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Data Science Senior Specialist | PwC Bulgaria | Обяви

Data Science Senior Specialist | PwC Bulgaria | Обяви
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United States , United Kingdom , Risk Assurance Services , Recurrent Neural Network , Term Memory Network , Convolutional Neural Network , Data Science , Eastern European , Central Europe , North America , Linear Regression , Generalized Linear Model , Support Vector Machine , Nearest Neighbors , Density Estimation , Means Clustering , Gaussian Mixture Model , Random Forest , Stacking Regressor , Deep Learning , Multilayer Perceptron , Long Short Term Memory Network ,

"Intelligent waste classification approach based on improved multi-laye" by Megha Chhabra, Bhagwati Sharan et al.

This study aims to improve the performance of organic to recyclable waste through deep learning techniques. Negative impacts on environmental and Social development have been observed relating to the poor waste segregation schemes. Separating organic waste from recyclable waste can lead to a faster and more effective recycling process. Manual waste classification is a time-consuming, costly, and less accurate recycling process. Automated segregation in the proposed work uses Improved Deep Convolutional Neural Network (DCNN). The dataset of 2 class category with 25077 images is divided into 70% training and 30% testing images. The performance metrics used are classification Accuracy, Missed Detection Rate (MDR), and False Detection Rate (FDR). The results of Improved DCNN are compared with VGG16, VGG19, MobileNetV2, DenseNet121, and EfficientNetB0 after transfer learning. Experimental results show that the image classification accuracy of the proposed model reaches 93.28%. ....

Convolutional Neural Network , Improved Deep Convolutional Neural Network , Missed Detection Rate , False Detection Rate , Deep Learning , Neural Network , Waste Classification ,

"Modeling of Controller for Motor-Controlled Prosthetic Hand Based on M" by Salina Mohmad and Abdalrahman Khaled Elnagar

This research study presents a computationally improved system using a pattern recognition (PR) algorithm to classify fingers movement based on data acquired by a surface EMG (sEMG) sensor when muscle is contracting. Ten subjects were involved in this investigation where the forearm’s muscle activities were acquired using two-channel sEMG placed at the flexor digitorum superficialis and extensor digitorum muscles. The focus of this study is to integrate the sensor with servo motors to control the movement of artificial limbs or prosthetics based on which muscle is at work. The work involved signal processing on raw sEMG signals, followed by multiple time domain feature extraction (TD). sEMG signal is then segmented using an overlapping window of size 250 ms and increments of 50 ms. The feature extraction was used to build up the convolutional neural network (CNN) which is used to train the classes of fingers movement. The dataset was split into 80% for training and 20% for testing th ....

Convolutional Neural Network , Urface Emg ,

Artificial Intelligence With Machine Learning, Deep Learning

Artificial Intelligence With Machine Learning, Deep Learning
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How to Detect Duplicate Document Images

The article talks about how to use JavaScript to detect duplicate document images. ....

Convolutional Neural Network , Detect Duplicate Document , Dynamsoft Document Normalizer ,