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In recent years, convolutional neural networks (CNN) have become leading algorithms for many computer vision tasks. Many studies have used deep learning and artificial intelligence (AI). This paper summarizes the development of deep learning approaches and the impact of AI on marketing and customer behavior and discusses the challenges facing marketing research. The ma jor marketing application of AI is to support customer decision-making. However, most previous marketing research has focused on how to contribute to marketing decision-making in practice. We believe that there is room for research on how deep learning approaches can be used to support marketing decision-making. One area that has seen major developments in deep learning approaches is that of image data analysis, including video analysis. In marketing decision-making, market segmentation and positioning strategies are closely related to image data analysis. We believe that more research based on deep learning approaches should be conducted to support decision-making in these areas. Therefore, to support decision-making in marketing, we present studies on market segmentation and positioning strategy formulation as examples of studies that combine deep learning approaches with traditional marketing research. [ABSTRACT FROM AUTHOR]

The acoustic survey is now gaining attention as a method by which to monitor birds. It has advantages over conventional methods, such as point count and line transect methods, because of its low disturbance to natural behaviors. Recent research has addressed development of automatic bird species identification systems using Artificial Intelligence (AI) techniques to actively identify bird calls. Despite a recent increase in research, an effective and systematic procedure has not been identified. To build an automated bird species identification process, we tested multiple methods of sound and image preprocessing with representative machine learning models. The target species was the Grey-faced Buzzard-eagle Butastur indicus, which is a species of raptor categorized as "Vulnerable (VU)" in the Japanese Red List. The sound files included the species' calls recorded at two different sites. The optimal sound threshold to detect the bird calls, and the sound duration to sample, were identified using a Convolutional Neural Network (CNN) model and evaluating the cross-validation and generalization performance. Alternative methods, including the random forest model and multi-layer perception model, were compared with the performance of CNN. We determined that the optimal method was applying CNN-based classification systems using sounds of 2-3 seconds, detected by the increase of 1-3 dB sound pressure level over the prior 10 milliseconds. The highest F1-score of cross-validation and generalization performance were 0.89 and 0.71, respectively. [ABSTRACT FROM AUTHOR]

Although one would expect to use genomic data to discriminate diseases/phenotypes, this is a complex and hard task as key information to achieve it is spread over high-dimensional spaces. To address this issue, we envisaged that clustering similar variables, e.g. genes, would facilitate the identification of such information. Thus, here we have developed DeepInsight, a method comprising three steps: rearrange variables, extract features, and construct a classification model. Non-image data are transformed into image data allowing us to apply and take advantage of the learning capabilities of Convolutional Neural Networks (CNNs). Our experiments showed that with real data, such as cancer transcriptome, DeepInsight achieved much higher accuracy than typical machine learning methods. Our methodology has the potential to contribute to various biomedical applications and improve feature and structure extraction in non-image data. [ABSTRACT FROM AUTHOR]

Facial expressions play an important role in communication as much as words. In facial expression recognition by human, it is difficult to uniquely judge, because facial expression has the sway of recognition by individual difference and subjective recognition. Therefore, it is difficult to evaluate the reliability of the result from recognition accuracy alone, and the analysis for explaining the result and feature learned by Convolutional Neural Networks (CNN) will be considered important. In this study, we carried out the facial expression recognition from facial ex- pression images using CNN. In addition, we analysed CNN for understanding learned features and prediction results. Emotions we focused on are "happiness", "sadness", "surprise", "anger", "disgust", "fear" and "neutral". As a result, using 32286 facial expression images, have obtained an emotion recognition score of about 57%; for two emotions (Happiness, Surprise) the recognition score exceeded 70%, but Anger and Fear was less than 50%. In the analysis of CNN, we focused on the learning process, input and intermediate layer. Analysis of the learning progress confirmed that increased data can be recognised in the following order "happiness", "surprise", "neutral", "anger", "disgust", "sadness" and "fear". From the analysis result of the input and intermediate layer, we confirmed that the feature of the eyes and mouth strongly influence the facial expression recognition, and intermediate layer neurons had active patterns corresponding to facial expressions, and also these activate patterns do not respond to partial features of facial expressions. From these results, we concluded that CNN has learned the partial features of eyes and mouth from input, and recognise the facial expression using hidden layer units having the area corresponding to each facial expression. [ABSTRACT FROM AUTHOR]