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Automobile industry has been the backbone of manufacturing sector in any country. During the past decade, passenger car industry has emerged as the one of the growing sectors in the Indian economy. Technological features in the passenger cars industry has been evolving in the global market, and customers have been the most important stakeholders to judge the requirement of these features. Therefore, the purpose of this paper is to analyze the customers’ need for these emerging technologies using Kano model of customer satisfaction.

This paper has used the Kano model to assess the customer satisfaction for Indian passenger car companies. Overall, 250 customers of passenger cars from Northern India have been surveyed using well-structured questionnaire designed as per the Kano model. On the basis of responses, this study has categorized the technological attributes of passenger cars as attractive, must be, one-dimensional and indifferent.

“Auto Gear Shift” system has emerged as a must be attribute. “Premium surround system” has been categorized under one-dimensional attribute. “Communication between vehicles,” “integration with smart phone,” “connecting applications,” “dual-stage airbags,” “in-dash navigation system,” “rearview camera,” “heated and cooled seats,” “built-in fourth generation long term evolution,” “Wi-Fi system” and “automated window cleaning system” have emerged as attractive features. The customers have been indifferent about “gesture control,” “reality display on car wind screen” and “run-on-flat tyre.” In contradiction to the popular belief, this study has found that customers have shown Indifferent attitude toward “hydrogen fuel-operated cars” and “battery cars.”

This present study gives insight about the acceptability of various emerging technological features in Indian car market. This study has fulfilled the existing dearth in assessing the customers’ insight about the implementation of these emerging technologies in Indian cars. This paper will be helpful to the manufacturers to inculcate the voice of the customers in designing the new technologies for the passenger cars.

Previous studies across the globe have applied Kano model for assessing customers’ satisfaction in various industries, but according to the authors’ knowledge, hardly any study was conducted in context of technological attributes for Indian passenger car companies.

Increasing trends of climatic risk pose challenges to the food security and livelihoods of smallholders in vulnerable regions, where farmers often face loss of the entire crop, pushing farmers (mostly men) out of agriculture in destitution, creating a situation of agricultural making agriculture highly feminization and compelling male farmers to out-migrate. Climate-smart agricultural practices (CSAPs) are promoted to cope with climatic risks. This study aims to assess how knowledge related to CSAPs, male out-migration, education and income contribute to the determinants of male out-migration and CSAPs adoption and how they respond to household food security.

Sex-disaggregated primary data were collected from adopter and non-adopter farm families. STATA 13.1 was used to perform principle component analysis to construct knowledge, yield and income indices.

Yield and income index of adopters was higher for men than women. The probability of out-migration reduced by 21% with adoption of CSAPs. An increase in female literacy by 1 unit reduces log of odds to migrate by 0.37. With every unit increase in knowledge index, increase in log-odds of CSAPs adoption was 1.57. Male:female knowledge gap was less among adopters. Non-adopters tended to reduce food consumption when faced with climatic risks significantly, and the probability of migration increased by 50% with a one-unit fall in the nutrition level, thus compelling women to work more in agriculture. Gender-equitable enhancement of CSAP knowledge is, therefore, key to safeguarding sustainable farming systems and improving livelihoods.

The enhancement of gender equitable knowledge on CSAPs is key to safeguard sustainable farming systems and improved livelihoods.

This study is based on the robust data sets of 100 each of male and female from 100 households (n = 200) using well-designed and validated survey instrument. From 10 randomly selected climate-smart villages in Samastipur and Vaishali districts of Bihar, India, together with focus group discussions, the primary data were collected by interviewing both men and women from the same household.

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.