Software Defect Prediction Based on Hybrid Swarm Intelligence and Deep Learning

In order to improve software quality and testing efficiency, this paper implements the prediction of software defects based on deep learning. According to the respective advantages and disadvantages of the particle swarm algorithm and the wolf swarm algorithm, the two algorithms are mixed to realize the complementary advantages of the algorithms. At the same time, the hybrid algorithm is used in the search of model hyperparameter optimization, the loss function of the model is used as the fitness function, and the collaborative search ability of the swarm intelligence population is used to find the global optimal solution in multiple local solution spaces. Through the analysis of the experimental results of six data sets, compared with the traditional hyperparameter optimization method and a single swarm intelligence algorithm, the model using the hybrid algorithm has higher and better indicators. And, under the processing of the autoencoder, the performance of the model has been further improved.

Hybrid Swarm Enhanced Classifier Ensembles

Os Sistemas de Multi-Classificadores (MCSs) constituem um dos paradigmas mais competitivos para a obtenção de classificações precisas no campo do aprendizado de máquina. Este artigo busca avaliar se a utilização de algoritmos híbridos de enxames pode melhorar a performance dos MCSs por meio da otimização de pesos em combinações por voto majoritário ponderado. A metodologia proposta rendeu resultados competitivos em 25 conjuntos de dados de referência. Adotou-se a acurácia como função objetivo a ser maximizada pelas seguintes meta-heurísticas: otimização do exame de partículas (PSO), a colônia artificial de abelhas (ABC), e a alternativa híbrida das anteriores usando a técnica de multi enxames dinâmicos (DM-PSO-ABC).

Recombination of standing variation in a multi-hybrid swarm drove adaptive radiation in a fungal pathogen and gave rise to two pandemic plant diseases

Adaptive radiations fuel speciation and are characterized by rapid genetic diversification and expansion into new ecological niches. Historically, these processes were believed to be driven by selection on novel mutations but genomic analyses now indicate that standing variation and gene flow often have prominent roles. How "old" variation is combined, however, and its resulting genetic architecture within newly adapted populations is not well understood. We reconstructed a recent radiation in the fungus, Pyricularia oryzae, that spawned a population pathogenic to eleven grass genera, and caused two new plant diseases: wheat blast - already a serious threat to global agriculture - and gray leaf spot of ryegrasses. We show that the new population evolved in a multi-hybrid swarm using only the standing variation that was present in seven individuals from five distinct, host-specialized lineages. Sexual and parasexual recombination within the swarm reassorted key host-specificity factors and generated more diversity in possibly just a few weeks than existing lineages had accumulated over hundreds to thousands of years. We suggest that the process was initiated by sexual opportunity arising when a fertile fungal strain was imported into Brazil on Urochloa introduced as forage for beef production; and we further contend that the host range expansion was largely fortuitous, with host selection playing little, if any, role in driving the process. Finally, we believe that our findings point to an overlooked role for happenstance in creating situations that allow organisms to skirt rules that would normally hold evolution in check.

A Novel Approach For Privacy Preservation In Blockchain Network Using Tensor Product And A Hybrid Swarm Intelligence

Therefore, block chain-based technique is developed for privacy protection using tensor product and a hybrid swarm intelligence based coefficient generation. Initially, the block chain data with mixed attributes was subjected to the privacy preservation process, in which the raw data matrix and solitude and utility (SU) coefficient were multiplied through the tensor product. Thus, the derivation of the SU coefficient, which handles both sensitive information and utility, was formulated as a searching problem. Then, the proposed algorithm was introduced to evaluate the SU coefficient. The performance of the developed technique was evaluated by means of accuracy and information loss. The achieved results have shown that the developed hybrid sward intelligence reached a maximal accuracy of 0.840 and minimal information loss of 0.159 using dataset-2, compared to the existing system.

A Combined Image Segmentation and Classification Approach for COVID-19 Infected Lungs

Lung infection or sickness is one of the most common acute ailments in humans. Pneumonia is one of the most common lung infections, and the annual global mortality rate from untreated pneumonia is increasing. Because of its rapid spread, pneumonia caused by the Coronavirus Disease (COVID-19) has emerged as a global danger as of December 2019. At the clinical level, the COVID-19 is frequently measured using a Computed Tomography Scan Slice (CTS) or a Chest X-ray. The goal of this study is to develop an image processing method for analysing COVID-19 infection in CT Scan patients. The images in this study were preprocessed using the Hybrid Swarm Intelligence and Fuzzy DPSO algorithms. According to extensive computer simulations, the persistent learning strategy for CT image segmentation using image enhancement is more efficient and adaptive than the Medical Image Segmentation (MIS) method. The findings suggest that the proposed method is more dependable, accurate, and simple than existing methods.

Optimal Shift Scheduling at Ibu Pejabat Polis Daerah (IPD) Kuala Muda using Goal Programming

Many workplaces encounter complex problems in preparing an optimal work scheduling to meet the 24 hours work demand especially in shift working hours. The schedule needs to consider many constraints and multi objectives at the same time. A mathematical model such as Goal programming is able to cater this kind of problems. Thus, this study was designed to provide a systematic and optimal schedule for police officers at Criminal Unit, IPD Kuala Muda, Kedah. This study is aimed to formulate the best model for the shift rotating schedule of the police officers and to find the best way to optimize the police scheduling related to the limitations, requirements of the police station and the preferences of the police. Lingo software is used to run the model. However, only one out of three goals set for the study was achieved. The new schedule obtained shows that all police officers have the same number of working days, which is 21 days in the 28-day planning period. The new schedule produced is better than the previous manual schedule since it takes less time to prepare it without neglecting the constraints involved. To improve efficiency and flexibility on the generated schedules, it is recommended to use other methods such as hybrid swarm-based optimization and many new limitations and preferences should be also considered in the analysis.

Research on Key Algorithms of the Lung CAD System Based on Cascade Feature and Hybrid Swarm Intelligence Optimization for MKL-SVM

Feature selection and lung nodule recognition are the core modules of the lung computer-aided detection (Lung CAD) system. To improve the performance of the Lung CAD system, algorithmic research is carried out for the above two parts, respectively. First, in view of the poor interpretability of deep features and the incomplete expression of clinically defined handcrafted features, a feature cascade method is proposed to obtain richer feature information of nodules as the final input of the classifier. Second, to better map the global characteristics of samples, the multiple kernel learning support vector machine (MKL-SVM) algorithm with a linear convex combination of polynomial kernel and sigmoid kernel is proposed. Furthermore, this paper applied the methods for speed contraction factor and roulette strategy, and a mixture of simulated annealing (SA) and particle swarm optimization (PSO) is used for global optimization, so as to solve the problem that the PSO is easy to lose particle diversity and fall into the local optimal solution as well as improve the model’s training speed. Therefore, the MKL-SVM algorithm is presented in this paper, which is based on swarm intelligence optimization is proposed for lung nodule recognition. Finally, the algorithm construction experiments are conducted on the cooperative hospital dataset and compared with 8 advanced algorithms on the public dataset LUNA16. The experimental results show that the proposed algorithms can improve the accuracy of lung nodule recognition and reduce the missed detection of nodules.