While urad bean is an important grain crop in several locations, it is frequently plagued by a number illness that has a devastating effect on harvest yields and quality. Using Convolutional Neural Net- works (CNNs) w...
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Segregation of seeds of different crops grown in the mixed cropping is a major cause of concern for the farmers as well as the food industry. Also, the classification and packaging of seeds based on their quality is a...
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Analysis and Prediction of forex has gained immense value in today's economy. The stock price prediction is a difficult process owing to the irregularities in stock prices. Every trader wants to know if the patter...
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Multimodal learning, which is simultaneous learning from different data sources such as audio, text, images, is a rapidly emerging field of machinelearning. It is also considered as machinelearning at the next upper...
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Acceleration data have been widely used to study human activity recognition. However, the acceleration data collected from the accelerometer do not consider the force of gravity. Thus, it has a difficulty in discrimin...
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This paper proposes a human machine interaction system in the field of stroke rehabilitation, based on the concept of mirror therapy (MT). It aims to improve the hand motor function of stroke patients, enabling a true...
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ISBN:
(数字)9781728158716
ISBN:
(纸本)9781728158716
This paper proposes a human machine interaction system in the field of stroke rehabilitation, based on the concept of mirror therapy (MT). It aims to improve the hand motor function of stroke patients, enabling a true synchronization between the affected hand and non-affected hand (healthy hand) for the stroke patient. It consists of a soft exoskeleton glove, a surface electromyography (sEMG) signal collecting armband and machinelearning (ML) algorithms. The glove is developed by integrating low-power motors to provide force strength for the hand movement. Unlike the rigid exoskeleton devices, the glove is comfortable to wear and lightweight, so it is more suitable for rehabilitation training of stroke patients in daily life. The armband collects the sEMG signals for patternrecognition by the ML algorithms. In the experiment, four subjects perform 10 hand gestures to collect data for model training. A comparison of data preprocessing is conducted to find the optimal data segmentation method and feature vector sets. A series of patternrecognition algorithms are developed and assessed in different aspects, including prediction accuracy, training time and predicting time. All 10 gestures can be recognized in offline mode with an accuracy up to 99.4%. The control of soft exoskeleton glove in real-time manner is also carried out, and the accuracy is 82.2%. The experiment result demonstrates the feasibility of the proposed system. The innovations and limitations of the work are discussed at the end of the paper.
The proceedings contain 7 papers. The topics discussed include: how to teach a computer to learn about microbes: KG-COVID-19 and microbial graph learning;explaining multivariate time series forecasts: an application t...
The proceedings contain 7 papers. The topics discussed include: how to teach a computer to learn about microbes: KG-COVID-19 and microbial graph learning;explaining multivariate time series forecasts: an application to predicting the Swedish GDP;towards participatory design spaces for explainable ai interfaces in expert domains;teaching AI to explain its decisions can affect class balance;foundations for solving classification problems with quantitative abstract argumentation;sequential exceptional pattern discovery using pattern-growth: an extensible framework for interpretable machinelearning on sequential data;and a comparative study of explainer modules applied to automated skin lesion classification.
Credit scoring (CS) is an effective and crucial approach used for risk management in banks and other financial institutions. It provides appropriate guidance on granting loans and reduces risks in the financial area. ...
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Credit scoring (CS) is an effective and crucial approach used for risk management in banks and other financial institutions. It provides appropriate guidance on granting loans and reduces risks in the financial area. Hence, companies and banks are trying to use novel automated solutions to deal with CS challenge to protect their own finances and customers. Nowadays, different machinelearning (ML) and datamining (DM) algorithms have been used to improve various aspects of CS prediction. In this paper, we introduce a novel methodology, named Deep Genetic Hierarchical Network of Learners (DGHNL). The proposed methodology comprises different types of learners, including Support Vector machines (SVM), k-Nearest Neighbors (kNN), Probabilistic Neural Networks (PNN), and fuzzy systems. The statlog German (1000 instances) credit approval dataset available in the UCI machinelearning repository is used to test the effectiveness of our model in the CS domain. Our DGHNL model encompasses five kinds of learners, two kinds of data normalization procedures, two extraction of features methods, three kinds of kernel functions, and three kinds of parameter optimizations. Furthermore, the model applies deep learning, ensemble learning, supervised training, layered learning, genetic selection of features (attributes), genetic optimization of learners parameters, and novel genetic layered training (selection of learners) approaches used along with the cross-validation (CV) train-ingtesting method (stratified 10-fold). The novelty of our approach relies on a proper flow and fusion of information (DGHNL structure and its optimization). We show that the proposed DGHNL model with a 29-layer structure is capable to achieve the prediction accuracy of 94.60% (54 errors per 1000 classifications) for the statlog German credit approval data. It is the best prediction performance for this well-known credit scoring dataset, compared to the existing work in the field. (C) 2019 The Authors. Publish
Herewith, we present a learning procedure that allows to deal with a partially labeled sequence dataset, i.e. when each sequence in the train dataset may contain labeled as well as unlabeled chunks. In our application...
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ISBN:
(纸本)9783030649111;9783030649128
Herewith, we present a learning procedure that allows to deal with a partially labeled sequence dataset, i.e. when each sequence in the train dataset may contain labeled as well as unlabeled chunks. In our application case, this occurs when motor activity has been manually annotated (due to the recognition based on the video recording) and independently registered by the measuring system of high precision (touch sensors): human annotation misses some events that have been captured by the sensors. In the general setting, we aim at predicting the labels for a new fully unlabeled movement sequence, while the training has been performed on the partially labeled dataset. For this purpose we propose to use classical sequence model (hidden Markov model) that is furnished with a constrained Viterbi algorithm, which gives us a quick access to the hard approximation of the correct labeling sequences. We demonstrate, that this simple modification that constrained Viterbi provide, allows the HMM model to be trained on sparse data, and overall results in surprisingly high log-likelihood and accuracy level in annotating the partially labeled behavioral sequences in climbing. The same time we show the way to access correct labeling of the unannotated signal that can be helpful in various sport science studies for movement pattern sequential prediction.
Predicting student performance is a fundamental task in Intelligent Tutoring Systems (ITSs), by which we can learn about students’ knowledge level and provide personalized teaching strategies for them. Researche...
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