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In time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent incidence among all the cancers whereas breast cancer takes fifth place in the case of mortality numbers. Out of many image processing techniques, certain works have focused on convolutional neural networks (CNNs) for processing these images. However, deep learning models are to be explored well.

In this work, multivariate statistics-based kernel principal component analysis (KPCA) is used for essential features. KPCA is simultaneously helpful for denoising the data. These features are processed through a heterogeneous ensemble model that consists of three base models. The base models comprise recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The outcomes of these base learners are fed to fuzzy adaptive resonance theory mapping (ARTMAP) model for decision making as the nodes are added to the F_2ˆa layer if the winning criteria are fulfilled that makes the ARTMAP model more robust.

The proposed model is verified using breast histopathology image dataset publicly available at Kaggle. The model provides 99.36% training accuracy and 98.72% validation accuracy. The proposed model utilizes data processing in all aspects, i.e. image denoising to reduce the data redundancy, training by ensemble learning to provide higher results than that of single models. The final classification by a fuzzy ARTMAP model that controls the number of nodes depending upon the performance makes robust accurate classification.

Research in the field of medical applications is an ongoing method. More advanced algorithms are being developed for better classification. Still, the scope is there to design the models in terms of better performance, practicability and cost efficiency in the future. Also, the ensemble models may be chosen with different combinations and characteristics. Only signal instead of images may be verified for this proposed model. Experimental analysis shows the improved performance of the proposed model. This method needs to be verified using practical models. Also, the practical implementation will be carried out for its real-time performance and cost efficiency.

The proposed model is utilized for denoising and to reduce the data redundancy so that the feature selection is done using KPCA. Training and classification are performed using heterogeneous ensemble model designed using RNN, LSTM and GRU as base classifiers to provide higher results than that of single models. Use of adaptive fuzzy mapping model makes the final classification accurate. The effectiveness of combining these methods to a single model is analyzed in this work.

This study aims to analyse how consumers' perceptions of justice in a service recovery scenario vary, not only due to the company's actions but also due to the comparisons they make with the experiences of other consumers.

Based on justice theory, social comparison theory and referent cognitions theory, this study describes an eight-scenario experiment with better or worse interactional, procedural and distributive justice (better/worse interactional justice given to other consumers) × 2 (better/worse procedural justice given to other consumers) × 2 (better/worse distributive justice given to other consumers).

First, consumers' perceptions of interactional, procedural and distributive justice vary based on the comparisons they draw with other consumers' experiences. Second, the results confirmed that interactional justice has a moderating effect on procedural justice, whereas procedural justice does not significantly moderate distributive justice.

First, based on justice theory, social comparison theory and referent cognitions theory, we focus on the influence of the treatment received by other consumers on the consumer's perceived justice in the same service recovery situation. Second, it is proposed that the three justice dimensions follow a defined sequence through the service recovery phases. Third, to the best of the authors' knowledge, this study is the first to propose a multistage model in which some justice dimensions influence other justice dimensions.

: 本研究擬探討在服務補救的處境裡, 消費者對公平的看法不但會受公司的行動所影響, 同時也會因他們與其他消費者的經驗作比較而有所改變。

: 本研究根據正義理論、社會比較理論和參照認知理論, 描述一個涵蓋八個處境的實驗, 實驗包含更好的或更差的互動的、程序上的和分配性的公平 (給予其他消費者更好的/更差的互動公平) × 2(給予其他消費者更好的/更差的程序上的公平) × 2 (給予其他消費者更好的/更差的分配性的公平)。

: 研究結果顯示, 消費者對互動的、程序上的和分配性公平的看法, 是會根據他們與其他消費者的體驗所作的比較而有所改變; 研究結果亦確認了互動的公平對程序上的公平會有調節作用, 而程序上的公平對分配性的公平則沒有顯著的調節作用。

: 首先, 我們根據正義理論、社會比較理論和參照認知理論, 把研究焦點放在於相同的服務補救情景中, 其他消費者受到的待遇, 如何影響消費者自身的認知公平; 另外, 我們建議, 這三個公平維度, 在各個服務補救階段裡, 均會跟隨一個清晰的次序。最後, 就研究人員所知, 本研究為首個提出一個公平維度互為影響的多階段模型的研究。