Deep Learning Model for Digital Sales Increasing and Forecasting: Towards Smart E-Commerce

2021 ◽  
pp. 26-34
Author(s):  
admin admin ◽  
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Programming Languages ◽  
Digital Marketing ◽  
Deep Belief Networks ◽  
Marketing Manager ◽  
Management Level ◽  
Effective System ◽  
Deep Learning Model

In this paper, we have proposed a system that will be able to forecast the sales of the e-commerce systems by using the techniques of the deep learning, the main goal of this paper is to help the business and the top management level of the company in decision making in order to provide the workplace the effectiveness and the efficiency in the workplace and to provide an efficient and effective system that it is intelligence to forecast and increase the sales of an e-commerce system, this paper will start with building an e-commerce website using different programming languages which are HTML, CSS, Django, JavaScript Bootstrap, and it this e-commerce website will have a specific database that contains different tables for the product list, the orders, and for the user information and many other tables, then the deep learning algorithms such as Deep Belief Networks and Convolutional Neural Networks will be applied in order to provide an effective system for digital marketing usage, so, it will be able to function as a marketing manager.

scholarly journals Explaining deep neural networks for knowledge discovery in electrocardiogram analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Steven A. Hicks ◽  
Jonas L. Isaksen ◽  
Vajira Thambawita ◽  
Jonas Ghouse ◽  
Gustav Ahlberg ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Clinical Decision Making ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Medical Data ◽  
Medical Doctors ◽  
Medical Tests ◽  
Deep Learning Model

AbstractDeep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.

scholarly journals The Application and Development of Deep Learning in Radiotherapy: A Systematic Review

2021 ◽  
Vol 20 ◽  
pp. 153303382110163
Author(s):  
Danju Huang ◽  
Han Bai ◽  
Li Wang ◽  
Yu Hou ◽  
Lan Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Radiation Oncology ◽  
Clinical Work ◽  
Radiation Oncologists ◽  
Basic Concepts ◽  
Further Development

With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation oncologists with many promising tools that can simplify the complex radiotherapy process in the clinical work of radiation oncology, improve the accuracy and objectivity of diagnosis, and reduce the workload, thus enabling clinicians to spend more time on advanced decision-making tasks. As the development of DL gets closer to clinical practice, radiation oncologists will need to be more familiar with its principles to properly evaluate and use this powerful tool. In this paper, we explain the development and basic concepts of AI and discuss its application in radiation oncology based on different task categories of DL algorithms. This work clarifies the possibility of further development of DL in radiation oncology.

scholarly journals Advanced Deep Learning Model

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Learning Model ◽  
Intelligence Analysis ◽  
Basic Parameters ◽  
Deep Learning Model ◽  
Learning Sets

Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate some of the different deep learning algorithms and methods which can be applied to artificial intelligence analysis, as well as the opportunities provided by the application in various decision making domains.

scholarly journals Neural network activation similarity: a new measure to assist decision making in chemical toxicology

2020 ◽  
Vol 11 (28) ◽  
pp. 7335-7348 ◽  
Author(s):  
Timothy E. H. Allen ◽  
Andrew J. Wedlake ◽  
Elena Gelžinytė ◽  
Charles Gong ◽  
Jonathan M. Goodman ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Test Data ◽  
High Performance ◽  
Chemical Binding ◽  
Biological Targets ◽  
Network Activation ◽  
Chemical Toxicology

Deep learning neural networks, constructed for the prediction of chemical binding at 79 pharmacologically important human biological targets, show extremely high performance on test data (accuracy 92.2 ± 4.2%, MCC 0.814 ± 0.093, ROC-AUC 0.96 ± 0.04).

scholarly journals Multimodal Multi-tasking for Skin Lesion Classification Using Deep Neural Networks

2021 ◽  
pp. 27-38
Author(s):  
Rafaela Carvalho ◽  
João Pedrosa ◽  
Tudor Nedelcu
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Skin Lesion ◽  
Deep Neural Networks ◽  
Classification Performance ◽  
Additional Information ◽  
Deep Learning Model ◽  
Lesion Classification

AbstractSkin cancer is one of the most common types of cancer and, with its increasing incidence, accurate early diagnosis is crucial to improve prognosis of patients. In the process of visual inspection, dermatologists follow specific dermoscopic algorithms and identify important features to provide a diagnosis. This process can be automated as such characteristics can be extracted by computer vision techniques. Although deep neural networks can extract useful features from digital images for skin lesion classification, performance can be improved by providing additional information. The extracted pseudo-features can be used as input (multimodal) or output (multi-tasking) to train a robust deep learning model. This work investigates the multimodal and multi-tasking techniques for more efficient training, given the single optimization of several related tasks in the latter, and generation of better diagnosis predictions. Additionally, the role of lesion segmentation is also studied. Results show that multi-tasking improves learning of beneficial features which lead to better predictions, and pseudo-features inspired by the ABCD rule provide readily available helpful information about the skin lesion.

Effectiveness of a Hybrid Deep Learning Model Integrated with a Hybrid Parameterisation Model in Decision-Making Analysis

2020 ◽  
Author(s):  
Masurah Mohamad ◽  
Ali Selamat
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Missing Data ◽  
Machine Learning Technique ◽  
Processing Times ◽  
Learning Technique ◽  
Neural Network Algorithm ◽  
Deep Learning Model

Deep learning has recently gained the attention of many researchers in various fields. A new and emerging machine learning technique, it is derived from a neural network algorithm capable of analysing unstructured datasets without supervision. This study compared the effectiveness of the deep learning (DL) model vs. a hybrid deep learning (HDL) model integrated with a hybrid parameterisation model in handling complex and missing medical datasets as well as their performance in increasing classification. The results showed that 1) the DL model performed better on its own, 2) DL was able to analyse complex medical datasets even with missing data values, and 3) HDL performed well as well and had faster processing times since it was integrated with a hybrid parameterisation model.

scholarly journals A Deep Convolutional Neural Network Architecture for Cancer Diagnosis using Histopathological Images

2021 ◽  
Vol 10 (12) ◽  
pp. 7-12
Author(s):  
Karthika Gidijala ◽  
◽  
Mansa Devi Pappu ◽  
Manasa Vavilapalli ◽  
Mahesh Kothuru ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Proposed Model ◽  
Histopathological Images

Many different models of Convolution Neural Networks exist in the Deep Learning studies. The application and prudence of the algorithms is known only when they are implemented with strong datasets. The histopathological images of breast cancer are considered as to have much number of haphazard structures and textures. Dealing with such images is a challenging issue in deep learning. Working on wet labs and in coherence to the results many research have blogged with novel annotations in the research. In this paper, we are presenting a model that can work efficiently on the raw images with different resolutions and alleviating with the problems of the presence of the structures and textures. The proposed model achieves considerably good results useful for decision making in cancer diagnosis.

scholarly journals Graph Transformer for drug response prediction

2021 ◽  
Author(s):  
Tuan Thanh Nguyen ◽  
Thang Chu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Drug Response ◽  
State Of The Art ◽  
Response Prediction ◽  
Graph Representation ◽  
Omics Data ◽  
Information Redundancy ◽  
Fully Connected ◽  
Deep Learning Model

Previous models have shown that learning drug features from their graph representation is more efficient than learning from their strings or numeric representations. Furthermore, integrating multi-omics data of cell lines increases the performance of drug response prediction. However, these models showed drawbacks in extracting drug features from graph representation and incorporating redundancy information from multi-omics data. This paper proposes a deep learning model, GraTransDRP, to better drug representation and reduce information redundancy. First, the Graph transformer was utilized to extract the drug representation more efficiently. Next, Convolutional neural networks were used to learn the mutation, meth, and transcriptomics features. However, the dimension of transcriptomics features is up to 17737. Therefore, KernelPCA was applied to transcriptomics features to reduce the dimension and transform them into a dense presentation before putting them through the CNN model. Finally, drug and omics features were combined to predict a response value by a fully connected network. Experimental results show that our model outperforms some state-of-the-art methods, including GraphDRP, GraOmicDRP.

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