scholarly journals Mosquito Larvae Detection using Deep Learning

2019 ◽  
Vol 8 (12) ◽  
pp. 804-809
Keyword(s):  
Deep Learning ◽  
Mobile Application ◽  
Web Application ◽  
Individual Performance ◽  
Mosquito Larvae ◽  
Accuracy Score ◽  
Performance Category ◽  
Training Time ◽  
The Cost ◽  
And Training

Dengue cases has become endemic in Malaysia. The cost of operation to exterminate mosquito habitats are also high. To do effective operation, information from community are crucial. But, without knowing the characteristic of Aedes larvae it is hard to recognize the larvae without guide from the expert. The use of deep learning in image classification and recognition is crucial to tackle this problem. The purpose of this project is to conduct a study of characteristics of Aedes larvae and determine the best convolutional neural network model in classifying the mosquito larvae. 3 performance evaluation vector which is accuracy, log-loss and AUC-ROC will be used to measure the model’s individual performance. Then performance category which consist of Accuracy Score, Loss Score, File Size Score and Training Time Score will be used to evaluate which model is the best to be implemented into web application or mobile application. From the score collected for each model, ResNet50 has proved to be the best model in classifying the mosquito larvae species.

scholarly journals Sincronización de datos en el contexto de procesos terapéuticos de rehabilitación física

2019 ◽  
pp. 11-16
Author(s):  
Marco Antonio Ramírez-Hernández ◽  
Randolfo Alberto Santos-Quiroz
Keyword(s):  
Mobile Application ◽  
Web Application ◽  
Personal Information ◽  
Transmission Protocol ◽  
Software Components ◽  
Information Platform ◽  
Training Professionals ◽  
Information Repository ◽  
Rehabilitation Clinic ◽  
And Training

In the development of an information platform focused on the daily operations of a physical rehabilitation clinic, two main software components are currently being worked on for their interaction with users. A web application focused on the operations of labor personnel (specialists and training professionals) and a native mobile application focused on the actions of patients, each of them has the need to interact with the same information repository, analyzing the side of the Patient problems arise from permanent connectivity to the main data through the Internet or some other data transmission protocol, the requirement arises to be able to interact with the generated personal information, which could be achieved by synchronizing data between client-server.

Software Estimation Framework for Mobile Application Projects

2019 ◽  
Vol 7 (2) ◽  
pp. 26-40
Author(s):  
Shailesh Kumar ◽  
Anant R. Koppar
Keyword(s):  
Mobile Devices ◽  
Mobile Application ◽  
Web Application ◽  
Effort Estimation ◽  
Adjustment Factor ◽  
Estimation Model ◽  
Mobile Web ◽  
Software Estimation ◽  
The Cost ◽  
Comprehensive Coverage

As mobile devices are becoming the primary access channels for information, the authors need to have accurate effort estimation model for mobile application projects. In this paper the authors discuss “Mobile application estimation framework” that was designed based on 14 mobile application projects and was validated against 5 mobile application projects. In this paper the authors discuss the estimation framework for both native/hybrid mobile application projects and mobile web application projects. The proposed “Mobile application estimation framework” provides comprehensive coverage for various factors involved in mobile estimation such as layer-wise components, horizontal components and others. The estimation framework also considers the cost drivers and is used as effort adjustment factor. The proposed mobile application estimation framework achieved the MMRE of 0.207 with pred (0.3) of 80%.

scholarly journals Influencing factors analysis in pear disease recognition using deep learning

2020 ◽  
Author(s):  
Fang Yang ◽  
Fuzhong Li ◽  
Kai Zhang ◽  
Wuping Zhang ◽  
Shancang Li
Keyword(s):  
Deep Learning ◽  
Influencing Factors ◽  
Recognition Accuracy ◽  
Sample Collection ◽  
Training Time ◽  
Factors Analysis ◽  
Resolution Scheme ◽  
And Training ◽  
Different Levels ◽  
Pear Disease

AbstractInfluencing factors analysis plays an important role in plant disease identification. This paper explores the key influencing factors and severity recognition of pear diseases using deep learning based on our established pear disease database (PDD2018), which contains 4944 pieces of diseased leaves. Using the deep learning neural networks, including VGG16, Inception V3, ResNet50 and ResNet101, we developed a “DL network + resolution” scheme that can be used in influencing factors analysis and diseases recognition at six different levels. The experimental results demonstrated that the resolution is directly proportional to disease recognition accuracy and training time and the recognition accuracies for pear diseases are up to 99.44%,98.43%, and 97.67% for Septoria piricola (SP), Alternaria alternate (AA), and Gymnosporangium haracannum (GYM), respectively. The results also shown that a forward suggestion on disease sample collection can significantly reduce the false recognition accuracy.

scholarly journals Intelligent recommendation algorithm of mobile application crowdsourcing test based on deep learning

2021 ◽  
Vol 39 (5) ◽  
pp. 1049-1056
Author(s):  
Jing CHENG ◽  
Wei WANG ◽  
Zhengyi SHUAI
Keyword(s):  
Deep Learning ◽  
Mobile Applications ◽  
Learning Ability ◽  
Test Quality ◽  
Recommendation Algorithm ◽  
Test Task ◽  
Training Time ◽  
Deep Feature ◽  
Task Requirements ◽  
And Training

As the functions of mobile applications become more and more complex, the crowdsourcing testing puts higher demands on the professional skills of testers. Therefore, it is an important factor to ensure test quality how to effectively match test task requirements with test personnel's skill level and achieve accurate crowdsourcing test task recommendation. This paper proposes a crowdsourcing test task recommendation algorithm for mobile applications based on deep learning. Firstly, feature analysis is carried out for testing tasks and testers, and feature systems are designed respectively. Second, the resulting characteristic data is used as input data for the Stacked Marginalized Denoising Autoencoder (SMDA). The deep feature data learned from SMDA are combined as the input of Deep Neural Networks (DNN). Finally, the learning ability of DNN is used for prediction. Experimental results show that the proposed algorithm has obvious advantages in both performance and training time compared with CDL and AUTOSVD ++, which verifies the effectiveness of the proposed algorithm. The proposed algorithm can recommend testing tasks to appropriate testers and improve the precision of the algorithm.

scholarly journals Foot Depth Map Point Cloud Completion using Deep Learning with Residual Blocks

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Nolan Lunscher ◽  
John Zelek
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Depth Map ◽  
3D Scanning ◽  
Training Time ◽  
Shoe Size ◽  
Learning Technique ◽  
Footwear Fit ◽  
And Training

Fit is extremely important in footwear as fit largely determines performanceand comfort. Current footwear fit estimation mainly usesonly shoe size, which is extremely limited in characterizing theshape of a foot or the shape of a shoe. 3D scanning presents asolution to this, where a foot shape can be captured and virtuallyfit with shoe models. Traditional 3D scanning techniques have theirown complications however, stemming from their need to collectviews covering all aspects of an object. In this work we explore adeep learning technique to compete a foot scan point cloud frominformation contained in a single depth map view. We examine thebenefits of implementing residual blocks in architectures for this application,and find that they can improve accuracies while reducingmodel size and training time.

scholarly journals P38 COVID-19 impact on Surgical Training And Recovery Planning (COVID-STAR) Survey

BJS Open ◽  
2021 ◽  
Vol 5 (Supplement_1) ◽  
Author(s):  
◽  
Joshua Clements
Keyword(s):  
Surgical Training ◽  
Negative Impact ◽  
Healthcare Delivery ◽  
Cross Sectional Study ◽  
Surgical Trainee ◽  
Surgical Specialty ◽  
Cross Sectional ◽  
Training Time ◽  
Surgical Trainees ◽  
And Training

Abstract Background The COVID-19 pandemic has resulted in dynamic changes to healthcare delivery. Surgery as a specialty has been significantly affected and with that the delivery of surgical training. Method This national, collaborative, cross sectional study comprising 13 surgical trainee associations distributed a pan surgical specialty survey on the COVID-19 impact on surgical training over a 4-week period (11th May - 8th June 2020). The survey was voluntary and open to medical students and surgical trainees of all specialties and training grades. All aspects of training were qualitatively assessed. This study was reported according to STROBE guidelines. Results 810 completed responses were analysed. (M401: F 390) with representation from all deaneries and training grades. 41% of respondents (n = 301) were redeployed with 74% (n = 223) redeployed > 4 weeks. Complete loss of training was reported in elective operating (69.5% n = 474), outpatient activity (67.3%, n = 457), Elective endoscopy (69.5% n = 246) with > 50% reduction in training time reported in emergency operating (48%, n = 326) and completion of work-based assessments (WBA) (46%, n = 309). 81% (n = 551) reported course cancellations and departmental and regional teaching programmes were cancelled without rescheduling in 58% and 60% of cases respectively. A perceived lack of Elective operative exposure and completions of WBA’s were the primary reported factor affecting potential training progression. Overall, > 50% of trainees (n = 377) felt they would not meet the competencies required for that training period. Conclusion This study has demonstrated a perceived negative impact on numerous aspects of surgical training affecting all training specialties and grades.

scholarly journals Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics

2021 ◽  
Vol 7 (3) ◽  
pp. 59
Author(s):  
Yohanna Rodriguez-Ortega ◽  
Dora M. Ballesteros ◽  
Diego Renza
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Training Data ◽  
Video Editing ◽  
Forgery Detection ◽  
Copy Move Forgery Detection ◽  
The Impact ◽  
And Training ◽  
Selection Of ◽  
Traditional Image

With the exponential growth of high-quality fake images in social networks and media, it is necessary to develop recognition algorithms for this type of content. One of the most common types of image and video editing consists of duplicating areas of the image, known as the copy-move technique. Traditional image processing approaches manually look for patterns related to the duplicated content, limiting their use in mass data classification. In contrast, approaches based on deep learning have shown better performance and promising results, but they present generalization problems with a high dependence on training data and the need for appropriate selection of hyperparameters. To overcome this, we propose two approaches that use deep learning, a model by a custom architecture and a model by transfer learning. In each case, the impact of the depth of the network is analyzed in terms of precision (P), recall (R) and F1 score. Additionally, the problem of generalization is addressed with images from eight different open access datasets. Finally, the models are compared in terms of evaluation metrics, and training and inference times. The model by transfer learning of VGG-16 achieves metrics about 10% higher than the model by a custom architecture, however, it requires approximately twice as much inference time as the latter.

Deep Learning-Based Mobile Application Isomorphic GUI Identification for Automated Robotic Testing

IEEE Software ◽  
2020 ◽  
Vol 37 (4) ◽  
pp. 67-74
Author(s):  
Tao Zhang ◽  
Ying Liu ◽  
Jerry Gao ◽  
Li Peng Gao ◽  
Jing Cheng
Keyword(s):  
Deep Learning ◽  
Mobile Application ◽  
Robotic Testing
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