A Multiobjective Genetic Fuzzy Approach for Intelligent System-level Exploration in Parameterized VLIW Processor Design

2006 ◽  
Author(s):  
G. Ascia ◽  
V. Catania ◽  
A.G. di Nuovo ◽  
M. Palesi ◽  
D. Patti
Keyword(s):  
Intelligent System ◽  
System Level ◽  
Fuzzy Approach ◽  
Processor Design ◽  
Vliw Processor

scholarly journals Machine Learning and Cryptographic Algorithms – Analysis and Design in Ransomware and Vulnerabilities Detection

2020 ◽  
Author(s):  
Nandkumar Niture
Keyword(s):  
Machine Learning ◽  
Intelligent System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
System Level ◽  
Management Systems ◽  
System Response ◽  
Hard Part ◽  
Analysis And Design ◽  
Password Management

The AI, deep learning and machine learning algorithms are gaining the ground in every application domain of information technology including information security. In formation security domain knows for traditional password management systems, auto-provisioning systems and user information management systems. There is another raising concern on the application and system level security with ransomware. On the existing systems cyber-attacks of Ransomware asking for ransom increasing every day. Ransomware is the class of malware where the goal is to gain the data through encryption mechanism and render back with the ransom. The ransomware attacks are mainly on the vulnerable systems which are exposed to the network with weak security measures. With the help of machine learning algorithms, the pattern of the attacks can be analyzed. Create or discuss a workaround solution of a machine learning model with combination of cryptographic algorithm which will enhance the effectiveness of the system response to the possible attacks. The other part of the problem, which is hard part to create an intelligence for the organizations for preventing the ransomware attacks with the help of intelligent system password management and intelligent account provisioning. In this paper I elaborate on the machine learning algorithms analysis for the intelligent ransomware detection problem, later part of this paper would be design of the algorithm.

A Fuzzy Model with Thermodynamic Based Consequents and a Niching Swarm-Based Supervisor to Capture the Uncertainties of Damavand Power System

Fuzzy Systems ◽  
2017 ◽  
pp. 292-307
Author(s):  
Ahmad Mozaffari ◽  
Moein Mohammadpour ◽  
Alireza Fathi ◽  
Mofid Gorji-Bandpy
Keyword(s):  
Machine Learning ◽  
Power System ◽  
Intelligent System ◽  
Fuzzy Model ◽  
Operating Conditions ◽  
Operating Parameters ◽  
Fuzzy Approach ◽  
Swarm Optimization ◽  
Learning Mechanism ◽  
High Degree

In this investigation, a novel fuzzy mathematical program based on thermodynamic principles is implemented to capture the uncertainties of a practical power system, known as Damavand power plant. The proposed intelligent machine takes the advantages of a niching bio-inspired learning mechanism to be reconciled to the requirements of the problem at hand. The aim of the bio-inspired fuzzy based intelligent system is to yield a model capable of recognizing different operating parameters of Damavand power system under different operating conditions. To justify the privileges of using a niching metaheuristic over gradient descend methods, the authors use the data, derived through data acquisition, together with a machine learning based approach to estimate the multi-modality associated with the training of the proposed fuzzy model. Moreover, the niching bio-inspired metaheuristic, niching particle swarm optimization (NPSO), is compared to canonical PSO (CPSO), stochastic social PSO (SSPSO), unified PSO (UPSO), comprehensive learning PSO (CLPSO), PSO with constriction factor (PSOCF) and fully informed PSO (FIPSO). Through experiments and analysis of the characteristics of the problem being optimized, it is proved that NPSO is not only able to tackle the deficiencies of the learning process, but also can effectively adjust the fuzzy approach to conduct the identification process with a high degree of robustness and accuracy.

scholarly journals Machine Learning and Cryptographic Algorithms – Analysis and Design in Ransomware and Vulnerabilities Detection

2020 ◽  
Author(s):  
Nandkumar Niture
Keyword(s):  
Machine Learning ◽  
Intelligent System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
System Level ◽  
Management Systems ◽  
System Response ◽  
Hard Part ◽  
Analysis And Design ◽  
Password Management

The AI, deep learning and machine learning algorithms are gaining the ground in every application domain of information technology including information security. In formation security domain knows for traditional password management systems, auto-provisioning systems and user information management systems. There is another raising concern on the application and system level security with ransomware. On the existing systems cyber-attacks of Ransomware asking for ransom increasing every day. Ransomware is the class of malware where the goal is to gain the data through encryption mechanism and render back with the ransom. The ransomware attacks are mainly on the vulnerable systems which are exposed to the network with weak security measures. With the help of machine learning algorithms, the pattern of the attacks can be analyzed. Create or discuss a workaround solution of a machine learning model with combination of cryptographic algorithm which will enhance the effectiveness of the system response to the possible attacks. The other part of the problem, which is hard part to create an intelligence for the organizations for preventing the ransomware attacks with the help of intelligent system password management and intelligent account provisioning. In this paper I elaborate on the machine learning algorithms analysis for the intelligent ransomware detection problem, later part of this paper would be design of the algorithm.

Agility of Supply Chain Management Solution Using Neural-Fuzzy Approach

2019 ◽  
Vol 16 (10) ◽  
pp. 4143-4148 ◽  
Author(s):  
Avinash Sharma ◽  
Aarti M. Karande ◽  
Dhananjay R. Kalbande
Keyword(s):  
Supply Chain ◽  
Business Process ◽  
Intelligent System ◽  
Business Environment ◽  
Rule Base ◽  
Fuzzy Approach ◽  
Computing Technique ◽  
Neuro Fuzzy ◽  
Soft Computing Technique ◽  
Unpredictable Environment

Enterprise solution is the architecture of collecting and processing business information. Business process agility affects process-based applications works as per changing business environment. This paper helps to understand different changing environment of business process in the supply chain domain. Changes depend on organizational policy; hence it can be incomplete or uncertain. To manage this unpredictable environment, a soft computing technique is used for constructing intelligent system. This paper shows use of Neuro-fuzzy approach to monitor agile behavior of the business process. Neural network phase is used for finding business process and parameter criticality. Fuzzy logic rule base phase calculates process agility based on the relation between process and it’s affecting parameter. Developed tool, shows that business architecture level is more prone to changes as compared to other architectural levels from the enterprise solution.

scholarly journals Weather Modeling Using Data-Driven Adaptive Rough-Neuro-Fuzzy Approach

2017 ◽  
Vol 12 (2) ◽  
pp. 429-435
Author(s):  
M. Sudha
Keyword(s):  
Intelligent Systems ◽  
Prediction Accuracy ◽  
Intelligent System ◽  
Data Driven ◽  
Fuzzy Approach ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Hybrid Data ◽  
Weather Modeling ◽  
Using Data

Recently, hybrid data-driven models have become appropriate predictive patterns in various hydrological forecast scenarios. Especially, meteorology has witnessed that there is a need for a much better approach to handle weather-related parameters intelligently. To handle this challenging issue, this research intends to apply the fuzzy and ANN theories for developing hybridized adaptive rough-neuro-fuzzy intelligent system. . Assimilating the features of ANN and FIS has attracted the rising attention of researchers due to the growing requisite of adaptive intelligent systems to solve the real world requirements. The proposed model is capable of handling soft rule boundaries and linguistic variables to improve the prediction accuracy. The adaptive rough-neuro-fuzzy approach attained an enhanced prediction accuracy of 95.49 % and outperformed the existing techniques.

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