Fully-automatic natural plant recognition system using deep neural network for dynamic outdoor environments

Health care for independently living elders is more important than ever. Automatic recognition of their Activities of Daily Living (ADL) is the first step to solving the health care issues faced by seniors in an efficient way. The paper describes a Deep Neural Network (DNN)-based recognition system aimed at facilitating smart care, which combines ADL recognition, image/video processing, movement calculation, and DNN. An algorithm is developed for processing skeletal data, filtering noise, and pattern recognition for identification of the 10 most common ADL including standing, bending, squatting, sitting, eating, hand holding, hand raising, sitting plus drinking, standing plus drinking, and falling. The evaluation results show that this DNN-based system is suitable method for dealing with ADL recognition with an accuracy rate of over 95%. The findings support the feasibility of this system that is efficient enough for both practical and academic applications.

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Real-time Hand-Gesture Recognition based on Deep Neural Network

SHS Web of Conferences ◽

10.1051/shsconf/202110204009 ◽

2021 ◽

Vol 102 ◽

pp. 04009

Author(s):

Naoto Ageishi ◽

Fukuchi Tomohide ◽

Abderazek Ben Abdallah

Keyword(s):

Neural Network ◽

Real Time ◽

Gesture Recognition ◽

Deep Neural Network ◽

Recognition System ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Robotic Control ◽

Driver Assistance Systems ◽

Fast Classification

Hand gestures are a kind of nonverbal communication in which visible bodily actions are used to communicate important messages. Recently, hand gesture recognition has received significant attention from the research community for various applications, including advanced driver assistance systems, prosthetic, and robotic control. Therefore, accurate and fast classification of hand gesture is required. In this research, we created a deep neural network as the first step to develop a real-time camera-only hand gesture recognition system without electroencephalogram (EEG) signals. We present the system software architecture in a fair amount of details. The proposed system was able to recognize hand signs with an accuracy of 97.31%.

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An Indoor and Outdoor Positioning Using a Hybrid of Support Vector Machine and Deep Neural Network Algorithms

Journal of Sensors ◽

10.1155/2018/1253752 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 12

Author(s):

Abebe Belay Adege ◽

Hsin-Piao Lin ◽

Getaneh Berie Tarekegn ◽

Yirga Yayeh Munaye ◽

Lei Yen

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Deep Neural Network ◽

Support Vector ◽

Feed Forward Neural Network ◽

Online Data ◽

Location Services ◽

Outdoor Environments ◽

Indoor And Outdoor Environments ◽

Indoor And Outdoor

Indoor and outdoor positioning lets to offer universal location services in industry and academia. Wi-Fi and Global Positioning System (GPS) are the promising technologies for indoor and outdoor positioning, respectively. However, Wi-Fi-based positioning is less accurate due to the vigorous changes of environments and shadowing effects. GPS-based positioning is also characterized by much cost, highly susceptible to the physical layouts of equipment, power-hungry, and sensitive to occlusion. In this paper, we propose a hybrid of support vector machine (SVM) and deep neural network (DNN) to develop scalable and accurate positioning in Wi-Fi-based indoor and outdoor environments. In the positioning processes, we primarily construct real datasets from indoor and outdoor Wi-Fi-based environments. Secondly, we apply linear discriminate analysis (LDA) to construct a projected vector that uses to reduce features without affecting information contents. Thirdly, we construct a model for positioning through the integration of SVM and DNN. Fourthly, we use online datasets from unknown locations and check the missed radio signal strength (RSS) values using the feed-forward neural network (FFNN) algorithm to fill the missed values. Fifthly, we project the online data through an LDA-based projected vector. Finally, we test the positioning accuracies and scalabilities of a model created from a hybrid of SVM and DNN. The whole processes are implemented using Python 3.6 programming language in the TensorFlow framework. The proposed method provides accurate and scalable positioning services in different scenarios. The results also show that our proposed approach can provide scalable positioning, and 100% of the estimation accuracies are with errors less than 1 m and 1.9 m for indoor and outdoor positioning, respectively.

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Smartphone assist deep neural network model to recognize the high-quality tea using leaf maturity and its effect on leaf chlorophyll

Journal of Applied and Natural Science ◽

10.31018/jans.v13i4.2950 ◽

2021 ◽

Vol 13 (4) ◽

pp. 1249-1255

Author(s):

Utpal Barman ◽

Ridip Dev Choudhury ◽

Bipul Kumar Talukdar ◽

George Bhokta ◽

Sahrul Alom Choudhari ◽

...

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Nearest Neighbor ◽

Recognition System ◽

Support Vector ◽

K Nearest Neighbor ◽

Tea Leaves ◽

High Quality ◽

Leaf Maturity ◽

Tea Leaf

Immature and tender tea leaves always produce high-quality tea than mature tea leaves. Depending on the maturity and age of the leaf, the colour and texture of the tea leaf are different. The photosynthesis capacity of the tea leaf also changes with the change of leaf maturity. Though the tea farmer plucks, classifies, and recognizes the best tea leaves (immature and tender) by viewing the visual symptoms and position of the leaves, the method is not authentic all time and leads to the overall degradation of the tea quality. The present study presents a smartphone assist tea leaf recognition system by analyzing the colour and texture properties of the tea leaf. The six different colour features and 4 Haralick texture features were extracted in the colour and grey domain of the leaf images. Three types of tea leaves, i.e., mature, immature, and tender, were classified using Deep Neural Network (DNN) with ADAM (Adaptive Moment Estimation) optimizer. With an accuracy of 97%, the DNN outperformed the Support Vector Machine (SVM) and K Nearest Neighbor (KNN). The SVM and KNN reported a total of 94.42% and 95.53% accuracy, respectively. The investigated system using DNN with an average precision and recall value of 98.67 and 98.34, respectively, may detect and classify the tea leaf maturity status. The system also can be used in AI-based tea plucking robotic systems or machines.

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