scholarly journals Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection

2021 ◽  
Vol 11 (23) ◽  
pp. 11268
Author(s):  
Guo-Jhang Hong ◽  
Dong-Lin Li ◽  
Shreya Pare ◽  
Amit Saxena ◽  
Mukesh Prasad ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Support System ◽  
Learning Algorithm ◽  
Local Binary Patterns ◽  
Learning System ◽  
Histogram Of Oriented Gradient ◽  
Proposed Model ◽  
External Model ◽  
On Line

A new online multi-class learning algorithm is proposed with three main characteristics. First, in order to make the feature pool fitter for the pattern pool, the adaptive feature pool is proposed to dynamically combine the three general features, Haar-like, Histogram of Oriented Gradient (HOG), and Local Binary Patterns (LBP). Second, the external model is integrated into the proposed model without re-training to enhance the efficacy of the model. Third, a new multi-class learning and updating mechanism are proposed that help to find unsuitable decisions and adjust them automatically. The performance of the proposed model is validated with multi-class detection and online learning system. The proposed model achieves a better score than other non-deep learning algorithms used in public pedestrian and multi-class databases. The multi-class databases contain data for pedestrians, faces, vehicles, motorcycles, bicycles, and aircraft.

scholarly journals An Investigation into the Application of Deep Learning in the Detection and Mitigation of DDOS Attack on SDN Controllers

Technologies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 14
Author(s):  
James Dzisi Gadze ◽  
Akua Acheampomaa Bamfo-Asante ◽  
Justice Owusu Agyemang ◽  
Henry Nunoo-Mensah ◽  
Kwasi Adu-Boahen Opare
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Single Point ◽  
Denial Of Service ◽  
Specific Work ◽  
Learning Models ◽  
Ddos Attack ◽  
Deep Learning Algorithm ◽  
Proposed Model

Software-Defined Networking (SDN) is a new paradigm that revolutionizes the idea of a software-driven network through the separation of control and data planes. It addresses the problems of traditional network architecture. Nevertheless, this brilliant architecture is exposed to several security threats, e.g., the distributed denial of service (DDoS) attack, which is hard to contain in such software-based networks. The concept of a centralized controller in SDN makes it a single point of attack as well as a single point of failure. In this paper, deep learning-based models, long-short term memory (LSTM) and convolutional neural network (CNN), are investigated. It illustrates their possibility and efficiency in being used in detecting and mitigating DDoS attack. The paper focuses on TCP, UDP, and ICMP flood attacks that target the controller. The performance of the models was evaluated based on the accuracy, recall, and true negative rate. We compared the performance of the deep learning models with classical machine learning models. We further provide details on the time taken to detect and mitigate the attack. Our results show that RNN LSTM is a viable deep learning algorithm that can be applied in the detection and mitigation of DDoS in the SDN controller. Our proposed model produced an accuracy of 89.63%, which outperformed linear-based models such as SVM (86.85%) and Naive Bayes (82.61%). Although KNN, which is a linear-based model, outperformed our proposed model (achieving an accuracy of 99.4%), our proposed model provides a good trade-off between precision and recall, which makes it suitable for DDoS classification. In addition, it was realized that the split ratio of the training and testing datasets can give different results in the performance of a deep learning algorithm used in a specific work. The model achieved the best performance when a split of 70/30 was used in comparison to 80/20 and 60/40 split ratios.

Multi-Criteria Decision Support System for Green Commercial Space Design

2018 ◽  
Vol 43 (4) ◽  
pp. 5-15
Author(s):  
Hao-Cheng Huang ◽  
Yeng-Horng Perng
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Energy Saving ◽  
Support System ◽  
Human Life ◽  
Evaluation Model ◽  
High Energy ◽  
Commercial Space ◽  
Proposed Model ◽  
Commercial Spaces

Commercial space features essential characteristics of attracting clients and creating profits; thus, the exterior and interior designs of conventional commercial space were often made to look grandiose and overdecorated. Over time, according to commercial attributes, operator preferences, and style of the designer, commercial spaces have constantly undergone renovation into varied styles. However, the physical renovation processhas caused multiple and composite types of environmental pollution, such as waste and noise pollution caused by breaking of walls or partitions, anddecorative paint pollution, as well as the use of high-energy-consuming lighting equipment, air-conditioning systems, and decorative materials. Global pollution has caused climate change, endangering living organismsand human life. Furthermore, no effective method exists to control the problem of high greenhouse gas emissions. Therefore, this study used energy-saving design concerns of a garden-type commercial space to propose an energy-saving evaluation model. The study combined three methodologies, the Delphi method, analytic hierarchy process, and fuzzy logic theory, to construct a multi-criteria decision support system for designing green commercial spaces, and used the green spatial design of a garden café as an example to illustrate the high objectivity and adaptability of the proposed model in practical application. The study also used an international award-winning case to validate that the proposed model had practical value as a reference to support key design decisions.

Toward a Decision Support System for Regulation in an Urban Transport Network

2018 ◽  
Vol 9 (2) ◽  
pp. 1-17
Author(s):  
Khadidja Yachba ◽  
Zakaria Bendaoud ◽  
Karim Bouamrane
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Urban Transport ◽  
Transport Network ◽  
Formal Modeling ◽  
Proposed Model ◽  
Available Information ◽  
Decision Support Method ◽  
Multicriteria Decision Support

In this article, a solution to the problem of perturbation in the urban transport network has been proposed. This solution is based on the multicriteria decision support method, which is an efficient way of identifying appropriate solutions to different perturbations situations. The proposed model should provide synthesis, evaluation and updating of available information in order to facilitate the task of the network monitoring operator. To achieve this objective, the authors propose formal modeling of the perturbation concept through an effective and above all significant decision support system exploiting the diversity of the criteria as well as the decision maker's subjectivity. This modeling makes it possible to capitalize the knowledge available within a checkpoint and to monitor the process. The authors show how, from the information available on the network, the modeling of the process represented by a perturbation makes it possible to enrich the possibilities of evaluation of the state of the network.

Development of Neural-Net-Based Decision Support System for Mattress Patterns Using Particle Swarm Optimization

2010 ◽  
Vol 4 (2) ◽  
pp. 178-183 ◽  
Author(s):  
Mitsue Kato ◽  
◽  
Toru Yamamoto ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Optimization Problems ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Neural Net ◽  
Swarm Optimization ◽  
Neural Network Learning

The particle swarm optimization (PSO) concept simulating a simplified social milieu, is optimization useful for solving nonconvex continuous optimization problems. We discuss a new learning algorithm for simultaneously adjusting connection weights in neural networks and user-specified parameters included in units. Based on our algorithm, neural network learning, e.g., learning cost or adaptability, can be improved, as demonstrated in mattress decision support system.

The Design and Implementation of Condition Monitoring and Decision Support System in HVDC Converter Station

2015 ◽  
Vol 734 ◽  
pp. 707-713
Author(s):  
Wei Wei Jin ◽  
Cun Yu ◽  
Xing Chao Yang ◽  
Zhan Xia Geng ◽  
Hao Ran Zhang ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Condition Monitoring ◽  
Support System ◽  
Engineering Application ◽  
Monitoring Method ◽  
Design And Implementation ◽  
On Line ◽  
Grid Construction ◽  
Condition Monitoring System

The shortcomings of current on-line condition monitoring system include: the research was concentrated in AC substation; the function was single and completely independent; the protocols were not compatible and the interface was not unified. To improve these disadvantages and better satisfy the needs of the condition monitoring in converter station, the design and implementation of condition monitoring and decision support system in HVDC converter station was realized. By studying of condition monitoring method, decision support technology and engineering application of HVDC project, the system design principle was discussed, the software structure was shown and the function realization was given in detail. The implementation realized real time monitoring for running equipments, provided reliable gist for maintenance and reduced the costs of resources. The implementation is applied to HVDC project witch has a great significance in improving the operation efficiency and promoting the smart grid construction.

scholarly journals Development of a model for choosing strategies for investing in information security

2021 ◽  
Vol 2 (3 (110)) ◽  
pp. 43-51
Author(s):  
Valeriy Lakhno ◽  
Volodimir Malyukov ◽  
Berik Akhmetov ◽  
Dmytro Kasatkin ◽  
Liubov Plyska
Keyword(s):  
Decision Support ◽  
Information Security ◽  
Decision Support System ◽  
Support System ◽  
New Class ◽  
Modular Software ◽  
Proposed Model ◽  
Software Product ◽  
Continue Research ◽  
Multidimensional Objects

This paper has proposed a model of the computational core for the decision support system (DSS) when investing in the projects of information security (IS) of the objects of informatization (OBI). Including those OBI that can be categorized as critically important. Unlike existing solutions, the proposed model deals with decision-making issues in the ongoing process of investing in the projects to ensure the OBI IS by a group of investors. The calculations were based on the bilinear differential quality games with several terminal surfaces. Finding a solution to these games is a big challenge. It is due to the fact that the Cauchy formula for bilinear systems with arbitrary strategies of players, including immeasurable functions, cannot be applied in such games. This gives grounds to continue research on finding solutions in the event of a conflict of multidimensional objects. The result was an analytical solution based on a new class of bilinear differential games. The solution describes the interaction of objects investing in OBI IS in multidimensional spaces. The modular software product "Cybersecurity Invest decision support system " (Ukraine) for the Windows platform is described. Applied aspects of visualization of the results of calculations obtained with the help of DSS have been also considered. The Plotly library for the Python algorithmic language was used to visualize the results. It has been shown that the model reported in this work can be transferred to other tasks related to the development of DSS in the process of investing in high-risk projects, such as information technology, cybersecurity, banking, etc.

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