scholarly journals Using Deep Learning Neural Network in Artificial Intelligence Technology to Classify Beef Cuts

2021 ◽  
Vol 2 ◽  
Author(s):  
Sunil GC ◽  
Borhan Saidul Md ◽  
Yu Zhang ◽  
Demetris Reed ◽  
Mostofa Ahsan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
New York ◽  
Deep Learning ◽  
Data Augmentation ◽  
Performance Metrics ◽  
Vision System ◽  
Neutral Network ◽  
Learning Technology ◽  
Deep Learning Neural Network

The objective of this research was to evaluate the deep learning neural network in artificial intelligence (AI) technologies to rapidly classify seven different beef cuts (bone in rib eye steak, boneless rib eye steak, chuck steak, flank steak, New York strip, short rib, and tenderloin). Color images of beef samples were acquired from a laboratory-based computer vision system and collected from the Internet (Google Images) platforms. A total of 1,113 beef cut images were used as training, validation, and testing data subsets for this project. The model developed from the deep learning neural network algorithm was able to classify certain beef cuts (flank steak and tenderloin) up to 100% accuracy. Two pretrained convolution neutral network (CNN) models Visual Geometry Group (VGG16) and Inception ResNet V2 were used to train, validate, and test these models in classifying beef cut images. An image augmentation technique was incorporated in the convolution neutral network models for avoiding the overfitting problems, which demonstrated an improvement in the performance of the image classifier model. The VGG16 model outperformed the Inception ResNet V2 model. The VGG16 model coupled with data augmentation technique was able to achieve the highest accuracy of 98.6% on 116 test images, whereas Inception ResNet V2 accomplished a maximum accuracy of 95.7% on the same test images. Based on the performance metrics of both models, deep learning technology evidently showed a promising effort for beef cuts recognition in the meat science industry.

scholarly journals Artificial Intelligence Sebagai Penggerak Industri 4.0 dan Tantangannya Bagi Sektor Pemerintah dan Swasta

2019 ◽  
Vol 10 (1) ◽  
pp. 68
Author(s):  
Reza Yogaswara
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Big Data ◽  
Deep Learning ◽  
Internet Of Things ◽  
Competitive Advantage ◽  
Smart City ◽  
Chief Information Officer ◽  
Deep Learning Neural Network

Artificial Intelligence (AI) atau kecerdasan buatan menjadi penggerak revolusi industri 4.0 yang menjanjikan banyak kemudahan bagi sektor pemerintah maupun industri. Internet of Things (IoT) dan big data contohnya dimana AI dapat diimplementasikan, teknologi yang telah banyak diadopsi di era industri 4.0 ini mampu menghubungkan setiap perangkat, seseorang dapat mengotomatisasi semua perangkat tanpa harus berada di lokasi, lebih dari itu, saat ini telah banyak mesin yang dapat menginterprestasi suatu kondisi atau kejadian tertentu dengan bantuan AI, sebagaimana telah kamera cerdas pendeteksi kepadatan volume kendaraan di jalan raya menggunakan teknologi Deep Learning Neural Network, yang telah diimplementasikan pada beberapa Pemerintah Daerah Kabupaten dan Kota dalam mendukung program Smart City yang telah dicanangkan. Pada sektor industri, banyak juga dari mereka yang telah mengotomatisasi mesin produksi dan manufaktur menggunakan robot dan Artificial Intelligence, sehingga Industri 4.0 akan meningkatkan daya saing melalui perangkat cerdas, setiap entitas yang mampu menguasai teknologi ini disitulah keunggulan kompetitifnya (competitive advantage). Namun ditengah perkembangan industri 4.0 yang cukup masif pemerintah harus bergerak cepat dalam mengadopsi platform ini, jika tidak, mereka akan menurunkan efisiensi proses bisnis untuk menjaga stabilitas layanan publik. Oleh sebab itu diperlukan keilmuan dan pemahaman yang benar bagi pemerintah dalam menghadapai era Industri 4.0, dimana Chief Information Officer (CIO) dapat mengambil peranan penting dalam memberikan dukungan yang didasari atas keilmuan mereka terkait tren teknologi industri 4.0, khususnya AI yang telah banyak diadopsi di berbagai sektor.

scholarly journals Design of Fault Diagnosis Algorithm for Electromechanical System Based on Artificial Intelligence

2021 ◽  
Vol 2050 (1) ◽  
pp. 012011
Author(s):  
Fuyou Zhao ◽  
Mingying Huo ◽  
Naiming Qi ◽  
Lianfeng Li ◽  
Weiwei Cui
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Data Sets ◽  
Learning Technology ◽  
Electrical System ◽  
Fault Recognition ◽  
Fault Features ◽  
Diagnosis Method

Abstract A relatively perfect system for the fault diagnosis of mechanical and electrical products has been formed through decades of development. Nevertheless, the traditional fault diagnosis methods fail to cope with the gradual huge mechanical and electrical system. As a result, the advantages of fault diagnosis mode driven by data are increasingly prominent. Meanwhile, the effect of fault diagnosis has exceeded the traditional fault diagnosis methods in many fields. Through the use of the deep learning technology based on artificial intelligence, it carries out mapping and fitting. By fully taking advantages of neural network, it can effectively obtain the accurate classification of fault data. A fault diagnosis method based on the fault data of mechanical and electrical system is designed in this thesis. When it comes to the basic process, it is to take data sets for different mechanical and electrical products. Through the use of feature engineering method, it extracts the fault features of data. Through the use of deep learning technology, it carries out the intelligent diagnosis. According to the experimental results, it indicates that the fault diagnosis method based on deep learning technology can distinguish a variety of fault modes in mechanical and electrical system in an effective way. What’s more, good classification results in fault recognition have been achieved by a variety of deep convolutional neural network structures, so the feasibility of the method is further verified.

scholarly journals Detection of Apple Lesions in Orchards Based on Deep Learning Methods of CycleGAN and YOLOV3-Dense

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yunong Tian ◽  
Guodong Yang ◽  
Zhe Wang ◽  
En Li ◽  
Zize Liang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Optical Sensors ◽  
Data Augmentation ◽  
Image Data ◽  
Disease Diagnosis ◽  
Lesion Detection ◽  
Training Data ◽  
Yield Reduction ◽  
Learning Technology

Plant disease is one of the primary causes of crop yield reduction. With the development of computer vision and deep learning technology, autonomous detection of plant surface lesion images collected by optical sensors has become an important research direction for timely crop disease diagnosis. In this paper, an anthracnose lesion detection method based on deep learning is proposed. Firstly, for the problem of insufficient image data caused by the random occurrence of apple diseases, in addition to traditional image augmentation techniques, Cycle-Consistent Adversarial Network (CycleGAN) deep learning model is used in this paper to accomplish data augmentation. These methods effectively enrich the diversity of training data and provide a solid foundation for training the detection model. In this paper, on the basis of image data augmentation, densely connected neural network (DenseNet) is utilized to optimize feature layers of the YOLO-V3 model which have lower resolution. DenseNet greatly improves the utilization of features in the neural network and enhances the detection result of the YOLO-V3 model. It is verified in experiments that the improved model exceeds Faster R-CNN with VGG16 NET, the original YOLO-V3 model, and other three state-of-the-art networks in detection performance, and it can realize real-time detection. The proposed method can be well applied to the detection of anthracnose lesions on apple surfaces in orchards.

scholarly journals Generative Art: Between the Nodes of Neuron Networks

Artnodes ◽  
2020 ◽  
Author(s):  
Bruno Caldas Vianna
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Generative Art ◽  
Creation Process ◽  
Neuron Networks ◽  
Starting Point ◽  
Deep Learning Neural Network ◽  
In The Beginning

This article uses the exhibition “Infinite Skulls”, which happened in Paris in the beginning of 2019, as a starting point to discuss art created by artificial intelligence and, by extension, unique pieces of art generated by algorithms. We detail the development of DCGAN, the deep learning neural network used in the show, from its cybernetics origin. The show and its creation process are described, identifying elements of creativity and technique, as well as question of the authorship of works. Then it frames these works in the context of generative art, pointing affinities and differences, and the issues of representing through procedures and abstractions. It describes the major breakthrough of neural network for technical images as the ability to represent categories through an abstraction, rather than images themselves. Finally, it tries to understand neural networks more as a tool for artists than an autonomous art creator.

scholarly journals Bearing Fault Diagnosis Based on Multiscale Convolutional Neural Network Using Data Augmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Seungmin Han ◽  
Seokju Oh ◽  
Jongpil Jeong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Feature Learning ◽  
Learning Technology ◽  
Financial Loss ◽  
Training Time ◽  
Imbalance Problem

Bearings are one of the most important parts of a rotating machine. Bearing failure can lead to mechanical failure, financial loss, and even personal injury. In recent years, various deep learning techniques have been used to diagnose bearing faults in rotating machines. However, deep learning technology has a data imbalance problem because it requires huge amounts of data. To solve this problem, we used data augmentation techniques. In addition, Convolutional Neural Network, one of the deep learning models, is a method capable of performing feature learning without prior knowledge. However, since conventional fault diagnosis based on CNN can only extract single-scale features, not only useful information may be lost but also domain shift problems may occur. In this paper, we proposed a Multiscale Convolutional Neural Network (MSCNN) to extract more powerful and differentiated features from raw signals. MSCNN can learn more powerful feature expression than conventional CNN through multiscale convolution operation and reduce the number of parameters and training time. The proposed model proved better results and validated the effectiveness of the model compared to 2D-CNN and 1D-CNN.

scholarly journals Toward a Highly Accurate Classification of Underwater Cable Images via Deep Convolutional Neural Network

2020 ◽  
Vol 8 (11) ◽  
pp. 924
Author(s):  
Guan Wei Thum ◽  
Sai Hong Tang ◽  
Siti Azfanizam Ahmad ◽  
Moath Alrifaey
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Power Transmission ◽  
Vision System ◽  
Autonomous Underwater Vehicles ◽  
Deep Convolutional Neural Network ◽  
Computational Time ◽  
Learning Method

Underwater cables or pipelines are commonly utilized elements in ocean research, marine engineering, power transmission, and communication-based activities. Their performance necessitates regularly conducted inspection for maintenance purposes. A vision system is commonly used by autonomous underwater vehicles (AUVs) to track and search for underwater cable. Its traditional methods are characteristically applicable in AUVs, wherein they are equipped with handcrafted features and shallow trainable architectures. However, such methods are subpar or even incapable of tracking underwater cable in fast-changing and complex underwater conditions. In contrast to this, the deep learning method is linked with the capacity to learn semantic, high-level, and deeper features, thus rendering it recommended for performing underwater cable tracking. In this study, several deep Convolutional Neural Network (CNN) models were proposed to classify underwater cable images obtained from a set of underwater images, whereby transfer learning and data augmentation were applied to enhance the classification accuracy. Following a comparison and discussion regarding the performance of these models, MobileNetV2 outperformed among other models and yielded lower computational time and the highest accuracy for classifying underwater cable images at 93.5%. Hence, the main contribution of this study is geared toward developing a deep learning method for underwater cable image classification.

scholarly journals Data Augmentation using Adversarial Networks for Tea Diseases Detection

2020 ◽  
Vol 20 (1) ◽  
pp. 29
Author(s):  
R. Sandra Yuwana ◽  
Fani Fauziah ◽  
Ana Heryana ◽  
Dikdik Krisnandi ◽  
R. Budiarianto Suryo Kusumo ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Original Data ◽  
The Other ◽  
Generative Adversarial Networks ◽  
Learning Technology ◽  
Limited Data ◽  
Adversarial Networks

Deep learning technology has a better result when trained using an abundant amount of data. However, collecting such data is expensive and time consuming.  On the other hand, limited data often be the inevitable condition. To increase the number of data, data augmentation is usually implemented.  By using it, the original data are transformed, by rotating, shifting, or both, to generate new data artificially. In this paper, generative adversarial networks (GAN) and deep convolutional GAN (DCGAN) are used for data augmentation. Both approaches are applied for diseases detection. The performance of the tea diseases detection on the augmented data is evaluated using various deep convolutional neural network (DCNN) including AlexNet, DenseNet, ResNet, and Xception.  The experimental results indicate that the highest GAN accuracy is obtained by DenseNet architecture, which is 88.84%, baselines accuracy on the same architecture is 86.30%. The results of DCGAN accuracy on the use of the same architecture show a similar trend, which is 88.86%. 

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