Improved Trial Division Algorithm by Lagrange?s Interpolation Function

Nowadays data communication over the internetgrowths the security risk on the side of receiver and transmitter. To reduce risk level, cryptography technique has been used which is based on aprivate and public key in disquiet of endorsement. The process of encryption and decryption improved the capacity of data security. Asymmetric cryptography technique provides renowned RSA public key cryptography technique. The success story of RSA algorithm depends on the prime factor. For the estimation of theprime factor used various mathematical functions. In this paper,Lagrange?s interpolation derivation for the estimation of aprime factoris used. The estimated prime factor is very complex and reduces the complexity of prime factor

MOCF: A Multi-Objective Clustering Framework using an Improved Particle Swarm Optimization Algorithm

Traditional clustering algorithms, such as K-Means, perform clustering with a single goal in mind. However, in many real-world applications, multiple objective functions must be considered at the same time. Furthermore, traditional clustering algorithms have drawbacks such as centroid selection, local optimal, and convergence. Particle Swarm Optimization (PSO)-based clustering approaches were developed to address these shortcomings. Animals and their social Behaviour, particularly bird flocking and fish schooling, inspire PSO. This paper proposes the Multi-Objective Clustering Framework (MOCF), an improved PSO-based framework. As an algorithm, a Particle Swarm Optimization (PSO) based Multi-Objective Clustering (PSO-MOC) is proposed. It significantly improves clustering efficiency. The proposed framework's performance is evaluated using a variety of real-world datasets. To test the performance of the proposed algorithm, a prototype application was built using the Python data science platform. The empirical results showed that multi-objective clustering outperformed its single-objective counterparts

A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems

Collaborative Filtering (CF) is a widely used technique in recommendation systems to suggest items to users based on their previous interactions with the system. CF involves finding correlations between the preferences of different users and using those correlations to provide recommendations. This technique can be divided into user-based and item-based CF, both of which utilize similarity metrics to generate recommendations. Content-based filtering is another commonly used recommendation technique that analyzes the attributes of items to suggest similar items. To enhance the accuracy of recommendation systems, hybrid algorithms that combine CF and content-based filtering techniques have been developed. These hybrid systems leverage the strengths of both approaches to provide more accurate and personalized recommendations. In conclusion, collaborative filtering is an essential technique in recommendation systems, and the use of various similarity metrics and hybrid techniques can enhance the quality of recommendations

An Approach for Mining Top-k High Utility Item Sets (HUI)

Itemsets have been extracted by utilising high utility item (HUI) mining, which provides more benefits to the consumer. This could be one of the significant domains in data mining and be resourceful for several real-time implementations. Even though modern HUI mining algorithms may identify item sets that meet the minimum utility threshold, However, fixing the minimum threshold utility value has not been a simple task, and often it is intricate for the consumers when we keep the minimum utility value low. It might generate a massive amount of itemsets, and when the value is at its maximum, it might provide a smaller amount of itemsets. To avoid these issues, top-k HUI mining, where k represents the number of HUIs to be identified, has been proposed. Further, in this manuscript, the authors projected an algorithm called the top-k exact utility (TKEU) algorithm, which works without computing and comparing transaction weighted utilisation (TWU) values and deliberates the individual utility item values for deriving the top-k HUI. The datasets are pre-processed by the proposed algorithm to lessen the system memory space and to provide optimal outcomes for condensed datasets

Minimizing the Localization Error in Wireless Sensor Networks Using Multi-Objective Optimization Techniques

When it comes to remote sensing applications, wireless sensor networks (WSN) are crucial. Because of their small size, low cost, and ability to communicate with one another, sensors are finding more and more applications in a wide range of wireless technologies. The sensor network is the result of the fusion of microelectronic and electromechanical technologies. Through the localization procedure, the precise location of every network node can be determined. When trying to pinpoint the precise location of a node, a mobility anchor can be used in a helpful method known as mobility-assisted localization. In addition to improving route optimization for location-aware mobile nodes, the mobile anchor can do the same for stationary ones. This system proposes a multi-objective approach to minimizing the distance between the source and target nodes by employing the Dijkstra algorithm while avoiding obstacles. Both the Improved Grasshopper Optimization Algorithm (IGOA) and the Butterfly Optimization Algorithm (BOA) have been incorporated into multi-objective models for obstacle avoidance and route planning. Accuracy in localization is enhanced by the proposed system. Further, it decreases both localization errors and computation time when compared to the existing systems

Cloud Host Selection using Iterative Particle-Swarm Optimization for Dynamic Container Consolidation

A significant portion of the energy consumption in cloud data centres can be attributed to the inefficient utilization of available resources due to the lack of dynamic resource allocation techniques such as virtual machine migration and workload consolidation strategies to better optimize the utilization of resources. We present a new method for optimizing cloud data centre management by combining virtual machine migration with workload consolidation. Our proposed Energy Efficient Particle Swarm Optimization (EE-PSO) algorithm to improve resource utilization and reduce energy consumption. We carried out experimental evaluations with the Container CloudSim toolkit to demonstrate the effectiveness of the proposed EE-PSO algorithm in terms of energy consumption, quality of service guarantees, the number of newly created VMs, and container migrations