scholarly journals Prediction of Crop and Weed Growth Stages using Neural Network in Machine Learning

2020 ◽  
Vol 9 (5) ◽  
pp. 665-671
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural System ◽  
Growth Stages ◽  
Phenological Stages ◽  
Making Sense ◽  
Weed Growth ◽  
Wide Range ◽  
Future Work ◽  
Phenological Phases

This paper presents the structure, usage and assessment of a convolutional neural system-based methodology has been applied for the arrangement of various phenological phases of plants. Our CNN design can consequently characterize distinctive phenological phases of eleven sorts of plants. So as to assess the presentation and productivity of our profound learning-based methodology, an old-style AI approach dependent on physically removed highlights is additionally executed. Textural highlights dependent on GLCM highlights have been removed and joined arrangement of highlights are taken care of into an AI calculation. The arrangement pace of the methodology dependent on physically extricated highlights is contrasted with those of our CNN based methodology. Trial results demonstrate that CNN put together methodology is fundamentally powerful with respect to the eleven sorts of plants we investigated. There are a wide range of ways profound learning can be applied on a dataset relying upon the size of the dataset. There are many research headings that we are intending to take for arranging phenological stages. Future work may comprise of building our own CNN design without any preparation especially prepared for arranging phenological phases of plants, just as trying different things with other pre-prepared CNN models and making sense of an approach to recognize infections in crops.

scholarly journals COMPARATIVE STUDY OF CAPSULE NEURAL NETWORK IN VARIOUS APPLICATIONS

2019 ◽  
Vol 01 (01) ◽  
pp. 19-27 ◽  
Author(s):  
Vijayakumar T
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Comparative Study ◽  
Computer Graphic ◽  
Spatial Location ◽  
Neural System ◽  
Learning System ◽  
The Comparative Study ◽  
Inverse Operation

The advancement in the machine learning and the computer vision has caused several improvements and development in numerous of domains. Capsule neural networks are one such machine learning system that imitates the neural system and develops the structures based on the hierarchical relationships. It does the inverse operation of the computer graphic in representing an object by, segregating the object in the image into different part and viewing the in-existing relationship between the each parts to represent in order to preserve even the minute details related to the object, unlike CNN that losses major of the information’s related to the spatial location of the object that are essential in the segmentation and the detection. So the paper presents the comparative study of the capsule neural network in various application, presenting the efficiency of the capsules networks over the convolutional neural networks.

scholarly journals Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Thomas Martynec ◽  
Christos Karapanagiotis ◽  
Sabine H. L. Klapp ◽  
Stefan Kowarik
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Monte Carlo ◽  
Kinetic Monte Carlo ◽  
Diffusion Barriers ◽  
Binding Energies ◽  
Atomic Scale ◽  
Training Data ◽  
Data Set ◽  
Wide Range

AbstractMachine learning is playing an increasing role in the discovery of new materials and may also facilitate the search for optimum growth conditions for crystals and thin films. Here, we perform kinetic Monte-Carlo simulations of sub-monolayer growth. We consider a generic homoepitaxial growth scenario that covers a wide range of conditions with different diffusion barriers (0.4–0.55 eV) and lateral binding energies (0.1–0.4 eV). These simulations are used as a training data set for a convolutional neural network that can predict diffusion barriers and binding energies. Specifically, a single Monte-Carlo image of the morphology is sufficient to determine the energy barriers with an accuracy of approximately 10 meV and the neural network is tolerant to images with noise and lower than atomic-scale resolution. We believe this new machine learning method will be useful for fundamental studies of growth kinetics and growth optimization through better knowledge of microscopic parameters.

scholarly journals Classifying Grains Using Behaviour-Informed Machine Learning

2021 ◽  
Author(s):  
Sudip Laudari ◽  
Benjy Marks ◽  
Pierre Rognon
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Granular Materials ◽  
Microfluidic Devices ◽  
Waste Recycling ◽  
Coffee Beans ◽  
Wide Range ◽  
Simple Neural Network ◽  
Grain Properties

Abstract Sorting granular materials such as ores, coffee beans, cereals, gravels and pills is essential forapplications in mineral processing, agriculture and waste recycling. Existing sorting methods are based on the detection of contrast in grain properties including size, colour, density and chemical composition. However, many grain properties cannot be directly detected in-situ, which significantly impairs sorting efficacy. We show here that a simple neural network can infer contrast in a wide range of grain properties by detecting patterns in their observable kinematics. These properties include grain size, density, stiffness, friction, dissipation and adhesion. This method of classification based on behaviour can significantly widen the range of granular materials that can be sorted. It can similarly be applied to enhance the sorting of other particulate materials including cells and droplets in microfluidic devices.

scholarly journals A Novel Machine Learning Model to Predict the Photo-Degradation Performance of Different Photocatalysts on a Variety of Water Contaminants

Catalysts ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1107
Author(s):  
Zhuoying Jiang ◽  
Jiajie Hu ◽  
Matthew Tong ◽  
Anna C. Samia ◽  
Huichun (Judy) Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Metal Oxide ◽  
Photocatalytic Degradation ◽  
Molecular Structures ◽  
Good Prediction ◽  
Ann Model ◽  
Water Contaminants ◽  
Molecular Fingerprints ◽  
Wide Range

This paper describes an innovative machine learning (ML) model to predict the performance of different metal oxide photocatalysts on a wide range of contaminants. The molecular structures of metal oxide photocatalysts are encoded with a crystal graph convolution neural network (CGCNN). The structure of organic compounds is encoded via digital molecular fingerprints (MF). The encoded features of the photocatalysts and contaminants are input to an artificial neural network (ANN), named as CGCNN-MF-ANN model. The CGCNN-MF-ANN model has achieved a very good prediction of the photocatalytic degradation rate constants by different photocatalysts over a wide range of organic contaminants. The effects of the data training strategy on the ML model performance are compared. The effects of different factors on photocatalytic degradation performance are further evaluated by feature importance analyses. Examples are illustrated on the use of this novel ML model for optimal photocatalyst selection and for assessing other types of photocatalysts for different environmental applications.

scholarly journals Competitive Coevolution through Evolutionary Complexification

2004 ◽  
Vol 21 ◽  
pp. 63-100 ◽  
Author(s):  
K. O. Stanley ◽  
R. Miikkulainen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Arms Race ◽  
Network Architectures ◽  
Complex Problems ◽  
Wide Range ◽  
Competitive Coevolution ◽  
Neural Network Architectures ◽  
Complex Solutions ◽  
Fixed Structure

Two major goals in machine learning are the discovery and improvement of solutions to complex problems. In this paper, we argue that complexification, i.e. the incremental elaboration of solutions through adding new structure, achieves both these goals. We demonstrate the power of complexification through the NeuroEvolution of Augmenting Topologies (NEAT) method, which evolves increasingly complex neural network architectures. NEAT is applied to an open-ended coevolutionary robot duel domain where robot controllers compete head to head. Because the robot duel domain supports a wide range of strategies, and because coevolution benefits from an escalating arms race, it serves as a suitable testbed for studying complexification. When compared to the evolution of networks with fixed structure, complexifying evolution discovers significantly more sophisticated strategies. The results suggest that in order to discover and improve complex solutions, evolution, and search in general, should be allowed to complexify as well as optimize.

scholarly journals Anti-CRISPR RNAs: designing universal riboregulators with deep learning of Csy4-mediated RNA processing

2020 ◽  
Author(s):  
Haotian Guo ◽  
Xiaohu Song ◽  
Ariel B. Lindner
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Rna Processing ◽  
Binding Sites ◽  
Transcriptional Networks ◽  
Promising Alternative ◽  
Wide Range ◽  
Flanking Sequences ◽  
Structural Commonality

AbstractRNA-based regulation offers a promising alternative of protein-based transcriptional networks. However, designing synthetic riboregulators with desirable functionalities using arbitrary sequences remains challenging, due in part to insufficient exploration of RNA sequence-to-function landscapes. Here we report that CRISPR-Csy4 mediates a nearly all-or-none processing of precursor CRISPR RNAs (pre-crRNAs), by profiling Csy4 binding sites flanked by > 1 million random sequences. This represents an ideal sequence-to-function space for universal riboregulator designs. Lacking discernible sequence-structural commonality among processable pre-crRNAs, we trained a neural network for accurate classification (f1-score ≈ 0.93). Inspired by exhaustive probing of palindromic flanking sequences, we designed anti-CRISPR RNAs (acrRNAs) that suppress processing of pre-crRNAs via stem stacking. We validated machine-learning-guided designs with >30 functional pairs of acrRNAs and pre-crRNAs to achieve switch-like properties. This opens a wide range of plug-and-play applications tailored through pre-crRNA designs, and represents a programmable alternative to protein-based anti-CRISPRs.

Prediction of Rainfall using Machine Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 1374-1377
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Cloud Cover ◽  
Feed Forward Neural Network ◽  
Proposed Model ◽  
Wide Range ◽  
New Methodologies ◽  
Complex Relationships ◽  
Artificial Neural Network Ann

Rainfall is one of the major livelihood of this world. Each and every organism in this universe need some of water to order to survive in its own living conditions. As rainfall is the main source of water and its need to agriculture is inevitable, there arises a necessity to analyze the pattern of the rainfall. The main aim of our paper is to predict the rainfall considering various factors like temperature, pressure, cloud cover, wind speed, pollution and precipitation. There are various ideas and new methodologies proposed in order to predict rainfall. But our proposed concept is based on machine learning because of its wide range of development and preferability nowadays. Among the various technologies built in Machine Learning (ML), Feed Forward Neural Network (FFNN) which is the simplest form of Artificial Neural Network (ANN) is preferred because this model learns the complex relationships among the various input parameters and helps to model them easily. Rainfall in our proposed model is predicted using different parameters influencing the rainfall along with their combinations and patterns. The experimental results depicts that the proposed model based on FFNN indicates suitable accuracy.

scholarly journals Hybrid artificial neural network and structural equation modelling techniques: a survey

2021 ◽  
Author(s):  
A. S. Albahri ◽  
Alhamzah Alnoor ◽  
A. A. Zaidan ◽  
O. S. Albahri ◽  
Hamsa Hameed ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Structural Equation Modelling ◽  
Structural Equation ◽  
Healthcare Sector ◽  
Autistic Children ◽  
Equation Modelling ◽  
Wide Range ◽  
Artificial Neural ◽  
Future Work

AbstractTopical treatments with structural equation modelling (SEM) and an artificial neural network (ANN), including a wide range of concepts, benefits, challenges and anxieties, have emerged in various fields and are becoming increasingly important. Although SEM can determine relationships amongst unobserved constructs (i.e. independent, mediator, moderator, control and dependent variables), it is insufficient for providing non-compensatory relationships amongst constructs. In contrast with previous studies, a newly proposed methodology that involves a dual-stage analysis of SEM and ANN was performed to provide linear and non-compensatory relationships amongst constructs. Consequently, numerous distinct types of studies in diverse sectors have conducted hybrid SEM–ANN analysis. Accordingly, the current work supplements the academic literature with a systematic review that includes all major SEM–ANN techniques used in 11 industries published in the past 6 years. This study presents a state-of-the-art SEM–ANN classification taxonomy based on industries and compares the effort in various domains to that classification. To achieve this objective, we examined the Web of Science, ScienceDirect, Scopus and IEEE Xplore® databases to retrieve 239 articles from 2016 to 2021. The obtained articles were filtered on the basis of inclusion criteria, and 60 studies were selected and classified under 11 categories. This multi-field systematic study uncovered new research possibilities, motivations, challenges, limitations and recommendations that must be addressed for the synergistic integration of multidisciplinary studies. It contributed two points of potential future work resulting from the developed taxonomy. First, the importance of the determinants of play, musical and art therapy adoption amongst autistic children within the healthcare sector is the most important consideration for future investigations. In this context, the second potential future work can use SEM–ANN to determine the barriers to adopting sensing-enhanced therapy amongst autistic children to satisfy the recommendations provided by the healthcare sector. The analysis indicates that the manufacturing and technology sectors have conducted the most number of investigations, whereas the construction and small- and medium-sized enterprise sectors have conducted the least. This study will provide a helpful reference to academics and practitioners by providing guidance and insightful knowledge for future studies.

scholarly journals Large-scale design and refinement of stable proteins using sequence-only models

2021 ◽  
Author(s):  
Jedediah M. Singer ◽  
Scott Novotney ◽  
Devin Strickland ◽  
Hugh K. Haddox ◽  
Nicholas Leiby ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Large Scale ◽  
Amino Acid Sequences ◽  
Novel Proteins ◽  
Wide Range ◽  
The Stability ◽  
Scale Design ◽  
Future Work

AbstractEngineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we report a neural network model that predicts protein stability based only on sequences of amino acids, and demonstrate its performance by evaluating the stability of almost 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We also report a second neural network model that is able to generate novel stable proteins. Finally, we show that the predictive model can be used to substantially increase the stability of both expert-designed and model-generated proteins.

Accurate prediction of grain boundary structures and energetics in CdTe: A machine-learning potential approach

2021 ◽  
Author(s):  
Tatsuya Yokoi ◽  
Kosuke Adachi ◽  
Sayuri Iwase ◽  
Katsuyuki Matsunaga
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Grain Boundary ◽  
Interatomic Potential ◽  
Learning Potential ◽  
Potential Approach ◽  
Atomic Structures ◽  
Wide Range ◽  
Artificial Neural Network Ann

To accurately predict grain boundary (GB) atomic structures and their energetics in CdTe, the present study constructs an artificial-neural-network (ANN) interatomic potential. To cover a wide range of atomic environments,...

Sign in / Sign up

Export Citation Format

Share Document