An Introduction to MultiAgent Systems.

Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks

This article presents the findings of a systematic review of perceived usability of educational technology systems. The research was conducted after studying, organizing, and analyzing the results ...

Perceived usability evaluation of educational technology using the System Usability Scale (SUS): A systematic review

HighlightsA multimodal emotion recognition system was developed with HMMs, ANNs, and PCA.Text stimuli was designed to create an emotional speech database of Mexican users.Genetic algorithms improved the performance of HMMs and ANNs for emotion recognition.A dialogue system was developed for interaction with a humanoid robot.Live test with different users showed a multimodal emotion recognition rate of 97%. Service robotics is an important field of research for the development of assistive technologies. Particularly, humanoid robots will play an increasing and important role in our society. More natural assistive interaction with humanoid robots can be achieved if the emotional aspect is considered. However emotion recognition is one of the most challenging topics in pattern recognition and improved intelligent techniques have to be developed to accomplish this goal. Recent research has addressed the emotion recognition problem with techniques such as Artificial Neural Networks (ANNs)/Hidden Markov Models (HMMs) and reliability of proposed approaches has been assessed (in most cases) with standard databases. In this work we (1) explored on the implications of using standard databases for assessment of emotion recognition techniques, (2) extended on the evolutionary optimization of ANNs and HMMs for the development of a multimodal emotion recognition system, (3) set the guidelines for the development of emotional databases of speech and facial expressions, (4) rules were set for phonetic transcription of Mexican speech, and (5) evaluated the suitability of the multimodal system within the context of spoken dialogue between a humanoid robot and human users. The development of intelligent systems for emotion recognition can be improved by the findings of the present work: (a) emotion recognition depends on the structure of the database sub-sets used for training and testing, and it also depends on the type of technique used for recognition where a specific emotion can be highly recognized by a specific technique, (b) optimization of HMMs led to a Bakis structure which is more suitable for acoustic modeling of emotion-specific vowels while optimization of ANNs led to a more suitable ANN structure for recognition of facial expressions, (c) some emotions can be better recognized based on speech patterns instead of visual patterns, and (d) the weighted integration of the multimodal emotion recognition system optimized with these observations can achieve a recognition rate up to 97.00 % in live dialogue tests with a humanoid robot.

Multimodal emotion recognition with evolutionary computation for human-robot interaction

Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming

Early recognition of citrus diseases is important for preventing crop losses and employing timely disease control measures in farms. Employing machine learning-based approaches, such as deep learning for accurate detection of multiple citrus diseases is challenging due to the limited availability of labeled diseased samples. Further, a lightweight architecture with low computational complexity is required to perform citrus disease classification on resource-constrained devices, such as mobile phones. This enables practical utility of the architecture to perform effective monitoring of diseases by farmers using their own mobile devices in the farms. Hence, we propose a lightweight, fast, and accurate deep metric learning-based architecture for citrus disease detection from sparse data. In particular, we propose a patch-based classification network that comprises an embedding module, a cluster prototype module, and a simple neural network classifier, to detect the citrus diseases accurately. Evaluation of our proposed approach using publicly available citrus fruits and leaves dataset reveals its efficiency in accurately detecting the various diseases from leaf images. Further, the generalization capability of our approach is demonstrated using another dataset, namely the tea leaves dataset. Comparison analysis of our approach with existing state-of-the-art algorithms demonstrate its superiority in terms of detection accuracy (95.04%), the number of parameters required for tuning (less than 2.3 M), and the time efficiency in detecting the citrus diseases (less than 10 ms) using the trained model. Moreover, the ability to learn with fewer resources and without compromising accuracy empowers the practical utility of the proposed scheme on resource-constrained devices, such as mobile phones.

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Deep metric learning based citrus disease classification with sparse data

Research in the field of micro-expressions has gained significance in recent years. Many researchers have concentrated on classifying micro-expressions in different discrete emotion classes, while detecting the presence of micro-expression in the video frames is considered as a pre-requisite step in the recognition process. Hence, there is a need to introduce more advanced detection models for micro-expressions. In order to address this, we propose a dual attention network based micro-expression detection architecture called LGAttNet. LGAttNet is one of the first to utilize a dual attention network grouped with 2-dimensional convolutional neural network to perform frame-wise automatic micro-expression detection. This method divides the feature extraction and enhancement task into two different convolutional neural network modules; sparse module and feature enhancement module. One of the key modules in our approach is the attention network which extracts local and global facial features, namely local attention module and global attention module. The attention mechanism adopts the human characteristic of focusing on the specific regions of micro-movements, which enables the LGAttNet to concentrate on particular facial regions along with the full facial features to identify the micro-expressions in the frames. Experiments performed on widely used publicly available databases demonstrate the robustness and superiority of our LGAttNet when compared to state-of-the-art approaches.

LGAttNet: automatic micro-expression detection using dual-stream local and global attentions

In this research, a literature in human-computer interaction is reviewed and the technology aspect of human computer interaction related with digital academic supportive devices is also analyzed. According to all these concerns, recommendations to design good human-computer digital academic supportive devices are analyzed and proposed. Due to improvements in both hardware and software, digital devices have unveiled continuous advances in efficiency and processing capacity. However, many of these systems are also becoming larger and increasingly more complex. Although such complexity usually poses no difficulties for many users, it often creates barriers for academic users while using digital devices. Usually, in designing those digital devices, the human-computer interaction is left behind without consideration. To achieve dependable, usable, and well-engineered interactive digital academic supportive devices requires applied human computer interaction research and awareness of its issues.

Effective Use of Human Computer Interaction in Digital Academic Supportive Devices

Detecting emotional states of human from videos is essential in order to automate the process of profiling human behaviour, which has applications in a variety of domains, such as social, medical and behavioural science. Considerable research has been carried out for binary classification of emotions using facial expressions. However, a challenge exists to automate the feature extraction process to recognise the various intensities or levels of emotions. The intensity information of emotions is essential for tasks such as sentiment analysis. In this work, we propose a metric-based intensity estimation mechanism for primary emotions, and a deep hybrid convolutional neural network-based approach to recognise the defined intensities of the primary emotions from spontaneous and posed sequences. Further, we extend the intensity estimation approach to detect the basic emotions. The frame level facial action coding system annotations and the intensities of action units associated with each primary emotion are considered for deriving the various intensity levels of emotions. The evaluation on benchmark datasets demonstrates that our proposed approach is capable of correctly classifying the various intensity levels of emotions as well as detecting them.

Emotion Intensity Estimation from Video Frames using Deep Hybrid Convolutional Neural Networks

Generally, in-the-wild emotions are complex in nature. They often occur in combinations of multiple basic emotions, such as fear, happy, disgust, anger, sadness and surprise. Unlike the basic emotions, annotation of complex emotions, such as pain, is a time-consuming and expensive exercise. Moreover, there is an increasing demand for profiling such complex emotions as they are useful in many real-world application domains, such as medical, psychology, security and computer science. The traditional emotion recognition systems require a significant amount of annotated training samples to understand the complex emotions. This limits the direct applicability of those methods for complex emotion detection from images and videos. Therefore, it is important to learn the profile of the in-the-wild complex emotions accurately using limited annotated samples. In this paper, we propose a deep framework to incrementally and actively profile in-the-wild complex emotions, from sparse data. Our approach consists of three major components, namely a pre-processing unit, an optimization unit and an active learning unit. The pre-processing unit removes the variations present in the complex emotion images extracted from an uncontrolled environment. Our novel incremental active learning algorithm along with an optimization unit effectively predicts the complex emotions present in-the-wild. Evaluation using multiple complex emotions benchmark datasets reveals that our proposed approach performs close to the human perception capability in effectively profiling complex emotions. Further, our proposed approach shows a significant performance enhancement, in comparison with the state-of-the-art deep networks and other benchmark complex emotion profiling approaches.

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Selvarajah Thuseethan

Papers

Deep metric learning based citrus disease classification with sparse data

LGAttNet: automatic micro-expression detection using dual-stream local and global attentions

Effective Use of Human Computer Interaction in Digital Academic Supportive Devices

Emotion Intensity Estimation from Video Frames using Deep Hybrid Convolutional Neural Networks

Complex emotion profiling: An incremental active learning based approach with sparse annotations