Predefined Object Reduction

Reduction techniques is still an open area to be explored in knowledge management. This paper defines algorithm known as Predefined Hybrid Reduction which generate its conditions for object co occurrences of original data then execute Hybrid Reduction data for their data to perform extractions. Predefined Hybrid Reduction give a proper solution for expansion the data set , it select significant object with high quality of informations, it delete every object not satisfies their conditions. It show appropriate relevant result. It provide better reduction without inconsistency problem unlike data comparisons. It manage the inferior object which store only significant data based on predefined confidence and predefined support for maintain the inferior object then Hybrid reduction which are dual reduction. As part of this proposal, a comparison test with Hybrid reduction. The conclusion part which shows better alternative result through our mode

An Alternative Algorithm for Soft Set Parameter Selection using Special Order

The outcome of the reduction of soft data is dependent on the quality and discount evidence that increases with optimization analysis. There is a set of techniques that can be used to reduce the data, but the different techniques showed different results as each technique is focused on solving a particular problem. This paper proposed a parameter reduction algorithm, known as 3C algorithm, to circumvent the false frequent object in reduction. Results indicated that the proposed algorithm is easy to implement and perform better than the state-of-the-art parameter reduction algorithm. Also, the proposed algorithm can be used as an effective alternative method for reducing parameters in order to enhance the decision making process. Comparative analysis were performed between the proposed algorithm and the state-of-the-art parameter reduction algorithm using several soft set in terms of parameter reductio

Self-Healing in Cyber–Physical Systems Using Machine Learning:A Critical Analysis of Theories and Tools

The rapid advancement of networking, computing, sensing, and control systems has introduced a wide range of cyber threats, including those from new devices deployed during the development of scenarios. With recent advancements in automobiles, medical devices, smart industrial systems, and other technologies, system failures resulting from external attacks or internal process malfunctions are increasingly common. Restoring the system’s stable state requires autonomous intervention through the self-healing process to maintain service quality. This paper, therefore, aims to analyse state of the art and identify where self-healing using machine learning can be applied to cyber–physical systems to enhance security and prevent failures within the system. The paper describes three key components of self-healing functionality in computer systems: anomaly detection, fault alert, and fault auto-remediation. The significance of these components is that self-healing functionality cannot be practical without considering all three. Understanding the self-healing theories that form the guiding principles for implementing these functionalities with real-life implications is crucial. There are strong indications that self-healing functionality in the cyber–physical system is an emerging area of research that holds great promise for the future of computing technology. It has the potential to provide seamless self-organising and self-restoration functionality to cyber–physical systems, leading to increased security of systems and improved user experience. For instance, a functional self-healing system implemented on a power grid will react autonomously when a threat or fault occurs, without requiring human intervention to restore power to communities and preserve critical services after power outages or defects. This paper presents the existing vulnerabilities, threats, and challenges and critically analyses the current self-healing theories and methods that use machine learning for cyber–physical systems

An enhanced soft set data reduction using decision partition order technique

Nowadays, redundant data is one of the open issues due to the rapid development of technologies. This issue is more visible especially in decision-making as the behaviour of such data is more complex and due to the uncertainty during a process of decision making. Besides, the need of extra memory is essential as redundant data makes use of storage and produce redundant copies due to its widespread use. Hence, the soft-set reduction techniques are introduced to assist in reducing storage space by facilitating less number of copies with minimum cost per line or per storage. The benefit of soft-set reduction is to foster the decision making process as well as to enhance the decision’s quality. Classification techniques that were previously proposed for eliminating inconsistency could not achieve an efficient soft-set reduction, which affects the obtained solutions; thus producing imprecise result. Furthermore, the decomposition based on previous algorithms could not achieve better parameter reduction in available domain space. The decomposition computational cost made during combination generation can cause machine infinite state as Nondeterministic Polynomial time (NP). The decomposition scenario in Rose’s and Kumar’s algorithms detects the reduction, but could not obtain the optimal decision. The contributions of this research are mainly focused on minimizing choices costs through adjusting the original classifications by decision partition order. Moreover, this research proposes a decision partition order technique to maintain the original classification consistency. The second contribution is enhancing the probability of search domain of Markov chain model. Furthermore, this research proposes an efficient Soft-Set Reduction accuracy based on Binary Particle Swarm optimized by Biogeography-Based Optimizer (SSR-BPSO-BBO) algorithm that can generate accurate decision for optimal and sub-optimal results. The results show that the decision partition order technique performs up to 50% in parameter reduction, while some algorithms could not obtain any reduction. On the other hand, the proposed Markov chain model could significantly represent the robustness of the proposed reduction technique in making the optimal decision and minimising the search domain by up to 33%. In terms of accuracy, the proposed SSR-BPSO-BBO algorithm outperforms other optimization algorithms by up to 100% in achieving high accuracy percentage of a given soft dataset. In addition, the proposed decision partition order technique has reduced the choices costs and thus improves the original classification consistency. Hence, the proposed technique could efficiently enhance the decision quality. Also, the accuracy of original soft-set optimal and sub-optimal results have been improved using an intelligent SSR-BPSO-BBO algorithm. The computational cost of search domain (space) has been enhanced using proposed Markov Chain Model

Hybrid Filter for Object Reduction

The basic idea to build significant attribute the uncertain objects should remove. Several theories are dealing with uncertainty, soft set theory also handles this uncertainty problem which still an open area to be explored in knowledge management. The propose techniques Known as Filtering data set which used for maintained the inferior object and we need to look at the other side of attribute reduction. The propose technique are reducing the size of object firstly, then the Hybrid reduction are executed for generating the decision extractions. These filters have reduced the size of memory without losing the characteristic of information which absolutely highly efficient. By using Filtering the inferior object of Hybrid techniques are managed. As part of this proposal, an analysis of Hybrid reduction techniques. In the conclusion part Filtering the Hybrid show better result compared to Hybrid reduction

Pre Soft Parameter Reduction From Soft Set

The complexity induced by machine infinite stae cost the decisions resource such as CPU time. In solving this problem serarch space should be divided or decomposed into several sub sets in order to look for the informations which contains original characterstics. Identical of original set and it’s reductions induced two sets which measured by Jaccard similirtity to select the reductions were totally dominated against original characterstics. Searching process decomposed implises into several sub combinations eliminations as pre reductions which checked by decision partition cluster using Hybrid complement reduction. The decomposisions performances enhanced searching complexity and provided faster result in the process of decisions making generation

Parameters Reduction Comparisions

In this proposal testing techniques has been done based on calculating reduction of optimal and sub optimal parameters for decision making soft set theory. Previous techniques of reduction in decision making for a Boolean-valued information system of standard soft set are described. Hybrid reduction compared to Maji, Chen, and Kong shows best criterion for parameters reductions on the conclusion par

Soft Set Decision/Forecasting System Based on Hybrid Parameter Reduction Algorithm

Existing classification techniques, which are previously proposed for eliminating data inconsistency, could not achieve an efficient parameter reduction in soft set theory as it affects the obtained decisions. Additionally, data decomposition based on previous algorithms could not achieve better parameter reduction with available domain space. Meanwhile, the computational cost made during the combination generation of datasets can cause machine infinite state as Nondeterministic Polynomial time (NP). Although the decomposition scenario in the previous algorithms detects the reduction, it could not obtain the optimal decision. The contributions of this study are mainly focused on minimizing choices costs through adjusting the original classifications by decision partition order and enhancing the probability of search domain by a developed HPC algorithm. The results show that the decision partition order technique performs better in parameter reduction up to 50%, while other algorithms could not obtain any reduction in some scenarios

Hybrid Filter for Attributes Reduction in Soft Set

The purpose of this research is to overcome hybrid parameterization reduction limitation that focuses only on individual parameter reduction, whereas in some cases the individual parameter reduction is not sufficient even implies reduction. It was found that the reduction sometimes is not able to reduce the number of data; hence, for this reason it became necessary to look for an alternative technique that can significantly reduce the parameters. This paper proposed an alternative method based on hybrid filter to select attributes in soft set. For significant candidates the method used R supp checking to confirm the correctness of the reduction. Comparison of the reduction methods shows that the proposed method provides better result than the parameterization reduction in enhancing reduction. The false candidates were filtered in the huge candidate reduction by the Min supp. The proposed method can be used to maintain object before attribute reduction as well as to reduce parameter size drastically while maintaining consistency in decision making