPAGATION NEURAL NETWORK TO CONTRACEPTIVE IUD PREDICTION

MEDIA STATISTIKA ◽

10.14710/medstat.13.1.36-46 ◽

2020 ◽

Vol 13 (1) ◽

pp. 36-46

Author(s):

Mustaqim Mustaqim ◽

Budi Warsito ◽

Bayu Surarso

Keyword(s):

Neural Network ◽

Neural Networks ◽

Contraceptive Use ◽

Imbalanced Data ◽

Training Data ◽

Backpropagation Neural Network ◽

Accuracy Rate ◽

Minority Class ◽

Testing Data ◽

Major Data

Data imbalance occurs when the amount of data in a class is more than other data. The majority class is more data, while the minority class is fewer. Imbalance class will decrease the performance of the classification algorithm. Data on IUD contraceptive use is imbalanced data. National IUD failure in 2018 was 959 or 3.5% from 27.400 users. Synthetic minority oversampling technique (SMOTE) is used to balance data on IUD failure. Balanced data is then predicted with neural networks. The system is for predicting someone when using IUD whether they have a pregnancy or not. This study uses 250 data with 235 major data (not pregnant) and 15 minor data (pregnant). From 250 data divided into two parts, 225 training and 25 testing data. Minority class on training data will be duplicated to 1524%, so that the amount of minority data become balanced with the majority data. The results of predictive with an accuracy rate of 99.9% at 1000 epoch.

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Direct Steering of de novo Molecular Generation using Descriptor Conditional Recurrent Neural Networks (cRNNs)

10.26434/chemrxiv.9860906.v2 ◽

2019 ◽

Cited By ~ 1

Author(s):

Panagiotis-Christos Kotsias ◽

Josep Arús-Pous ◽

Hongming Chen ◽

Ola Engkvist ◽

Christian Tyrchan ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Recurrent Neural Networks ◽

Molecular Descriptors ◽

De Novo ◽

Simple Approach ◽

Regions Of Interest ◽

Generative Process ◽

The Past

Deep learning has acquired considerable momentum over the past couple of years in the domain of de-novo drug design. Particularly, transfer and reinforcement learning have demonstrated the capability of steering the generative process towards chemical regions of interest. In this work, we propose a simple approach to the focused generative task by constructing a conditional recurrent neural network (cRNN). For this purpose, we aggregate selected molecular descriptors along with a QSAR-based bioactivity label and transform them into initial LSTM states before starting the generation of SMILES strings that are focused towards the aspired properties. We thus tackle the inverse QSAR problem directly by training on molecular descriptors, instead of iteratively optimizing around a set of candidate molecules. The trained cRNNs are able to generate molecules near multiple specified conditions, while maintaining an output that is more focused than traditional RNNs yet less focused than autoencoders. The method shows promise for applications in both scaffold hoping and ligand series generation, depending on whether the cRNN is trained on calculated scalar molecular properties or structural fingerprints. This also demonstrates that fingerprint-to-molecule decoding is feasible, leading to molecules that are similar – if not identical – to the ones the fingerprints originated from. Additionally, the cRNN is able to generate a larger fraction of predicted active compounds against the DRD2 receptor when compared to an RNN trained with the transfer learning model.

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Pengolahan Citra untuk Membedakan Ikan Segar dan Tidak Segar Menggunakan Convolutional Neural Network

Indonesian Journal of Applied Informatics ◽

10.20961/ijai.v5i1.41770 ◽

2021 ◽

Vol 5 (1) ◽

pp. 11

Author(s):

Arif Agustyawan

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Training Data ◽

Independent Learning ◽

Fresh Fish ◽

Testing Data ◽

Neural Network Algorithm ◽

Sorting Process ◽

Manual Methods

Abstrak: Proses penyortiran ikan yang dilakukan oleh nelayan atau penjual, untuk menyeleksi ikan berdasar kualitasnya masih menggunakan metode manual dan terkadang meleset karena faktor keterbatasan indra penglihatan ketika lelah. Selama ini pemeriksaan hanya dillihat secara fisik. Akibatnya, saat akan dikonsumsi ikan tersebut kerap kali sudah rusak. Penelitian ini mencoba menerapkan algoritma Convolutional Neural Network (CNN) untuk membedakan ikan segar dan tidak segar. Convolutional Neural Network merupakan salah satu metode deep learning yang mampu melakukan proses pembelajaran mandiri untuk pengenalan objek, ekstraksi objek, dan klasifikasi objek. Pada penelitian ini, diterapkan algoritma Convolutional Neural Network untuk membedakan ikan segar dan tidak segar. Proses learning jaringan menghasilkan akurasi 100% terhadap data training dan data validation. Pengujian terhadap data testing juga menghasilkan akurasi 100%. Hasil penelitian ini menunjukan bahwa penggunaan metode Convolutional Neural Network mampu mengidentifikasi dan mengklasifikasikan ikan segar dan tidak segar dengan sangat baik.___________________________Abstract:The fish sorting process carried out by fishermen or sellers, to select fish based on quality is still using manual methods and sometimes misses due to the limited sense of sight when tired. So far the examination has only been seen physically. As a result, the fish will often be damaged when consumed. This study tries to apply the Convolutional Neural Network (CNN) algorithm to distinguish between fresh and non-fresh fish. Convolutional Neural Network is a method of deep learning that is capable of conducting independent learning processes for object recognition, object extraction, and object classification. In this study, the Convolutional Neural Network algorithm is applied to distinguish between fresh and non-fresh fish. Network learning process produces 100% accuracy of training data and data validation. Testing of testing data also results in 100% accuracy. The results of this study indicate that the use of the Convolutional Neural Network method can identify and classify fresh and non-fresh fish very well.

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