scholarly journals Research on Management Model Based on Deep Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yuting Zhao
Keyword(s):  
Deep Learning ◽  
Training System ◽  
Management Model ◽  
Good Prediction ◽  
Complex Data ◽  
Mean Relative Error ◽  
Management Models ◽  
Future Prediction ◽  
Reduce Risk ◽  
The Cost

In this study, the focus was on the development of management models and future prediction for the cost and risk by using an improved deep learning (DL) algorithm. Management model can be defined as the management activities that are interlinked and organized inside organization of institutions. Different opportunities and different organizations are offered by different management models. Proper management models lead to strategies and decisions help to success organization. Deep neural network (DNN) is proposed to make good prediction for organization for increasing the cost and reduce risk in companies and institutions. The error of prediction is updated according to variable hidden layers and nodes within iteration. Improved DNN is used and modify weights that have an effect on the features extracted in advance to increase the accuracy and precisions are used. The proposed method is based on dynamic hidden layers with backpropagation and feedforward. Absolute mean relative error (AMRE) and variance (R2) are used for evaluation in term of accuracy. The training system is used with three available datasets from big company, health issue, and industry. Gained result proves the worth of the proposed system and is suitable for predicting complex data and reducing the risk as possible.

scholarly journals Deep Learning-Based Prediction of Throttle Value and State for Wheel Loaders

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7202
Author(s):  
Jianfei Huang ◽  
Xinchun Cheng ◽  
Yuying Shen ◽  
Dewen Kong ◽  
Jixin Wang
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Short Term Memory ◽  
Prediction Method ◽  
Good Prediction ◽  
Construction Machinery ◽  
Autonomous Operation ◽  
Good Prediction Accuracy ◽  
Wheel Loaders ◽  
The Cost

Accurate prediction of the throttle value and state for wheel loaders can help to achieve autonomous operation, thereby reducing the cost and accident rate. However, existing methods based on a physical model cannot accurately reflect the operator’s driving habits and the interaction between wheel loaders and the environment. In this paper, a deep-learning-based prediction model is developed to predict the throttle value and state for wheel loaders by learning from driving data. Multiple long–short-term memory (LSTM) networks are used to extract the temporal features of different stages during the operation of the wheel loader. Two backward-propagation neural networks (BPNNs), which use the temporal feature extracted by LSTM as the input, are designed to output the final prediction results of throttle value and state, respectively. The proposed prediction model is trained and tested using the data from two different conditions. The end-to-end LSTM prediction model and BPNNs are used as benchmark models. The results indicate that the proposed prediction model has good prediction accuracy and adaptability. Furthermore, the relationship between the prediction performance and signal sampling frequency is also studied. The proposed prediction method that combines driving data and deep learning can make the throttle action conform to the decisions of an experienced operator, providing technical support for the autonomous operation of construction machinery.

Janus – A New Approach to Air Combat Pilot Training

2019 ◽  
Vol 2019 (4) ◽  
pp. 7-22
Author(s):  
Georges Bridel ◽  
Zdobyslaw Goraj ◽  
Lukasz Kiszkowiak ◽  
Jean-Georges Brévot ◽  
Jean-Pierre Devaux ◽  
...  
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
High Energy ◽  
Training System ◽  
Embedded Sensors ◽  
Pilot Training ◽  
New Approach ◽  
Combat Aircraft ◽  
Air Combat ◽  
The Cost

Abstract Advanced jet training still relies on old concepts and solutions that are no longer efficient when considering the current and forthcoming changes in air combat. The cost of those old solutions to develop and maintain combat pilot skills are important, adding even more constraints to the training limitations. The requirement of having a trainer aircraft able to perform also light combat aircraft operational mission is adding unnecessary complexity and cost without any real operational advantages to air combat mission training. Thanks to emerging technologies, the JANUS project will study the feasibility of a brand-new concept of agile manoeuvrable training aircraft and an integrated training system, able to provide a live, virtual and constructive environment. The JANUS concept is based on a lightweight, low-cost, high energy aircraft associated to a ground based Integrated Training System providing simulated and emulated signals, simulated and real opponents, combined with real-time feedback on pilot’s physiological characteristics: traditionally embedded sensors are replaced with emulated signals, simulated opponents are proposed to the pilot, enabling out of sight engagement. JANUS is also providing new cost effective and more realistic solutions for “Red air aircraft” missions, organised in so-called “Aggressor Squadrons”.

Deep Learning in the Study of Protein-Related Interactions

2020 ◽  
Vol 27 (5) ◽  
pp. 359-369 ◽  
Author(s):  
Cheng Shi ◽  
Jiaxing Chen ◽  
Xinyue Kang ◽  
Guiling Zhao ◽  
Xingzhen Lao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Physiological Data ◽  
Great Promise ◽  
Complex Data ◽  
Protein Protein Interactions ◽  
Learning Patterns ◽  
Introductory Overview ◽  
Protein Research ◽  
Neural Network Theory

: Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated interactions have always been costly and inefficient. In recent years, advances in biological and medical technology have provided us with explosive biological and physiological data, and deep learning-based algorithms have shown great promise in extracting features and learning patterns from complex data. At present, deep learning in protein research has emerged. In this review, we provide an introductory overview of the deep neural network theory and its unique properties. Mainly focused on the application of this technology in protein-related interactions prediction over the past five years, including protein-protein interactions prediction, protein-RNA\DNA, Protein– drug interactions prediction, and others. Finally, we discuss some of the challenges that deep learning currently faces.

scholarly journals Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4736
Author(s):  
Sk. Tanzir Mehedi ◽  
Adnan Anwar ◽  
Ziaur Rahman ◽  
Kawsar Ahmed
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Transfer Learning ◽  
Security Requirements ◽  
Detection Accuracy ◽  
Area Network ◽  
Complex Data ◽  
Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

scholarly journals Automatic Segmentation of Choroid Layer Using Deep Learning on Spectral Domain Optical Coherence Tomography

2021 ◽  
Vol 11 (12) ◽  
pp. 5488
Author(s):  
Wei Ping Hsia ◽  
Siu Lun Tse ◽  
Chia Jen Chang ◽  
Yu Len Huang
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Choroidal Thickness ◽  
Automatic Segmentation ◽  
Average Error ◽  
Good Prediction ◽  
Optical Coherence ◽  
Learning Method ◽  
Subfoveal Choroidal Thickness ◽  
Fully Connected

The purpose of this article is to evaluate the accuracy of the optical coherence tomography (OCT) measurement of choroidal thickness in healthy eyes using a deep-learning method with the Mask R-CNN model. Thirty EDI-OCT of thirty patients were enrolled. A mask region-based convolutional neural network (Mask R-CNN) model composed of deep residual network (ResNet) and feature pyramid networks (FPNs) with standard convolution and fully connected heads for mask and box prediction, respectively, was used to automatically depict the choroid layer. The average choroidal thickness and subfoveal choroidal thickness were measured. The results of this study showed that ResNet 50 layers deep (R50) model and ResNet 101 layers deep (R101). R101 U R50 (OR model) demonstrated the best accuracy with an average error of 4.85 pixels and 4.86 pixels, respectively. The R101 ∩ R50 (AND model) took the least time with an average execution time of 4.6 s. Mask-RCNN models showed a good prediction rate of choroidal layer with accuracy rates of 90% and 89.9% for average choroidal thickness and average subfoveal choroidal thickness, respectively. In conclusion, the deep-learning method using the Mask-RCNN model provides a faster and accurate measurement of choroidal thickness. Comparing with manual delineation, it provides better effectiveness, which is feasible for clinical application and larger scale of research on choroid.

Research on Project Cost Management Model of Ecological Construction

2011 ◽  
Vol 480-481 ◽  
pp. 1197-1200
Author(s):  
Feng Liu ◽  
Jun Min Wang
Keyword(s):  
Project Management ◽  
Energy Consumption ◽  
Construction Projects ◽  
Cost Management ◽  
Building Energy ◽  
Management Model ◽  
Building Energy Consumption ◽  
Specific Cost ◽  
The Cost ◽  
Ecological Construction

In recent years, in order to reduce building energy consumption in China, and vigorously promote the development of ecological construction, the paper studies disadvantages of the cost of traditional project management applications in the ecological construction projects, exploring specific cost management adapt to the ecological construction project.

scholarly journals DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity

2021 ◽  
Vol 15 ◽  
pp. 117793222110303
Author(s):  
Asad Ahmed ◽  
Bhavika Mam ◽  
Ramanathan Sowdhamini
Keyword(s):  
Deep Learning ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Chemical Space ◽  
Biological Significance ◽  
Protein Crystal ◽  
Ligand Docking ◽  
Complex Data ◽  
Binding Prediction ◽  
Data Set

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to “learn” intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes.

Why management models are crucial to the success of business models

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Stephen Denning
Keyword(s):  
20Th Century ◽  
21St Century ◽  
Design Methodology ◽  
Business Models ◽  
Digital Age ◽  
Management Model ◽  
Digital Innovation ◽  
Content Type ◽  
Management Models ◽  
Practical Implications

Purpose The author posits that the management model of an organization determines what kind of business models can be pursued within that organization and that successful 21st century management models are very different from those that succeeded in the 20th century. Design/methodology/approach The author compares and contrasts successful 21st century management models with models that succeeded in the 20th century. Findings Success in the digital age requires a 21st century management model and mindset based on an obsession with delivering value to customers. Practical implications The management model incorporates the key ‘written and unwritten rules’ of the firm. The success of digital innovation can be threatened by 20th Century management assumptions that thwart Agile initiatives. Originality/value Article explains how Agile mindsets and practices are essential to the 21st century management model, and how they potentiate the firm’s focus on creating customers.

scholarly journals Evaluating Counterterrorism Spending

2014 ◽  
Vol 28 (3) ◽  
pp. 237-248 ◽  
Author(s):  
John Mueller ◽  
Mark G. Stewart
Keyword(s):  
Cost Benefit ◽  
September 11 ◽  
Terrorist Attacks ◽  
Security Measures ◽  
Successful Attack ◽  
Security Measure ◽  
Reduce Risk ◽  
The Cost

In this article, we present a simple back-of-the-envelope approach for evaluating whether counterterrorism security measures reduce risk sufficiently to justify their costs. The approach uses only four variables: the consequences of a successful attack, the likelihood of a successful attack, the degree to which the security measure reduces risk, and the cost of the security measure. After measuring the cost of a counterterrorism measure, we explore a range of outcomes for the costs of terrorist attacks and a range of possible estimates for how much risk might be reduced by the measure. Then working from this mix of information and assumptions, we can calculate how many terrorist attacks (and of what size) would need to be averted to justify the cost of the counterterrorism measure in narrow cost–benefit terms. To illustrate this approach, we first apply it to the overall increases in domestic counterterrorism expenditures that have taken place since the terrorist attacks of September 11, 2001, and alternatively we apply it to just the FBI's counterterrorism efforts. We then evaluate evidence on the number and size of terrorist attacks that have actually been averted or might have been averted since 9/11.

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