Earticle

The present research article focused on the factual findings of the potential usage of the combinational Feed-Forward Back Propagation Neural Network as a judgment making for lung cancer. In this context, Support Vector Machine is integrated with Feed-Forward Back Propagation Neural Network to create a hybrid algorithm that further helps in reducing the computation complexity of the classification. A set of 500 images are utilized in which 75% data is used for the training purpose and the rest 25% is used to achieve the classification. In the view of forgoing, a three-block mechanism is proposed for the classification in which the first block preprocesses the dataset, the second block extracts the features via the SURF technique followed by the optimization using Genetic Algorithm and the terminal block is for the classification via FFBPNN. The hybrid classification algorithm is named as Kernel Attribute Selected Classifier and the overall classification accuracy of the proposed algorithm is 98.08%. Herein, the objective of the study is to enhance the classification accuracy by applying a hybrid classification algorithm.

With recent trends, the cloud-computing paradigm has gained significant attention, especially in patient health monitoring applications. To date, several cryptographic methods have been introduced to accomplish secure medical data access and storage in cloud service providers. However, these methods have failed to strike a balance among the demands of Electronic Health Records (EHRs) security solutions. Blockchain-based technology is a practical option for managing individual EHRs, as it offers an advanced solution for enhancing the privacy and security of medical data. Therefore, this research proposes a hybrid cryptographic algorithm: Secure Hash Algorithm-256 (SHA-256) combined with Composite Logistic Sine Map (CLSM), namely SHACLSM, with Proxy Re-Encryption (PRE). Overall, the effectiveness of the proposed SHACLSM is analyzed in terms of scalability, computational delay, latency, transaction time, hash verification time, and hash generation time. The outcomes clearly indicate that the proposed SHACLSM-PRE cryptographic algorithm requires minimal transaction time, latency, hash generation time, and hash verification time when evaluated against conventional algorithms, namely Advanced Encryption Standard (AES), SHA-256, Elliptic Curve Cryptography (ECC), Rivest-Shamir-Adleman (RSA), and AES New Instructions (AES-NI).

In cloud computing systems the continuous increasing need for a variety of applications has made workload distribution and resource allocation more difficult. These difficulties lead to a high degree of imbalance and poor system performance. To address these issues, this study presented a high scalable Improved Multi-Objective Hybrid Load Balancing (IMH_LB) Algorithm. It combines the Grey Wolf Optimization (GWO) algorithm with the velocity driven approach of Particle Swarm Optimization (PSO) technique. The suggested method has been developed to optimize cloud computing environments by improving resource utilization with high scalability. The quality-of-service (QoS) parameters are comparatively analyzed in CloudSim by benchmarking the proposed algorithm against standard (GWO and PSO) algorithms and state of the art (Hybrid GWO-PSO and Improved Hybrid Genetic Algorithm-GWO) algorithms. The experimental findings show the superiority of the proposed approach with average improvements of 72.16% in average response time, 67.27% in makespan, 5.18 times in throughput, in 7.51 times in average resource utilization and 78.51% in degree of imbalance over the standard (GWO and PSO) and state of the art (HGWO-PSO and IHGA-GWO) methods. The proposed algorithm achieves high scalability while stabilizing at an average utilization of 89 % at significantly high workloads.

Task scheduling is one of the most important issues in heterogeneous environments when high efficiency is required. Because task scheduling is a Nondeterministic Polynomial (NP)-hard problem, many evolutionary algorithms have been adopted to solve this problem. Since the convergence speed of solutions in population-based algorithms is low, they are integrated with local search algorithms. Thus, in this paper, to optimize the task scheduling makespan, a hybrid particle swarm optimization and hill climbing algorithm is proposed. The experimental results on random and scientific Directed Acyclic Graph (DAG) showed that the proposed algorithm performs effectively in terms of the makespan compared to the current well-known heuristic and particle swarm optimization algorithms.

For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. The system that uses the artificial neural network(ANN) falls in a local minima when it learns many nonlinear data, and its classification accuracy ratio becomes low. To make up for this risk, the SVM/ANN hybrid algorithm is proposed in this paper. The proposed algorithm is the method that ANN learns selectively after clustering the SNR data by SVM, then more improved performance estimation can be obtained than using ANN only and The proposed algorithm can make the higher classification accuracy by decreasing the nonlinearity of the massive data during the training procedure. Experimental results indicate that the proposed SVM/ANN hybrid algorithm generally outperforms ANN algorithm.

플라스틱 사출 제품은 다양한 가전제품과 하이테크 제품에 널리 사용되고 있다. 그러나 현재의 치열한 경쟁적 비즈니스 환경에서 플라스틱 사출 제품 제조업자들은 고객을 만족시키면서 경쟁력을 얻기 위하여 다른 경쟁자들보다 먼저 새로운 제품을 시장에 출시하고 신제품의 개발기간을 줄이기 위한 노력을 할 여유가 부족하다. 따라서 무한 경쟁의 시장에서 살아남기 위해서는 제조업자들은 시장 마켓 점유를 빠르게 올리는 것과 동시에 제품의 가격 경쟁력을 가져야 한다. 특징기반 모델의 구조는 현재 연구에서 3D 제작 도구로서 일반적으로 적용되고 있으며 신제품 개발 엔지니어들이 새로운 제품의 개념을 개발하는 데에도 널리 사용되고 있다. 본 연구에서는 특징기반 플라스틱 사출제품을 위한 유전자 알고리즘과 Support Vector Regression (SVR) 기반의 새로운 하이브리드 비용 평가 모델을 제안한다. 제안하는 하이브리드 모델은 기존의 플라스틱 사출제품의 비용평가절차와 계산을 위해 필요로 하는 변수들을 극적으로 간단하게 하고 줄일 수 있다. 사례연구에서는 제안하는 하이브리드 모델과 기존의 multilayer perceptron networks (MLP) 및 pure SVR과의 비교분석을 통하여 제안모델이 플라스틱 사출 제품의 개발단계에서의 비용평가문제를 해결하는데 효율성과 효과성이 있음을 입증한다.

According to Genetic algorithms principle, the new hybrid evolutionary algorithm (HEA) is proposed in this paper by combining the Immune algorithm, Genetic algorithm and Pareto optimal solutions. The HEA has high convergence precision and improved the diversity of population. Multiple near optimization paths can be developed by the algorithm with multi‐objective restriction, and satisfy to minimize the routing of transportation and the numbers of the vehicles. The HEA has been used to solve the vehicle routing problem, the results of simulation experiment show that the HEA can gain higher global convergence rate and higher speed.

An ensemble classifier is a method that combines output of multiple classifiers. It has been widely accepted that ensemble classifiers can improve the prediction accuracy. Recently, ensemble techniques have been successfully applied to the bankruptcy prediction. Bagging and random subspace are the most popular ensemble techniques. Bagging and random subspace have proved to be very effective in improving the generalization ability respectively. However, there are few studies which have focused on the integration of bagging and random subspace. In this study, we proposed a new hybrid ensemble model to integrate bagging and random subspace method using genetic algorithm for improving the performance of the model. The proposed model is applied to the bankruptcy prediction for Korean companies and compared with other models in this study. The experimental results showed that the proposed model performs better than the other models such as the single classifier, the original ensemble model and the simple hybrid model.

Traveling salesman problem is to minimize the total cost for a traveling salesman who wants to make a tour given finite number of cities along with the cost of travel between each pair them, visiting each cities exactly once before returning home. Traveling salesman problem is known to be NP-hard, and it needs a lot of computing time to get the optimal solution, so that heuristics are more frequently developed than optimal algorithms. This study suggests a hybrid parallel genetic algorithm(HPGA) for traveling salesman problem. The suggested algorithm combines parallel genetic algorithm, nearest neighbor search, and 2-opt. The suggested algorithm has been tested on 7 problems in TSPLIB and compared the results of existing methods(heuristics, meta-heuristics, hybrid, and parallel). Experimental results shows that HPGA could obtain good solution in total travel distance minimization.

This paper proposes a hybrid algorithm by combining Lazy Snapping and Grass-fire Algorithm to make a faster and accurate 2D/3D image conversion. By removing the current noises from Optical Flow and using Lazy Snapping with Grass-fire together, the total progress will extract the object more accurately.

A new hybridized Artificial Bee Colony (HABC) algorithm is presented. The exploration/exploitation balancing strategy of Simulated Annealing is incorporated into the original ABC algorithm to improve its search efficiency and reduce its computational cost. The algorithm begins with a high exploration rate and minimal exploitation effort and gradually switches to higher exploitation rates as the promising areas of the search space are identified. The proposed algorithm is applied to a number of benchmark problems. Comparison of the results indicates that in most cases the hybridized algorithm outperforms the other two algorithms.

The traveling salesman problem (TSP) is one of the most studied in operations research and computer science. Research has led to a large number of techniques to solve this problem; in particular, genetic algorithms (GA) produce good results compared to other techniques. A disadvantage of GA, though, is that they easily become trapped in the local minima. In this paper, a cuckoo search optimizer (CS) is used along with a GA in order to avoid the local minima problem and to benefit from the advantages of both types of algorithms. A 2-opt operation was added to the algorithm to improve the results. The suggested algorithm was applied to multiple sequence alignment and compared with the previous algorithms.

The Congestion in wireless sensor networks occurs when packets arrive at a fully loaded buffer of a sensor node. Due to lack of space in the buffer, packets are dropped. It also leads to packet delay, retransmission of packets, reduced QoS and throughput. A hybrid of rate control and resource control algorithms can be used to control congestion considering the priority of the packets. Congestion is detected when the service time is greater than the inter-arrival time of packets. The inter-arrival time can be increased by a certain factor computed as congestion degree, thus reducing the rate of sending packets. The congestion degree which is the ratio of service time to inter-arrival time can be sent to the sources. The sources can then adjust the rate of sending packets accordingly to control congestion in the upstream nodes. At the time of congestion, high priority packets are sent using multiple paths after increasing the time between generations of packets thus decreasing the rate of sending packets. Low priority packets are sent using a single path after decreasing the packet sending rate thus increasing the time between sending consecutive packets. The method also serves as a hybrid of congestion control and congestion avoidance.

Clustering is a process of discovering group of objects such that the objects of the same group are similar, and objects belonging to different groups are dissimilar. A number of clustering algorithms exist that can solve the problem of clustering, but most of them are very sensitive to their input parameters. Minimum Spanning Tree clustering algorithm is capable of detecting clusters with irregular boundaries. A density-based notion of clusters which is designed to discover clusters of arbitrary shape. In this paper we propose a combined approach based on Minimum Spanning Tree based clustering and Density-based clustering for noise-free high density best number of clusters. The algorithm uses a new cluster validation criterion based on the geometric property of data partition of the data set in order to find the proper number of clusters at each level. The algorithm works in two phases. The first phase of the algorithm produces subtrees (noise-free clusters). The second phase finds high density clusters from the subtrees.

It is important to extract salient object image and to solve the invariance problem for image recognition. In this paper we propose a new hybrid algorithm for invariance and improved classification performance in image recognition, whose algorithm is combined by FT(Frequency-tuned Salient Region Detection) algorithm, Guided filter, Zernike moments, and a simple artificial neural network (Multi-layer Perceptron). The conventional FT algorithm is used to extract initial salient object image, the guided filtering to preserve edge details, Zernike moments to solve invariance problem, and a classification to recognize the extracted image. For guided filtering, guided filter is used, and Multi-layer Perceptron which is a simple artificial neural networks is introduced for classification. Experimental results show that this algorithm can achieve a superior performance in the process of extracting salient object image and invariant moment feature. And the results show that the algorithm can also classifies the extracted object image with improved recognition rate.

A new hybrid Optimization Algorithm is proposed to solve Constrained Multi-objective Optimization Problems (CMOPs). The algorithm is named BBO/DE which combines the exploitation ability of Biogeography-based Optimization (BBO) and the exploration ability of Differential Evolution (DE). Meanwhile distance measures and adaptive penalty functions are adopted to handle the constraints so that optimal solutions in the infeasible space can be searched effectively. In addition, the feasible archive is applied to store the non-dominated feasible solutions obtained so far and is updated based on crowding-distance. Experiment results demonstrate that the proposed hybrid algorithm BBO/DE can approximate the true Pareto front and has better distribution.

Optimization is ability of find the best solution in the existing situations. Optimization is used in design and maintenance of systems engineering, economic, social and even necessary to reduce costs and increase profits. The widespread importance of optimization problem has a lot of grown. There are many algorithms for optimization and they are trying to reduce the disadvantages of other methods and increase the ability of resolve the problem. This paper proposed an adaptive ABC and Bat algorithm. The idea of algorithm is improved speed of convergence and optimized search in search space for ABC algorithm with Bat algorithm. The proposed algorithm is compared with ABC and Bat algorithm on benchmark function and test shows ABC_M are improved obviously. Also can be known a complete local search is more important from global search.

Allocation of optimum active power is a backbone of power system generation planning and its high impact contribution is the need of current electrical utilities and power engineers need to browse this area in short and long term planning scenarios. Power demand requirements mapped to economic feasible solutions matching voltage profile, power demand, minimization of losses, voltage stability and improve the capacity of the system is the need of the hour. Modern techniques based on evolutionary computing, artificial intelligence, search method find their objectives in the area of economic load dispatch planning to reach global optimal solution for this multi-decision, multi-objective combinatorial problem subjected to different constraints. Many algorithms suffer from global convergence problem. To vanish this drawback, neuro inspired genetic hybrid algorithm (NIGHA) has been proposed in this paper to solve economic dispatch problem. Unlike other algorithms, NIGHA utilizes the weights of Neural Network to explore information and knowledge to train GA parameters to search for feasible region where optimal global solution converges. The suggested technique is tested on IEEE 25 bus system. Test results are compared with other techniques presented in literature. Proposed technique has outperformed other methods in terms of cost, computation time.