Pixel normalization from numeric data as input to neural networks: For machine learning and image processing

2017 ◽  
Author(s):  
Parth Sane ◽  
Ravindra Agrawal
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Numeric Data

scholarly journals Image classification using Deep learning

2018 ◽  
Vol 7 (2.7) ◽  
pp. 614 ◽  
Author(s):  
M Manoj krishna ◽  
M Neelima ◽  
M Harshali ◽  
M Venu Gopala Rao
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Classical Problem

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.  

Artificial intelligence with neural networks in optical measurement and inspection systems

2020 ◽  
Vol 68 (6) ◽  
pp. 477-487
Author(s):  
Michael Heizmann ◽  
Alexander Braun ◽  
Markus Hüttel ◽  
Christina Klüver ◽  
Erik Marquardt ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Optical Measurement ◽  
Development Effort ◽  
Processing Methods ◽  
Machine Learning Methods ◽  
Inspection Systems ◽  
Classical Image

AbstractOptical measuring and inspection systems play an important role in automation as they allow a comprehensive and non-contact quality assessment of products and processes. In this field, too, systems are increasingly being used that apply artificial intelligence and machine learning, mostly by means of artificial neural networks. Results achieved with this approach are often very promising and require less development effort. However, the supplementation and replacement of classical image processing methods by machine learning methods is not unproblematic, especially in applications with high safety or quality requirements, since the latter have characteristics that differ considerably from classical image processing methods. In this paper, essential aspects and trends of machine learning and artificial intelligence for the application in optical measurement and inspection systems are presented and discussed.

scholarly journals Transmol: Repurposing Language Model for Molecular Generation

2021 ◽  
Author(s):  
Rustam Zhumagambetov ◽  
Vsevolod A. Peshkov ◽  
Siamac Fazli
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Language Processing ◽  
Scientific Community ◽  
De Novo ◽  
Language Model ◽  
The Core ◽  
Molecular Libraries ◽  
Core Metrics

Recent advances in convolutional neural networks have inspired the application of deep learning to other disciplines. Even though image processing and natural language processing have turned out to be the most successful, there are many other areas that have benefited, like computational chemistry in general and drug design in particular. From 2018 the scientific community has seen a surge of methodologies related to the generation of diverse molecular libraries using machine learning. However, no algorithm used an attention mechanisms for <i>de novo</i> molecular generation. Here we employ a variant of transformers, a recent NLP architecture, for this purpose. We have achieved a statistically significant increase in some of the core metrics of the MOSES benchmark. Furthermore, a novel way of generating libraries fusing two molecules as seeds has been described.

Using Machine Learning, Image Processing and Neural Networks to Sense Bullying in K-12 Schools: Enhanced

2021 ◽  
pp. 1-12
Author(s):  
Lalit Kumar ◽  
Palash Goyal ◽  
Karan Malik ◽  
Rishav Kumar ◽  
Dhruv Shrivastav
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
K 12

scholarly journals Machine Learning Based Nutrient Deficiency Detection in Crops

2020 ◽  
Vol 8 (6) ◽  
pp. 5330-5333
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Convolutional Neural Networks ◽  
Crop Yield ◽  
Crop Production ◽  
Labour Force ◽  
Nutrient Deficiency ◽  
Input Image ◽  
Robotic Vehicle

Indian economy is mainly based on Agriculture, involves the process of cultivating certain plants for producing food and many other desired products and raising of domesticated animals. Nutrients play a major role in agriculture and crop production. There are number of reasons for decreasing of crop yield. One such factor involved is nutrient deficiency. The proper detection of nutrient deficiency and appropriate fertilizer for that deficiency are the major problems faced by many farmers. Hence, in order to improve productivity, Automation in agriculture evolved drastically in recent years. This paper aims at designing an automatic robotic vehicle which detects the nutrient deficiency in crops just by simply capturing the image of leaves of the crop plants. The captured image is then processed by using the convolutional neural networks (CNN). This technique uses captured image, processing it by comparing it with the already available dataset. When the input image is matched or partially matched with any one of the existing images in the dataset, it will provide the result of nutrient deficiency in crops, in terms of the percentage. The name of disease associated with nutrient deficiency and appropriate amount of fertilizer will be displayed in the LCD. This will reduce the problems of the labour force and the burden of farmers.

scholarly journals IMAGE RECOGNITION USING MACHINE LEARNING

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Abhinav N Patil
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Image Classification ◽  
Image Recognition ◽  
Training Dataset ◽  
Test Dataset ◽  
Human Support ◽  
And Training

Image recognition is important side of image processing for machine learning without involving any human support at any step. In this paper we study how image classification is completed using imagery backend. Couple of thousands of images of every, cats and dogs are taken then distributed them into category of test dataset and training dataset for our learning model. The results are obtained using custom neural network with the architecture of Convolution Neural Networks and Keras API.

scholarly journals Transmol: Repurposing Language Model for Molecular Generation

2021 ◽  
Author(s):  
Rustam Zhumagambetov ◽  
Vsevolod A. Peshkov ◽  
Siamac Fazli
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Language Processing ◽  
Scientific Community ◽  
De Novo ◽  
Language Model ◽  
The Core ◽  
Molecular Libraries ◽  
Core Metrics

Recent advances in convolutional neural networks have inspired the application of deep learning to other disciplines. Even though image processing and natural language processing have turned out to be the most successful, there are many other areas that have benefited, like computational chemistry in general and drug design in particular. From 2018 the scientific community has seen a surge of methodologies related to the generation of diverse molecular libraries using machine learning. However, no algorithm used an attention mechanisms for <i>de novo</i> molecular generation. Here we employ a variant of transformers, a recent NLP architecture, for this purpose. We have achieved a statistically significant increase in some of the core metrics of the MOSES benchmark. Furthermore, a novel way of generating libraries fusing two molecules as seeds has been described.

scholarly journals Integrating Traditional Machine Learning and Neural Networks for Image Processing

2021 ◽  
Author(s):  
Nikita Laptev ◽  
Vladislav Laptev ◽  
Olga Gerget ◽  
Dmitriy Kolpashchikov
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Short Term Memory ◽  
Learning Algorithms ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Neural Network Models ◽  
Fire Source ◽  
Artificial Neural

The article describes a feasibility study to assess the use of neural networks and traditional machine learning algorithms to solve various problems including image processing. A brief description of some algorithms of traditional machine learning, as well as anautomated service for choosing the best method for a specific task, is given. The authors also describe the features of artificial neural networks and the most popular places for theirapplication. An algorithm for solving the problem of detecting fire hazardous objects andlocalizing a fire source in a forest using video sequence frames is presented. The article compares the characteristics of artificial neural network models according to the followingcriteria: underlying architecture, the number of analyzed frames, the size of the input image, the transfer learning model used as a feature vector composing network. Acomparative analysis of traditional machine learning algorithms and neural networks withlong short-term memory in the problem of classification of forest fire hazards is made. A solution to localization of the source of fire based on clustering is described. A hybrid algorithm for finding a fire source in a forest is developed and illustrated.

Seismic image processing through the generative adversarial network

2019 ◽  
Vol 7 (3) ◽  
pp. SF15-SF26
Author(s):  
Francesco Picetti ◽  
Vincenzo Lipari ◽  
Paolo Bestagini ◽  
Stefano Tubaro
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Proof Of Concept ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Seismic Image ◽  
Geophysical Imaging

The advent of new deep-learning and machine-learning paradigms enables the development of new solutions to tackle the challenges posed by new geophysical imaging applications. For this reason, convolutional neural networks (CNNs) have been deeply investigated as novel tools for seismic image processing. In particular, we have studied a specific CNN architecture, the generative adversarial network (GAN), through which we process seismic migrated images to obtain different kinds of output depending on the application target defined during training. We have developed two proof-of-concept applications. In the first application, a GAN is trained to turn a low-quality migrated image into a high-quality one, as if the acquisition geometry was much more dense than in the input. In the second example, the GAN is trained to turn a migrated image into the respective deconvolved reflectivity image. The effectiveness of the investigated approach is validated by means of tests performed on synthetic examples.

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