The Real-Time Mobile Application for Classifying of Endangered Parrot Species Using the CNN Models Based on Transfer Learning

Among the many deep learning methods, the convolutional neural network (CNN) model has an excellent performance in image recognition. Research on identifying and classifying image datasets using CNN is ongoing. Animal species recognition and classification with CNN is expected to be helpful for various applications. However, sophisticated feature recognition is essential to classify quasi-species with similar features, such as the quasi-species of parrots that have a high color similarity. The purpose of this study is to develop a vision-based mobile application to classify endangered parrot species using an advanced CNN model based on transfer learning (some parrots have quite similar colors and shapes). We acquired the images in two ways: collecting them directly from the Seoul Grand Park Zoo and crawling them using the Google search. Subsequently, we have built advanced CNN models with transfer learning and trained them using the data. Next, we converted one of the fully trained models into a file for execution on mobile devices and created the Android package files. The accuracy was measured for each of the eight CNN models. The overall accuracy for the camera of the mobile device was 94.125%. For certain species, the accuracy of recognition was 100%, with the required time of only 455 ms. Our approach helps to recognize the species in real time using the camera of the mobile device. Applications will be helpful for the prevention of smuggling of endangered species in the customs clearance area.

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Design and Development of the CTAR All-Star

10.29007/h37n ◽

2019 ◽

Author(s):

Terri Heglar ◽

Andrew Penrose ◽

Austin Yount ◽

Kristine Galek ◽

Yantao Shen ◽

...

Keyword(s):

Real Time ◽

Mobile Device ◽

Pressure Sensor ◽

Mobile Application ◽

Future Research ◽

Objective Data ◽

Chin Tuck ◽

Time Graph ◽

Cross Platform ◽

Over Time

The CTAR All-Star is a system consisting of a rubber ball, a pressure sensor, and a bluetooth transmitter paired with a cross-platform mobile application. The device is used as a rehabilitation tool for people with dysphagia in a similar fashion to the traditional chin tuck against resistance (CTAR) exercise by squeezing a ball between the chin and upper chest. The mobile device monitors and displays the pressure inside the ball on a real-time graph allowing the patient to follow exercise routines set by Speech-Language Pathologists. Additionally, the application stores exercise data that can be used to both monitor the patient's progress over time and provide objective data for future research purposes.

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Augmented Reality in the Presentation of City Monuments: A Case Study of “Bielsko-Biała AR Guide” Mobile Application

Folia Quaternaria ◽

10.4467/21995923gp.21.003.14974 ◽

2021 ◽

Vol 20 ◽

pp. 31-40

Author(s):

Stanisław Szombara ◽

Małgorzata Zontek

Keyword(s):

Augmented Reality ◽

Real Time ◽

Mobile Device ◽

Mobile Application ◽

Geospatial Data ◽

Camera Image ◽

Historical Objects ◽

The City ◽

Modern Technologies

Augmented Reality (AR) is one of the modern technologies used for sharing 3D geospatial data. This article presents possible ways of enriching a mobile application containing information about 50 objects located in the city of Bielsko-Biała with an AR functionality. The application was created in two programs: Android Studio and Unity. The application allows to get to know historical objects of the city, encourages to visit them by adding virtual elements observed in the background of a real-time camera image from a mobile device. The article presents the statistics of the application usage and the results of a survey conducted among a group of testers. Feedback from application testers confirms the validity of using AR technology in the application. ROZSZERZONA RZECZYWISTOŚĆ W PREZENTACJI ZABYTKÓW MIASTA: APLIKACJA „BIELSKO-BIAŁA PRZEWODNIK AR”, STUDIUM PRZYPADKU Rzeczywistość Rozszerzona (Augmented Reality – AR) jest jedną z nowoczesnych technologii wykorzystywanych do udostępniania danych przestrzennych 3D. W artykule przedstawiono możliwe sposoby wzbogacenia aplikacji mobilnej o funkcjonalność AR. Aplikacja zawiera informacje o 50 obiektach zlokalizowanych na terenie miasta Bielska-Białej i została stworzona w dwóch programach: Android Studio oraz Unity. Aplikacja pozwala na poznanie zabytkowych obiektów miasta oraz zachęca do ich zwiedzania poprzez dodanie wirtualnych elementów obserwowanych w czasie rzeczywistym na tle obrazu z kamery urządzenia mobilnego. W artykule przedstawiono statystyki użytkowania aplikacji oraz wyniki ankiety przeprowadzonej wśród grupy testerów. Informacje zwrotne od testerów aplikacji potwierdzają zasadność zastosowania technologii AR w aplikacji.

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Sky Segmentation for Enhanced Depth Reconstruction and Bokeh Rendering with Efficient Architectures

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.14.coimg-378 ◽

2020 ◽

Vol 2020 (14) ◽

pp. 378-1-378-7

Author(s):

Tyler Nuanes ◽

Matt Elsey ◽

Radek Grzeszczuk ◽

John Paul Shen

Keyword(s):

Real Time ◽

Mobile Device ◽

Computational Cost ◽

False Positives ◽

Compact Model ◽

High Quality ◽

False Negatives ◽

Trade Off ◽

Depth Reconstruction ◽

Binary Classifiers

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.

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Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽

10.3390/s21144736 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4736

Author(s):

Sk. Tanzir Mehedi ◽

Adnan Anwar ◽

Ziaur Rahman ◽

Kawsar Ahmed

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Intrusion Detection ◽

Real Time ◽

Transfer Learning ◽

Security Requirements ◽

Detection Accuracy ◽

Area Network ◽

Complex Data ◽

Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

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Plant Recognition Using Morphological Feature Extraction and Transfer Learning over SVM and AdaBoost

Symmetry ◽

10.3390/sym13020356 ◽

2021 ◽

Vol 13 (2) ◽

pp. 356

Author(s):

Shubham Mahajan ◽

Akshay Raina ◽

Xiao-Zhi Gao ◽

Amit Kant Pandit

Keyword(s):

Feature Extraction ◽

Transfer Learning ◽

Plant Species ◽

Species Recognition ◽

Morphological Features ◽

Support Vector ◽

Additional Advantage ◽

Adaptive Boosting ◽

Vast Number ◽

Axis Length

Plant species recognition from visual data has always been a challenging task for Artificial Intelligence (AI) researchers, due to a number of complications in the task, such as the enormous data to be processed due to vast number of floral species. There are many sources from a plant that can be used as feature aspects for an AI-based model, but features related to parts like leaves are considered as more significant for the task, primarily due to easy accessibility, than other parts like flowers, stems, etc. With this notion, we propose a plant species recognition model based on morphological features extracted from corresponding leaves’ images using the support vector machine (SVM) with adaptive boosting technique. This proposed framework includes the pre-processing, extraction of features and classification into one of the species. Various morphological features like centroid, major axis length, minor axis length, solidity, perimeter, and orientation are extracted from the digital images of various categories of leaves. In addition to this, transfer learning, as suggested by some previous studies, has also been used in the feature extraction process. Various classifiers like the kNN, decision trees, and multilayer perceptron (with and without AdaBoost) are employed on the opensource dataset, FLAVIA, to certify our study in its robustness, in contrast to other classifier frameworks. With this, our study also signifies the additional advantage of 10-fold cross validation over other dataset partitioning strategies, thereby achieving a precision rate of 95.85%.

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Development of The Personnel Monitoring System Using Mobile Application and Real-Time Database During the COVID19 Pandemic

2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) ◽

10.1109/isriti51436.2020.9315377 ◽

2020 ◽

Author(s):

Muladi ◽

Aripriharta ◽

Ilham Ari Elbaith Zaeni ◽

Siti Sendari ◽

Abd. Kadir bin Mahamad ◽

...

Keyword(s):

Real Time ◽

Monitoring System ◽

Mobile Application ◽

Real Time Database

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Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning

Scientific Reports ◽

10.1038/s41598-021-90688-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Simon Tam ◽

Mounir Boukadoum ◽

Alexandre Campeau-Lecours ◽

Benoit Gosselin

Keyword(s):

Real Time ◽

Transfer Learning ◽

Control Strategy ◽

High Density ◽

Fine Tuning ◽

Mode Switching ◽

Correct Prediction ◽

Learning Approach ◽

Predictive Values ◽

Hand Prosthesis

AbstractMyoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.

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