Deep learning for image-based large-flowered chrysanthemum cultivar recognition

Abstract Background Cultivar recognition is a basic work in flower production, research, and commercial application. Chinese large-flowered chrysanthemum (Chrysanthemum × morifolium Ramat.) is miraculous because of its high ornamental value and rich cultural deposits. However, the complicated capitulum structure, various floret types and numerous cultivars hinder chrysanthemum cultivar recognition. Here, we explore how deep learning method can be applied to chrysanthemum cultivar recognition. Results We propose deep learning models with two networks VGG16 and ResNet50 to recognize large-flowered chrysanthemum. Dataset A comprising 14,000 images for 103 cultivars, and dataset B comprising 197 images from different years were collected. Dataset A was used to train the networks and determine the calibration accuracy (Top-5 rate of above 98%), and dataset B was used to evaluate the model generalization performance (Top-5 rate of above 78%). Moreover, gradient-weighted class activation mapping (Grad-CAM) visualization and feature clustering analysis were used to explore how the deep learning model recognizes chrysanthemum cultivars. Conclusion Deep learning method applied to cultivar recognition is a breakthrough in horticultural science with the advantages of strong recognition performance and high recognition speed. Inflorescence edge areas, disc floret areas, inflorescence colour and inflorescence shape may well be the key factors in model decision-making process, which are also critical in human decision-making.

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Neuromarketing Perspective of Consumer Choice

Applying Neuroscience to Business Practice - Advances in Business Strategy and Competitive Advantage ◽

10.4018/978-1-5225-1028-4.ch003 ◽

2017 ◽

pp. 49-62

Author(s):

Salim Lahmiri

Keyword(s):

Decision Making ◽

Consumer Choice ◽

Investor Sentiment ◽

Research Area ◽

Key Factors ◽

Economic Decision Making ◽

Consumer Decision Process ◽

Psychology And Economics ◽

Human Decision ◽

Financial Decision

Behavioural research attempts to study how individuals make decisions and interact and influence other individuals, organizations, markets and society. In this regard, applied neuroscience in human decision-making has gained an increasing attention in recent decades with emergence of two disciplines; namely neuroeconomics and neuromarketing. Indeed, neuroeconomics has emerged as a multidisciplinary research area that integrates knowledge from neuroscience, psychology, and economics to better understand economic decision making and to specify more accurate models of choice and decision. In particular, neuroeconomics is becoming an attractive area of study and research in financial decision making with particular emphasis on understanding investor sentiment and fear when faced to different investment opportunities characterized by various scenarios. In particular, it aims to understand and explain consumer decision process and influence of marketing key factors on consumer choice. As a result, companies may define appropriate marketing strategies based on neuromarketing studies.

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Neuromarketing Perspective of Consumer Choice

Applications of Neuroscience ◽

10.4018/978-1-5225-5478-3.ch013 ◽

2018 ◽

pp. 286-295

Author(s):

Salim Lahmiri

Keyword(s):

Decision Making ◽

Consumer Choice ◽

Investor Sentiment ◽

Research Area ◽

Key Factors ◽

Economic Decision Making ◽

Consumer Decision Process ◽

Psychology And Economics ◽

Human Decision ◽

Financial Decision

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DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

Computation ◽

10.3390/computation7020025 ◽

2019 ◽

Vol 7 (2) ◽

pp. 25 ◽

Cited By ~ 15

Author(s):

Abhaya Kumar Sahoo ◽

Chittaranjan Pradhan ◽

Rabindra Kumar Barik ◽

Harishchandra Dubey

Keyword(s):

Health Care ◽

Decision Making ◽

Deep Learning ◽

Recommender System ◽

Data Analytics ◽

Big Data Analytics ◽

Health Condition ◽

Healthcare Sector ◽

Learning Method ◽

Decision Making Processes

In today’s digital world healthcare is one core area of the medical domain. A healthcare system is required to analyze a large amount of patient data which helps to derive insights and assist the prediction of diseases. This system should be intelligent in order to predict a health condition by analyzing a patient’s lifestyle, physical health records and social activities. The health recommender system (HRS) is becoming an important platform for healthcare services. In this context, health intelligent systems have become indispensable tools in decision making processes in the healthcare sector. Their main objective is to ensure the availability of the valuable information at the right time by ensuring information quality, trustworthiness, authentication and privacy concerns. As people use social networks to understand their health condition, so the health recommender system is very important to derive outcomes such as recommending diagnoses, health insurance, clinical pathway-based treatment methods and alternative medicines based on the patient’s health profile. Recent research which targets the utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed which reduces the workload and cost in health care. In the healthcare sector, big data analytics using recommender systems have an important role in terms of decision-making processes with respect to a patient’s health. This paper gives a proposed intelligent HRS using Restricted Boltzmann Machine (RBM)-Convolutional Neural Network (CNN) deep learning method, which provides an insight into how big data analytics can be used for the implementation of an effective health recommender engine, and illustrates an opportunity for the health care industry to transition from a traditional scenario to a more personalized paradigm in a tele-health environment. By considering Root Square Mean Error (RSME) and Mean Absolute Error (MAE) values, the proposed deep learning method (RBM-CNN) presents fewer errors compared to other approaches.

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Using Deep Learning to Predict Human Decisions, and Cognitive Models to Explain Deep Learning Models

10.1101/2021.01.13.426629 ◽

2021 ◽

Author(s):

Matan Fintz ◽

Margarita Osadchy ◽

Uri Hertz

Keyword(s):

Neural Networks ◽

Decision Making ◽

Deep Learning ◽

Deep Neural Networks ◽

High Capacity ◽

Cognitive Models ◽

Human Behaviour ◽

Data Driven ◽

Human Decision ◽

High Level

AbstractDeep neural networks (DNN) models have the potential to provide new insights in the study of human decision making, due to their high capacity and data-driven design. While these models may be able to go beyond theory-driven models in predicting human behaviour, their opaque nature limits their ability to explain how an operation is carried out. This explainability problem remains unresolved. Here we demonstrate the use of a DNN model as an exploratory tool to identify predictable and consistent human behaviour in value-based decision making beyond the scope of theory-driven models. We then propose using theory-driven models to characterise the operation of the DNN model. We trained a DNN model to predict human decisions in a four-armed bandit task. We found that this model was more accurate than a reinforcement-learning reward-oriented model geared towards choosing the most rewarding option. This disparity in accuracy was more pronounced during times when the expected reward from all options was similar, i.e., no unambiguous good option. To investigate this disparity, we introduced a reward-oblivious model, which was trained to predict human decisions without information about the rewards obtained from each option. This model captured decision-sequence patterns made by participants (e.g., a-b-c-d). In a series of experimental offline simulations of all models we found that the general model was in line with a reward-oriented model’s predictions when one option was clearly better than the others.However, when options’ expected rewards were similar to each other, it was in-line with the reward-oblivious model’s pattern completion predictions. These results indicate the contribution of predictable but task-irrelevant decision patterns to human decisions, especially when task-relevant choices are not immediately apparent. Importantly, we demonstrate how theory-driven cognitive models can be used to characterise the operation of DNNs, making them a useful explanatory tool in scientific investigation.Author SummaryDeep neural networks (DNN) models are an extremely useful tool across multiple domains, and specifically for performing tasks that mimic and predict human behaviour. However, due to their opaque nature and high level of complexity, their ability to explain human behaviour is limited. Here we used DNN models to uncover hitherto overlooked aspects of human decision making, i.e., their reliance on predictable patterns for exploration. For this purpose, we trained a DNN model to predict human choices in a decision-making task. We then characterised this data-driven model using explicit, theory-driven cognitive models, in a set of offline experimental simulations. This relationship between explicit and data-driven approaches, where high-capacity models are used to explore beyond the scope of established models and theory-driven models are used to explain and characterise these new grounds, make DNN models a powerful scientific tool.

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Study of Deep Learning Methods f or Fingerprint Recognition

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c6478.0910321 ◽

2021 ◽

Vol 10 (3) ◽

pp. 192-197

Author(s):

Mamadou Diarra ◽

◽

Ayikpa Kacoutchy Jean ◽

Ballo Abou Bakary ◽

Kouassi Brou Medard ◽

...

Keyword(s):

Decision Making ◽

Deep Learning ◽

High Reliability ◽

Color Images ◽

Fingerprint Recognition ◽

Behavioral Characteristics ◽

Network System ◽

Learning Method ◽

Biometric Systems ◽

Deep Convolution Network

Biometric systems aim to reliably identify and authenticate an individual using physiological or behavioral characteristics. Traditional systems such as the use of access cards, passwords have shown limitations such as forgotten passwords, stolen cards, etc. As an alternative, biometric systems present themselves as efficient systems with a high reliability due to the physiological characteristics of each individual. This paper focuses on a deep learning method for fingerprint recognition. The described architecture uses a pre-processing phase in which grayscale images are represented on the RGB bands and then merged to obtain color images. On the obtained color images will be extracted the characteristics of the fingerprints textures.The fingerprint images after preprocessing are used in a deep convolution network system for decision making. The method is robust with an accuracy of over 99.43% and 99.53% with the respective variants densenet-201 and ResNet-50.

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A Deep Learning Method for Lane Changing Situation Assessment and Decision Making

IEEE Access ◽

10.1109/access.2019.2940853 ◽

2019 ◽

Vol 7 ◽

pp. 133749-133759 ◽

Cited By ~ 3

Author(s):

Xiao Liu ◽

Jun Liang ◽

Bing Xu

Keyword(s):

Decision Making ◽

Deep Learning ◽

Learning Method ◽

Situation Assessment ◽

Lane Changing

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A novel deep learning approach to extract Chinese clinical entities for lung cancer screening and staging

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01575-x ◽

2021 ◽

Vol 21 (S2) ◽

Author(s):

Huanyao Zhang ◽

Danqing Hu ◽

Huilong Duan ◽

Shaolei Li ◽

Nan Wu ◽

...

Keyword(s):

Lung Cancer ◽

Decision Making ◽

Deep Learning ◽

Cancer Screening ◽

Clinical Decision Making ◽

Lung Cancer Screening ◽

Clinical Decision ◽

Experimental Results ◽

Entity Recognition ◽

Learning Method

Abstract Background Computed tomography (CT) reports record a large volume of valuable information about patients’ conditions and the interpretations of radiology images from radiologists, which can be used for clinical decision-making and further academic study. However, the free-text nature of clinical reports is a critical barrier to use this data more effectively. In this study, we investigate a novel deep learning method to extract entities from Chinese CT reports for lung cancer screening and TNM staging. Methods The proposed approach presents a new named entity recognition algorithm, namely the BERT-based-BiLSTM-Transformer network (BERT-BTN) with pre-training, to extract clinical entities for lung cancer screening and staging. Specifically, instead of traditional word embedding methods, BERT is applied to learn the deep semantic representations of characters. Following the long short-term memory layer, a Transformer layer is added to capture the global dependencies between characters. Besides, pre-training technique is employed to alleviate the problem of insufficient labeled data. Results We verify the effectiveness of the proposed approach on a clinical dataset containing 359 CT reports collected from the Department of Thoracic Surgery II of Peking University Cancer Hospital. The experimental results show that the proposed approach achieves an 85.96% macro-F1 score under exact match scheme, which improves the performance by 1.38%, 1.84%, 3.81%,4.29%,5.12%,5.29% and 8.84% compared to BERT-BTN, BERT-LSTM, BERT-fine-tune, BERT-Transformer, FastText-BTN, FastText-BiLSTM and FastText-Transformer, respectively. Conclusions In this study, we developed a novel deep learning method, i.e., BERT-BTN with pre-training, to extract the clinical entities from Chinese CT reports. The experimental results indicate that the proposed approach can efficiently recognize various clinical entities about lung cancer screening and staging, which shows the potential for further clinical decision-making and academic research.

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An efficient deep learning model for cultivar identification of a pistachio tree

British Food Journal ◽

10.1108/bfj-12-2020-1100 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Ahmad Heidary-Sharifabad ◽

Mohsen Sardari Zarchi ◽

Sima Emadi ◽

Gholamreza Zarei

Keyword(s):

Decision Making ◽

Deep Learning ◽

Cultivar Identification ◽

Recognition Rate ◽

Final Decision ◽

Content Type ◽

Image Patches ◽

Human Decision ◽

High Resolution Images ◽

Effective Use

PurposeThis paper proposes a novel deep learning based method towards the identification of a pistachio tree cultivar from its image.Design/methodology/approachThe investigated scope of this study includes Iranian commercial pistachios (Jumbo, Long, Round and Super long) trees. Effective use of high-resolution images with standard deep models is addressed in this study. A novel image patches extraction method is also used to boost the number of samples and dataset augmentation. In the proposed method, handcrafted ORB features are used to detect and extract patches which may contain identifiable information. An innovative algorithm is proposed for searching and extracting these patches. After extracting patches from initial images, a Convolutional Neural Network, named EfficientNet-B1, was fine-tuned on it. In the testing phase, several patches were extracted from the prompted image using the ORB-based method, and the results of their prediction were consolidated. In this method, patch prediction scores were in descending order, sorted by the highest score in a list, and finally, the average of a few list tops was calculated and the final decision was made.FindingsExamining the proposed method on the test images led to an achievement of a recognition rate of 97.2% accuracy. Investigation of decision-making in the test dataset could reveal that this method outperformed human experts.Originality/valueCultivar identification using deep learning methods, due to their high recognition speed, lack of specialist requirement, and independence from human decision-making error is considered as a breakthrough in horticultural science. Variety cultivars of pistachio trees possess variant characteristics or traits, accordingly recognising cultivars is crucial to reduce the costs, prevent damages and harvest the optimal yields.

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