scholarly journals Smartphone-based food recognition system using multiple deep CNN models

2021 ◽  
Author(s):  
Abdulnaser Fakhrou ◽  
Jayakanth Kunhoth ◽  
Somaya Al Maadeed
Keyword(s):  
Low Vision ◽  
Smartphone Application ◽  
Recognition System ◽  
Learning Approach ◽  
Food Recognition ◽  
Multiple State ◽  
Deep Cnn ◽  
Children With Visual Impairments ◽  
State Of Art ◽  
Better Than

AbstractPeople with blindness or low vision utilize mobile assistive tools for various applications such as object recognition, text recognition, etc. Most of the available applications are focused on recognizing generic objects. And they have not addressed the recognition of food dishes and fruit varieties. In this paper, we propose a smartphone-based system for recognizing the food dishes as well as fruits for children with visual impairments. The Smartphone application utilizes a trained deep CNN model for recognizing the food item from the real-time images. Furthermore, we develop a new deep convolutional neural network (CNN) model for food recognition using the fusion of two CNN architectures. The new deep CNN model is developed using the ensemble learning approach. The deep CNN food recognition model is trained on a customized food recognition dataset.The customized food recognition dataset consists of 29 varieties of food dishes and fruits. Moreover, we analyze the performance of multiple state of art deep CNN models for food recognition using the transfer learning approach. The ensemble model performed better than state of art CNN models and achieved a food recognition accuracy of 95.55 % in the customized food dataset. In addition to that, the proposed deep CNN model is evaluated in two publicly available food datasets to display its efficacy for food recognition tasks.

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

Technological Aids for Deaf and Mute in Modern World

2020 ◽  
Vol 14 ◽  
Author(s):  
Vasu Mehra ◽  
Dhiraj Pandey ◽  
Aayush Rastogi ◽  
Aditya Singh ◽  
Harsh Preet Singh
Keyword(s):  
Sign Language ◽  
Gesture Recognition ◽  
Recognition System ◽  
Modern World ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Background Elimination ◽  
Sign Recognition ◽  
Technological Advances ◽  
Better Than

Background:: People suffering from hearing and speaking disabilities have a few ways of communicating with other people. One of these is to communicate through the use of sign language. Objective:: Developing a system for sign language recognition becomes essential for deaf as well as a mute person. The recognition system acts as a translator between a disabled and an able person. This eliminates the hindrances in exchange of ideas. Most of the existing systems are very poorly designed with limited support for the needs of their day to day facilities. Methods:: The proposed system embedded with gesture recognition capability has been introduced here which extracts signs from a video sequence and displays them on screen. On the other hand, a speech to text as well as text to speech system is also introduced to further facilitate the grieved people. To get the best out of human computer relationship, the proposed solution consists of various cutting-edge technologies and Machine Learning based sign recognition models which have been trained by using Tensor Flow and Keras library. Result:: The proposed architecture works better than several gesture recognition techniques like background elimination and conversion to HSV because of sharply defined image provided to the model for classification. The results of testing indicate reliable recognition systems with high accuracy that includes most of the essential and necessary features for any deaf and dumb person in his/her day to day tasks. Conclusion:: It’s the need of current technological advances to develop reliable solutions which can be deployed to assist deaf and dumb people to adjust to normal life. Instead of focusing on a standalone technology, a plethora of them have been introduced in this proposed work. Proposed Sign Recognition System is based on feature extraction and classification. The trained model helps in identification of different gestures.

Contextual Learning Approach and Performance Assessment in Mathematics Learning

2016 ◽  
Vol 3 (3) ◽  
Author(s):  
I Wayan Eka Mahendra
Keyword(s):  
Formative Assessment ◽  
Performance Assessment ◽  
Junior High School ◽  
Mathematics Learning ◽  
Sampling Technique ◽  
Learning Outcome ◽  
Contextual Learning ◽  
Learning Approach ◽  
Better Than ◽  
A Performance

This study aims to determine the effect of formative assessment and learning approach to the mathematics learning outcome after controlling the numerical aptitude. It was a quasi-experiment with a sample of 186 students obtained by using multistage random sampling technique with 2x2 factorial designs. The data were analyzed by ANCOVA. After controlling the numerical aptitude, the results are: the mathematics learning outcome of the students who followed a contextual approach was better than the ones who followed a conventional learning approach, the mathematics learning outcome of the students who were given a performance assessment was better than the ones who were given a conventional assessment, the interaction between the learning approach and formative assessment affected the students learning outcome for mathematics, the students who followed a contextual learning approach were more suitable to be given a performance assessment, whereas the ones who followed a conventional learning approach were more appropriate to be given a conventional assessment. Based on the research findings, junior high school teachers are suggested to improve their students learning outcome for mathematics. Then, teachers need to use a learning approach and formative assessment accurately and correctly. 

The Expanded Core Curriculum Areas Experienced by Campers during Camp Abilities: A Qualitative Study

2021 ◽  
Vol 115 (1) ◽  
pp. 28-41
Author(s):  
Lauren J. Lieberman ◽  
Katie Ericson ◽  
Maria Lepore-Stevens ◽  
Karen Wolffe
Keyword(s):  
Focus Group ◽  
Independent Living ◽  
Core Curriculum ◽  
Low Vision ◽  
Visual Impairments ◽  
Structured Learning ◽  
Focus Group Discussions ◽  
Group Discussions ◽  
Expanded Core Curriculum ◽  
Children With Visual Impairments

Introduction: The expanded core curriculum (ECC) refers to the generally accepted nine areas of instruction that children who are visually impaired (i.e., those who are blind or have low vision) must learn through explicit instruction in order to live independently as adults. Children with visual impairments must experience immersion in the ECC in their daily lives throughout the year rather than only being taught these skills during the school year by teachers of students with visual impairments. Therefore, this research was undertaken to determine whether athletes attending Camp Abilities, a sports camp for children with visual impairments, experienced new ECC skills or practiced previously learned ECC skills and if so, how. Method: Researchers chose to interview 10 athletes from a purposeful sample of 30 children who had previously attended camp. The 10 coaches who worked with these athletes one-on-one participated in focus group discussions at the end of the weeklong camp. In addition, all athletes and coaches attending camp listed their thoughts on posters describing how all athletes attending experienced areas of the ECC. Finally, researchers documented observations of athletes’ opportunities to practice ECC content throughout the weeklong program. Researchers transcribed interviews and focus group discussions and reviewed for themes relating to ECC areas that were part of the students’ lived experience during camp. Results: Three major conclusions emerged from reviews of the interviews, discussion group transcripts, posters, and observations: (1) athletes and coaches were initially unclear about what the ECC areas were and how the athletes experienced the ECC in their everyday academic and home activities; (2) following clarification of the ECC areas, the athletes came to recognize how they learned and applied ECC skills during the camp experience; and (3) a more structured instructional approach to applying the ECC at camp may further enhance their experiences. Discussion: The youth participants were not able to list and describe all of the ECC areas when interviewed at the end of camp. However, adult participants (coaches) listed most ECC areas and described how athletes experienced the ECC during camp in their focus group discussions. Once researchers clarified ECC areas for athletes, they identified self-determination, recreation and leisure, social interaction, and independent living as the areas of the ECC most often experienced during camp. Implications for practitioners: Practitioners need to pay attention to structured learning of the ECC areas and consider articulating for students which areas overlap in their everyday lives, so that they are fully aware of the multiple skills they are acquiring. Camp Abilities is a functional way for youths with visual impairments to experience all areas of the ECC.

scholarly journals OCR with the Deep CNN Model for Ligature Script-Based Languages like Manchu

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Diandian Zhang ◽  
Yan Liu ◽  
Zhuowei Wang ◽  
Depei Wang
Keyword(s):  
Recognition Accuracy ◽  
Sliding Window ◽  
Recognition System ◽  
Text Retrieval ◽  
Text Recognition ◽  
Manual Segmentation ◽  
Low Resource ◽  
Sample Data ◽  
Deep Cnn

Manchu is a low-resource language that is rarely involved in text recognition technology. Because of the combination of typefaces, ordinary text recognition practice requires segmentation before recognition, which affects the recognition accuracy. In this paper, we propose a Manchu text recognition system divided into two parts: text recognition and text retrieval. First, a deep CNN model is used for text recognition, using a sliding window instead of manual segmentation. Second, text retrieval finds similarities within the image and locates the position of the recognized text in the database; this process is described in detail. We conducted comparative experiments on the FAST-NU dataset using different quantities of sample data, as well as comparisons with the latest model. The experiments revealed that the optimal results of the proposed deep CNN model reached 98.84%.

scholarly journals Extreme Learning Machine with sigmoid activation function on large data

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3523-3526
Keyword(s):  
Efficient Algorithm ◽  
Large Data ◽  
Activation Function ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Set ◽  
Learning Machine ◽  
Sigmoid Activation Function ◽  
State Of Art ◽  
Better Than

This paper describes an efficient algorithm for classification in large data set. While many algorithms exist for classification, they are not suitable for larger contents and different data sets. For working with large data sets various ELM algorithms are available in literature. However the existing algorithms using fixed activation function and it may lead deficiency in working with large data. In this paper, we proposed novel ELM comply with sigmoid activation function. The experimental evaluations demonstrate the our ELM-S algorithm is performing better than ELM,SVM and other state of art algorithms on large data sets.

A multi-objective opposition-based barnacles mating optimization for image super resolution using hyper-Spectral images

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Valli Bhasha A. ◽  
Venkatramana Reddy B.D.
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Discrete Wavelet ◽  
Content Type ◽  
Resolution Model ◽  
Deep Cnn ◽  
Image Super Resolution ◽  
Better Than

Purpose The problems of Super resolution are broadly discussed in diverse fields. Rather than the progression toward the super resolution models for real-time images, operating hyperspectral images still remains a challenging problem. Design/methodology/approach This paper aims to develop the enhanced image super-resolution model using “optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT), and Optimized Deep Convolutional Neural Network”. Once after converting the HR images into LR images, the NSSR images are generated by the optimized NSSR. Then the ADWT is used for generating the subbands of both NSSR and HRSB images. The residual image with this information is obtained by the optimized Deep CNN. All the improvements on the algorithms are done by the Opposition-based Barnacles Mating Optimization (O-BMO), with the objective of attaining the multi-objective function concerning the “Peak Signal-to-Noise Ratio (PSNR), and Structural similarity (SSIM) index”. Extensive analysis on benchmark hyperspectral image datasets shows that the proposed model achieves superior performance over typical other existing super-resolution models. Findings From the analysis, the overall analysis of the suggested and the conventional super resolution models relies that the PSNR of the improved O-BMO-(NSSR+DWT+CNN) was 38.8% better than bicubic, 11% better than NSSR, 16.7% better than DWT+CNN, 1.3% better than NSSR+DWT+CNN, and 0.5% better than NSSR+FF-SHO-(DWT+CNN). Hence, it has been confirmed that the developed O-BMO-(NSSR+DWT+CNN) is performing well in converting LR images to HR images. Originality/value This paper adopts a latest optimization algorithm called O-BMO with optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT) and Optimized Deep Convolutional Neural Network for developing the enhanced image super-resolution model. This is the first work that uses O-BMO-based Deep CNN for image super-resolution model enhancement.

Assistive Technology based Solution for Hearing Impairment using Smartphone

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Gesture Recognition ◽  
Video Sequence ◽  
Recognition System ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Background Elimination ◽  
Sign Recognition ◽  
Limited Support ◽  
Recognition Capability ◽  
Better Than

Developing a system for sign language recognition becomes essential for the deaf as well as a mute person. The recognition system acts as a translator between a disabled and an able person. This eliminates the hindrances in the exchange of ideas. Most of the existing systems are very poorly designed with limited support for the needs of their day to day facilities. The proposed system embedded with gesture recognition capability has been introduced here which extracts signs from a video sequence and displays them on screen. On the other hand, a speech to text as well as text to speech system is also introduced to further facilitate the grieved people. To get the best out of a human-computer relationship, the proposed solution consists of various cutting-edge technologies and Machine Learning based sign recognition models that have been trained by using TensorFlow and Keras library. The proposed architecture works better than several gesture recognition techniques like background elimination and conversion to HSV

The Effect of Brain Based Learning on Improving Students Critical Thinking Ability and Self Regulated

2020 ◽  
Vol 2 (2) ◽  
pp. 162
Author(s):  
Sarifah Sari Maryati ◽  
Irma Purwanti ◽  
Melinda Putri Mubarika
Keyword(s):  
Critical Thinking ◽  
Thinking Skills ◽  
Critical Thinking Skills ◽  
Learning Approach ◽  
School Students ◽  
Critical Thinking Ability ◽  
Thinking Ability ◽  
Brain Based Learning ◽  
The Brain ◽  
Better Than

This research is motivated by the low ability of mathematical critical thinking and Self Regulated Cimahi 10 Public Middle School students, so that a learning approach is needed to overcome these problems. The alternative approach applied is the Brain Based Learning Model approach.The objectives of this researcher are: 1) to examine students' mathematical critical thinking skills; 2) reviewing the Self Regulated attitude of students who obtain Brain Based Learning learning with students who have expository learning; 3) examine there is a positive correlation between Critical Thinking with Self Regulated students who obtain Brain Based Learning and expository learning. The population in this study was grade VII students of SMP Negeri 10 Cimahi. The samples in this study were class VII-B (Brain Based Learning) and class VII-D (expository). The instruments used in this study were the Critical Thinking test and the student's Self Regulated questionnaire. The test used is a subjective type test (description). The way to analyze data is with IBM SPSS Statistics 18.0 for Windows. The results showed that: 1) the mathematical critical thinking ability of students who obtained learning based on the Brain Based Learning approach was better than students who gained expository learning; 2) Self Regulated  attitude, students who get Brain Based Learning are better than students who get expository approach learning; 3) there is no correlation between critical thinking with Self Regulated students who obtain Brain Based Learning and expository learning.

Visual Attention for Behavioral Biometric Systems

2010 ◽  
pp. 290-316 ◽  
Author(s):  
Concetto Spampinato
Keyword(s):  
Computational Models ◽  
Careful Analysis ◽  
Recognition System ◽  
Biometric System ◽  
The Third ◽  
Morphological Aspects ◽  
The People ◽  
Face Recognition System ◽  
Biometric Systems ◽  
State Of Art

The chapter is so articulated: the first section will tackle the state of art of the attention theory, with the third paragraph related to the computational models that implement the attention theories, with a particular focus on the model that is the basis for the proposed biometric systems. Such an algorithm will be used for describing the first biometric system. The following section will tackle the people recognition algorithms carried out by evaluating the FOAs distribution. In detail, two different systems are proposed: 1) a face recognition system that takes into account both the behavioral and morphological aspects, and 2) a pure behavioral biometric system that recognizes people according to their actions evaluated by a careful analysis of the extracted FOAs.

Sign in / Sign up

Export Citation Format

Share Document