Research on Practical Intelligent Mode of Digital Image Economy Based on Improved Genetic Multilayer Neural Network

In the context of economic globalization and digitization, the current financial field is in an unprecedented complex situation. The methods and means to deal with this complexity are developing towards image intelligence. This paper takes financial prediction as the starting point, selects the artificial neural network in the intelligent algorithm and optimizes the algorithm, forecasts through the improved multilayer neural network, and compares it with the traditional neural network. Through comparison, it is found that the prediction success rate of the improved genetic multilayer neural network increases with the increase of the dimension of the input image data. This shows that, by adding more technical indicators as the input of the combined network, the prediction efficiency of the improved genetic multilayer neural network can be further improved and the advantage of computing speed can be maintained.

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Detection of Neurons in the Proteomic and Genomic Image Data

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d4524.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 11151-11157

Keyword(s):

Neural Network ◽

Human Body ◽

Sensory System ◽

Image Data ◽

Input Image ◽

The Body ◽

Basic Unit ◽

Biomedical Data ◽

Information Flows ◽

Network Method

Nowadays, the major biomedical data required for diagnosing the disease is neurons in the nerve cell. Just a brief timeframe after the neuron became recognized as the basic unit of the sensory system, the main endeavors were made to appraise the quantity of neurons in various parts of the sensory system. During the previous century, an incredible number of techniques have been utilized in making such gauges. In spite of the fact that the most generally utilized and acknowledged strategy is that of direct including in the magnifying lens, different systems, including photographic, projection, homogenate, programmed, and visual strategies have been planned. And in this project we are taking a brain tissue as an image data and from that image we are finding the number of neurons which are active in state for the first 24 hrs. and again check for 48 hrs. and finally for 72 hrs. so we here find how neurons are responding after giving information to a body and that information flows through nerves of the body and reaches to the neurons present in a human brain and the neurons react to the information and we take the data that how many neurons are responding to the information that is given to a human body. So, by finding the number of neurons responding to the information given to human body we could estimate the neurons which are alive, and which are dead by this we could declare the mental status of a person. So we are finding the number of neurons with the help of neural network method using MATLAB software and we created a page with the help of MATLAB so we can give input image in the page and the code we written will help to check the number of neurons.

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CNN-Based Illumination Estimation with Semantic Information

Applied Sciences ◽

10.3390/app10144806 ◽

2020 ◽

Vol 10 (14) ◽

pp. 4806 ◽

Cited By ~ 1

Author(s):

Ho-Hyoung Choi ◽

Hyun-Soo Kang ◽

Byoung-Ju Yun

Keyword(s):

Neural Network ◽

Computer Vision ◽

Semantic Information ◽

Color Constancy ◽

Noisy Data ◽

Image Data ◽

Input Image ◽

The Novel ◽

Image Patches ◽

Wide Range

For more than a decade, both academia and industry have focused attention on the computer vision and in particular the computational color constancy (CVCC). The CVCC is used as a fundamental preprocessing task in a wide range of computer vision applications. While our human visual system (HVS) has the innate ability to perceive constant surface colors of objects under varying illumination spectra, the computer vision is facing the color constancy challenge in nature. Accordingly, this article proposes novel convolutional neural network (CNN) architecture based on the residual neural network which consists of pre-activation, atrous or dilated convolution and batch normalization. The proposed network can automatically decide what to learn from input image data and how to pool without supervision. When receiving input image data, the proposed network crops each image into image patches prior to training. Once the network begins learning, local semantic information is automatically extracted from the image patches and fed to its novel pooling layer. As a result of the semantic pooling, a weighted map or a mask is generated. Simultaneously, the extracted information is estimated and combined to form global information during training. The use of the novel pooling layer enables the proposed network to distinguish between useful data and noisy data, and thus efficiently remove noisy data during learning and evaluating. The main contribution of the proposed network is taking CVCC to higher accuracy and efficiency by adopting the novel pooling method. The experimental results demonstrate that the proposed network outperforms its conventional counterparts in estimation accuracy.

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Penerapan Convolutional Neural Network Deep Learning dalam Pendeteksian Citra Biji Jagung Kering

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v5i2.3040 ◽

2021 ◽

Vol 5 (2) ◽

pp. 265-271

Author(s):

Arum TiaraSari ◽

Emy Haryatmi

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Image Data ◽

Input Image ◽

Training Dataset ◽

Corn Kernel ◽

Testing Data ◽

Corn Kernels ◽

Industry Area

Corn kernels detection can be implemented in industry area. This can be implemented in the selection and packaging the corn kernels before it is distributed. This technique can be implemented in the selection and packaging machine to detect corn kernels accurately. Corn kernel images was used before it is implemented in real-time. The objective of this research was corn kernel detection using Convolutional Neural Network (CNN) deep learning. This technique consists of 3 main stages, the first preprocessing or normalizing the input of corn kernels image data by wrapping and cropping, both modeling and training the system, and testing. The experiment used CNN method to classify images of dry corn kernels and to determine the accuracy value. This research used 20 dry corn kernels images as testing from 80 dry corn kernels images which used in training dataset. The accuracy of detection was dependent from the size of image and position when the image was taken. The accuracy is around 80% - 100% by using 7 convolutional layers and the average of accuracy for testing data was 0,90296. The convolutional layer which implemented in CNN has the strength to detect features in the input image.

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CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i03.5643 ◽

2020 ◽

Vol 34 (03) ◽

pp. 2594-2601

Author(s):

Arjun Akula ◽

Shuai Wang ◽

Song-Chun Zhu

Keyword(s):

Neural Network ◽

State Of The Art ◽

Input Image ◽

Classification Model ◽

Learning Models ◽

Fault Line ◽

Semantic Level ◽

Explainable Ai ◽

Fault Lines ◽

Classification Category

We present CoCoX (short for Conceptual and Counterfactual Explanations), a model for explaining decisions made by a deep convolutional neural network (CNN). In Cognitive Psychology, the factors (or semantic-level features) that humans zoom in on when they imagine an alternative to a model prediction are often referred to as fault-lines. Motivated by this, our CoCoX model explains decisions made by a CNN using fault-lines. Specifically, given an input image I for which a CNN classification model M predicts class cpred, our fault-line based explanation identifies the minimal semantic-level features (e.g., stripes on zebra, pointed ears of dog), referred to as explainable concepts, that need to be added to or deleted from I in order to alter the classification category of I by M to another specified class calt. We argue that, due to the conceptual and counterfactual nature of fault-lines, our CoCoX explanations are practical and more natural for both expert and non-expert users to understand the internal workings of complex deep learning models. Extensive quantitative and qualitative experiments verify our hypotheses, showing that CoCoX significantly outperforms the state-of-the-art explainable AI models. Our implementation is available at https://github.com/arjunakula/CoCoX

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The European Bioinformatics Institute in 2020: building a global infrastructure of interconnected data resources for the life sciences

Nucleic Acids Research ◽

10.1093/nar/gkz1033 ◽

2019 ◽

Vol 48 (D1) ◽

pp. D17-D23 ◽

Cited By ~ 8

Author(s):

Charles E Cook ◽

Oana Stroe ◽

Guy Cochrane ◽

Ewan Birney ◽

Rolf Apweiler

Keyword(s):

Data Exchange ◽

Life Sciences ◽

Image Data ◽

European Bioinformatics Institute ◽

Added Value ◽

Value Analysis ◽

Starting Point ◽

Core Mission ◽

Training Team ◽

Exchange Data

Abstract Data resources at the European Bioinformatics Institute (EMBL-EBI, https://www.ebi.ac.uk/) archive, organize and provide added-value analysis of research data produced around the world. This year's update for EMBL-EBI focuses on data exchanges among resources, both within the institute and with a wider global infrastructure. Within EMBL-EBI, data resources exchange data through a rich network of data flows mediated by automated systems. This network ensures that users are served with as much information as possible from any search and any starting point within EMBL-EBI’s websites. EMBL-EBI data resources also exchange data with hundreds of other data resources worldwide and collectively are a key component of a global infrastructure of interconnected life sciences data resources. We also describe the BioImage Archive, a deposition database for raw images derived from primary research that will supply data for future knowledgebases that will add value through curation of primary image data. We also report a new release of the PRIDE database with an improved technical infrastructure, a new API, a new webpage, and improved data exchange with UniProt and Expression Atlas. Training is a core mission of EMBL-EBI and in 2018 our training team served more users, both in-person and through web-based programmes, than ever before.

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Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2018-0213 ◽

2020 ◽

Vol 65 (6) ◽

pp. 759-773

Author(s):

Segu Praveena ◽

Sohan Pal Singh

Keyword(s):

Neural Network ◽

Acute Lymphoblastic Leukaemia ◽

Convolutional Neural Network ◽

Optimization Algorithm ◽

Lymphoblastic Leukaemia ◽

Input Image ◽

Deep Convolutional Neural Network ◽

Grey Wolf ◽

Deep Cnn

AbstractLeukaemia detection and diagnosis in advance is the trending topic in the medical applications for reducing the death toll of patients with acute lymphoblastic leukaemia (ALL). For the detection of ALL, it is essential to analyse the white blood cells (WBCs) for which the blood smear images are employed. This paper proposes a new technique for the segmentation and classification of the acute lymphoblastic leukaemia. The proposed method of automatic leukaemia detection is based on the Deep Convolutional Neural Network (Deep CNN) that is trained using an optimization algorithm, named Grey wolf-based Jaya Optimization Algorithm (GreyJOA), which is developed using the Grey Wolf Optimizer (GWO) and Jaya Optimization Algorithm (JOA) that improves the global convergence. Initially, the input image is applied to pre-processing and the segmentation is performed using the Sparse Fuzzy C-Means (Sparse FCM) clustering algorithm. Then, the features, such as Local Directional Patterns (LDP) and colour histogram-based features, are extracted from the segments of the pre-processed input image. Finally, the extracted features are applied to the Deep CNN for the classification. The experimentation evaluation of the method using the images of the ALL IDB2 database reveals that the proposed method acquired a maximal accuracy, sensitivity, and specificity of 0.9350, 0.9528, and 0.9389, respectively.

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The International Radiomics Platform – An Initiative of the German and Austrian Radiological Societies

RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren ◽

10.1055/a-1244-2775 ◽

2020 ◽

Author(s):

Daniel Overhoff ◽

Peter Kohlmann ◽

Alex Frydrychowicz ◽

Sergios Gatidis ◽

Christian Loewe ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Image Data ◽

Quality Parameters ◽

Image Features ◽

Performance Criteria ◽

Data Sets ◽

Proof Of Concept ◽

Concept Study ◽

Artificial Intelligence Methods

Purpose The DRG-ÖRG IRP (Deutsche Röntgengesellschaft-Österreichische Röntgengesellschaft international radiomics platform) represents a web-/cloud-based radiomics platform based on a public-private partnership. It offers the possibility of data sharing, annotation, validation and certification in the field of artificial intelligence, radiomics analysis, and integrated diagnostics. In a first proof-of-concept study, automated myocardial segmentation and automated myocardial late gadolinum enhancement (LGE) detection using radiomic image features will be evaluated for myocarditis data sets. Materials and Methods The DRG-ÖRP IRP can be used to create quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis and is characterized by the following performance criteria: Possibility of using multicentric networked data, automatically calculated quality parameters, processing of annotation tasks, contour recognition using conventional and artificial intelligence methods and the possibility of targeted integration of algorithms. In a first study, a neural network pre-trained using cardiac CINE data sets was evaluated for segmentation of PSIR data sets. In a second step, radiomic features were applied for segmental detection of LGE of the same data sets, which were provided multicenter via the IRP. Results First results show the advantages (data transparency, reliability, broad involvement of all members, continuous evolution as well as validation and certification) of this platform-based approach. In the proof-of-concept study, the neural network demonstrated a Dice coefficient of 0.813 compared to the expert's segmentation of the myocardium. In the segment-based myocardial LGE detection, the AUC was 0.73 and 0.79 after exclusion of segments with uncertain annotation.The evaluation and provision of the data takes place at the IRP, taking into account the FAT (fairness, accountability, transparency) and FAIR (findable, accessible, interoperable, reusable) criteria. Conclusion It could be shown that the DRG-ÖRP IRP can be used as a crystallization point for the generation of further individual and joint projects. The execution of quantitative analyses with artificial intelligence methods is greatly facilitated by the platform approach of the DRG-ÖRP IRP, since pre-trained neural networks can be integrated and scientific groups can be networked.In a first proof-of-concept study on automated segmentation of the myocardium and automated myocardial LGE detection, these advantages were successfully applied.Our study shows that with the DRG-ÖRP IRP, strategic goals can be implemented in an interdisciplinary way, that concrete proof-of-concept examples can be demonstrated, and that a large number of individual and joint projects can be realized in a participatory way involving all groups. Key Points: Citation Format

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Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽

10.3390/e23070816 ◽

2021 ◽

Vol 23 (7) ◽

pp. 816

Author(s):

Pingping Liu ◽

Xiaokang Yang ◽

Baixin Jin ◽

Qiuzhan Zhou

Keyword(s):

Neural Network ◽

Image Processing ◽

Diabetic Retinopathy ◽

Convolutional Neural Network ◽

Image Classification ◽

Network Model ◽

Rapid Development ◽

Image Data ◽

Lesion Detection ◽

Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

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