scholarly journals Identification of Microcontroller Based Objects Using Image Classification in Python

2021 ◽  
Vol 9 (9) ◽  
pp. 87-92
Author(s):  
Zenith Nandy
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Classification ◽  
Programming Language ◽  
Multiple Testing ◽  
Activation Function ◽  
Convolution Neural Network ◽  
Output Layer ◽  
Deep Learning Neural Network ◽  
Percent Accuracy

Abstract: In this paper, I built an AI model using deep learning, which identifies whether a given image is of an Arduino, a Beaglebone Black or a Jetson Nano. The identification of the object is based on prediction. The model is trained using 300 to 350 datasets of each category and is tested multiple times using different images at different angles, background colour and size. After multiple testing, the model is found to have 95 percent accuracy. Model used is Sequential and uses Convolution Neural Network (CNN) as its architecture. The activation function of each layer is RELU and for the output layer is Softmax. The output is a prediction and hence it is of probability type. This is a type of an application based project. The entire scripting is done using Python 3 programming language. Keywords: image classification, microcontroller boards, python, AI, deep learning, neural network

scholarly journals Deep Learning-Based X-Ray Baggage Hazardous Object Detection – An FPGA Implementation

2021 ◽  
Vol 35 (5) ◽  
pp. 431-435
Author(s):  
Vijayakumar Ponnusamy ◽  
Diwakar R. Marur ◽  
Deepa Dhanaskodi ◽  
Thangavel Palaniappan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Classification ◽  
Real Time ◽  
Activation Function ◽  
Data Representation ◽  
Fpga Implementation ◽  
X Ray ◽  
Computationally Intensive ◽  
Deep Learning Neural Network

This work proposes deep learning neural network-based X-ray image classification. The X-ray baggage scanning machinery plays an essential role in the safeguard of customs, airports, and other systematically very important landmarks and infrastructures. The technology at present of baggage scanning machines is designed on X-ray attenuation. The detection of threatful objects is built on how different objects attenuate the X-ray beams going through them. In this paper, the deep convolutional neural network of YOLO is utilized in classifying baggage images. Real-time performance of the baggage image classification is an essential one for security scanning. There are many computationally intensive operations in the You Only Look Once (YOLO) architecture. The computational intensive operations are implemented in the Field Programmable Gate Array (FPGA) platform to optimize process delays. The critical issues involved in those implementations include data representation, inner products computation and implementation of activation function and resolving these issues will also be a significant task. The FPGA implementation results show that with less resource occupancy, the YOLO implementation provides maximum accuracy of 98.9% in classifying X-ray baggage images and identifying hazardous materials. This result proves that the proposed implementation is best suited for practical system deployments for real-time Baggage scanning.

scholarly journals Bare Skin Image Classification using Convolution Neural Network

2022 ◽  
Vol 12 (1) ◽  
pp. 138-145
Author(s):  
Jaya Gupta ◽  
◽  
Sunil Pathak ◽  
Gireesh Kumar
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Satellite Image ◽  
Activation Function ◽  
Convolution Neural Network ◽  
Classification Model ◽  
Significant Research ◽  
Research Problems ◽  
Facial Expression Classification ◽  
Expression Classification

Image classification is critical and significant research problems in computer vision applications such as facial expression classification, satellite image classification, and plant classification based on images. Here in the paper, the image classification model is applied for identifying the display of daunting pictures on the internet. The proposed model uses Convolution neural network to identify these images and filter them through different blocks of the network, so that it can be classified accurately. The model will work as an extension to the web browser and will work on all websites when activated. The extension will be blurring the images and deactivating the links on web pages. This means that it will scan the entire web page and find all the daunting images present on that page. Then we will blur those images before they are loaded and the children could see them. Keywords— Activation Function, CNN, Images Classification , Optimizers, VGG-19

scholarly journals Park Assist: A Deep Convolution Neural Network based Parking Assistant system

2020 ◽  
Vol 8 (6) ◽  
pp. 14-18
Author(s):  
Sukhdeep Sharma ◽  
Aayushya ‎ ◽  
Dr. Madhumita Kathuria ◽  
Pronika Chawla
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Classification ◽  
Convolution Neural Network ◽  
The Other ◽  
Human Labour ◽  
Deep Convolution Neural Network ◽  
Classification Tasks ◽  
Parking Facilities

With the proliferation in number of vehicles an unnoticeable problem regarding parking of these vehicles has emerged in places like shopping complexes where current car parking facilities are incapable of managing the parking of vehicles without human labour . Even in current automated PGI’s human labour is required in some or the other way . Motivated by the affordable and remarkable performance of Convolutional Nueral Network in various image classification tasks, this paper presents a review on the automated parking systems based on the CNN technique . The classifier are trained and tested by deep learning of nueral network thus using of PHP and HTML to create the UI and knowledge of MySQL to create a database to store information about vehicles .Similarly by converting the process into three small procedures we will be able to evaluate the bill in accordance to the timestamp of the parked vehicle without the use of human efforts.

scholarly journals Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network

2021 ◽  
Vol 18 (2(Suppl.)) ◽  
pp. 0925
Author(s):  
Asroni Asroni ◽  
Ku Ruhana Ku-Mahamud ◽  
Cahya Damarjati ◽  
Hasan Basri Slamat
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolution Neural Network ◽  
Classification Model ◽  
Mel Frequency Cepstral Coefficients ◽  
Speech Classification ◽  
Deep Learning Neural Network ◽  
Voice Data ◽  
The Voice

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to evaluate the pronunciation of the Arabic alphabet. Voice data from six school children are recorded and used to test the performance of the proposed method. The padding technique has been used to augment the voice data before feeding the data to the CNN structure to developed the classification model. In addition, three other feature extraction techniques have been introduced to enable the comparison of the proposed method which employs padding technique. The performance of the proposed method with padding technique is at par with the spectrogram but better than mel-spectrogram and mel-frequency cepstral coefficients. Results also show that the proposed method was able to distinguish the Arabic alphabets that are difficult to pronounce. The proposed method with padding technique may be extended to address other voice pronunciation ability other than the Arabic alphabets.

scholarly journals Recognition of Cursive Pashto Optical Digits and Characters with Trio Deep Learning Neural Network Models

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2508
Author(s):  
Muhammad Zubair Rehman ◽  
Nazri Mohd. Nawi ◽  
Mohammad Arshad ◽  
Abdullah Khan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Models ◽  
The Other ◽  
Neural Network Models ◽  
The World ◽  
Deep Cnn ◽  
Deep Learning Neural Network ◽  
Percent Accuracy

Pashto is one of the most ancient and historical languages in the world and is spoken in Pakistan and Afghanistan. Various languages like Urdu, English, Chinese, and Japanese have OCR applications, but very little work has been conducted on the Pashto language in this perspective. It becomes more difficult for OCR applications to recognize handwritten characters and digits, because handwriting is influenced by the writer’s hand dynamics. Moreover, there was no publicly available dataset for handwritten Pashto digits before this study. Due to this, there was no work performed on the recognition of Pashto handwritten digits and characters combined. To achieve this objective, a dataset of Pashto handwritten digits consisting of 60,000 images was created. The trio deep learning Convolutional Neural Network, i.e., CNN, LeNet, and Deep CNN were trained and tested with both Pashto handwritten characters and digits datasets. From the simulations, the Deep CNN achieved 99.42 percent accuracy for Pashto handwritten digits, 99.17 percent accuracy for handwritten characters, and 70.65 percent accuracy for combined digits and characters. Similarly, LeNet and CNN models achieved slightly less accuracies (LeNet; 98.82, 99.15, and 69.82 percent and CNN; 98.30, 98.74, and 66.53 percent) for Pashto handwritten digits, Pashto characters, and the combined Pashto digits and characters recognition datasets, respectively. Based on these results, the Deep CNN model is the best model in terms of accuracy and loss as compared to the other two models.

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