scholarly journals Classification of Rigid and Non-Rigid Objects Using CNN

2021 ◽  
Vol 35 (4) ◽  
pp. 341-347
Author(s):  
Aparna Gullapelly ◽  
Barnali Gupta Banik
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Moving Objects ◽  
Binary Classification ◽  
High Accuracy ◽  
Learning System ◽  
Data Set ◽  
Rigid Objects ◽  
Soft Objects

Classifying moving objects in video surveillance can be difficult, and it is challenging to classify hard and soft objects with high Accuracy. Here rigid and non-rigid objects are limited to vehicles and people. CNN is used for the binary classification of rigid and non-rigid objects. A deep-learning system using convolutional neural networks was trained using python and categorized according to their appearance. The classification is supplemented by the use of a data set, which contains two classes of images that are both rigid and not rigid that differ by illuminations.

Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Patrick Beyersdorffer ◽  
Wolfgang Kunert ◽  
Kai Jansen ◽  
Johanna Miller ◽  
Peter Wilhelm ◽  
...  
Keyword(s):  
Neural Networks ◽  
Adverse Events ◽  
Convolutional Neural Networks ◽  
Surgical Training ◽  
Binary Classification ◽  
Image Data ◽  
Training Image ◽  
Training Environment ◽  
Data Set

Abstract Uncontrolled movements of laparoscopic instruments can lead to inadvertent injury of adjacent structures. The risk becomes evident when the dissecting instrument is located outside the field of view of the laparoscopic camera. Technical solutions to ensure patient safety are appreciated. The present work evaluated the feasibility of an automated binary classification of laparoscopic image data using Convolutional Neural Networks (CNN) to determine whether the dissecting instrument is located within the laparoscopic image section. A unique record of images was generated from six laparoscopic cholecystectomies in a surgical training environment to configure and train the CNN. By using a temporary version of the neural network, the annotation of the training image files could be automated and accelerated. A combination of oversampling and selective data augmentation was used to enlarge the fully labeled image data set and prevent loss of accuracy due to imbalanced class volumes. Subsequently the same approach was applied to the comprehensive, fully annotated Cholec80 database. The described process led to the generation of extensive and balanced training image data sets. The performance of the CNN-based binary classifiers was evaluated on separate test records from both databases. On our recorded data, an accuracy of 0.88 with regard to the safety-relevant classification was achieved. The subsequent evaluation on the Cholec80 data set yielded an accuracy of 0.84. The presented results demonstrate the feasibility of a binary classification of laparoscopic image data for the detection of adverse events in a surgical training environment using a specifically configured CNN architecture.

scholarly journals Classification of Coffee and Wine with a Microwave Resonator and Deep Learning Machine Technique

2020 ◽  
Author(s):  
Hyo Bong Hong ◽  
Jae-Chan Jeong ◽  
Hans Joachim Krause
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
High Accuracy ◽  
Complex Compounds ◽  
Learning System ◽  
Microwave Resonator ◽  
Microwave Resonance ◽  
Peak Assignment ◽  
Learning Machine

In this study, coffee and wine were measured using an microwave resonator, and a deep learning system was trained using the acquired data, and then tested to see if the deep leaning system could distinguish these samples. We tested 6 kinds of wine, 6 kinds of cold brew coffee and 6 kinds of bottled coffee. The microwave resonance spectra of all samples were graphically displayed. The graphical images were processed by an artificial intelligence (AI) technique. By applying deep learning machine technique instead of the peak assignment for complex compounds in general, it was possible to facilitate the classification of coffee or wine with high accuracy.

scholarly journals FETCH: A deep-learning based classifier for fast transient classification

2020 ◽  
Vol 497 (2) ◽  
pp. 1661-1674 ◽  
Author(s):  
Devansh Agarwal ◽  
Kshitij Aggarwal ◽  
Sarah Burke-Spolaor ◽  
Duncan R Lorimer ◽  
Nathaniel Garver-Daniels
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Radio Frequency ◽  
Real Time ◽  
Machine Learning Algorithms ◽  
Dispersion Measure ◽  
Radio Frequency Interference ◽  
Data Set ◽  
Python Package

ABSTRACT With the upcoming commensal surveys for Fast Radio Bursts (FRBs), and their high candidate rate, usage of machine learning algorithms for candidate classification is a necessity. Such algorithms will also play a pivotal role in sending real-time triggers for prompt follow-ups with other instruments. In this paper, we have used the technique of Transfer Learning to train the state-of-the-art deep neural networks for classification of FRB and Radio Frequency Interference (RFI) candidates. These are convolutional neural networks which work on radio frequency-time and dispersion measure-time images as the inputs. We trained these networks using simulated FRBs and real RFI candidates from telescopes at the Green Bank Observatory. We present 11 deep learning models, each with an accuracy and recall above 99.5 per cent on our test data set comprising of real RFI and pulsar candidates. As we demonstrate, these algorithms are telescope and frequency agnostic and are able to detect all FRBs with signal-to-noise ratios above 10 in ASKAP and Parkes data. We also provide an open-source python package fetch (Fast Extragalactic Transient Candidate Hunter) for classification of candidates, using our models. Using fetch, these models can be deployed along with any commensal search pipeline for real-time candidate classification.

scholarly journals Classification of Coffee and Wine with a Microwave Resonator and Deep Learning Machine Technique

2020 ◽  
Author(s):  
Hyo Bong Hong ◽  
Jae-Chan Jeong ◽  
Hans Joachim Krause
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
High Accuracy ◽  
Complex Compounds ◽  
Learning System ◽  
Microwave Resonator ◽  
Microwave Resonance ◽  
Peak Assignment ◽  
Learning Machine

In this study, coffee and wine were measured using an microwave resonator, and a deep learning system was trained using the acquired data, and then tested to see if the deep leaning system could distinguish these samples. We tested 6 kinds of wine, 6 kinds of cold brew coffee and 6 kinds of bottled coffee. The microwave resonance spectra of all samples were graphically displayed. The graphical images were processed by an artificial intelligence (AI) technique. By applying deep learning machine technique instead of the peak assignment for complex compounds in general, it was possible to facilitate the classification of coffee or wine with high accuracy.

scholarly journals A generalized quadratic loss for SVM and Deep Learning

2021 ◽  
Author(s):  
filippo portera
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Binary Classification ◽  
Multiple Kernel Learning ◽  
Problem Formulation ◽  
Point Of View ◽  
Learning Problems ◽  
Quadratic Loss ◽  
Data Set ◽  
Primal And Dual

We consider some supervised binary classification tasks and a regression task, whereas SVM and Deep Learning, at present, exhibitthe best generalization performances. We extend the work [3] on a gen-eralized quadratic loss for learning problems that examines pattern cor-relations in order to concentrate the learning problem into input spaceregions where patterns are more densely distributed. From a shallowmethods point of view (e.g.: SVM), since the following mathematicalderivation of problem (9) in [3] is incorrect, we restart from problem (8)in [3] and we try to solve it with one procedure that iterates over the dualvariables until the primal and dual objective functions converge. In ad-dition we propose another algorithm that tries to solve the classificationproblem directly from the primal problem formulation. We make alsouse of Multiple Kernel Learning to improve generalization performances.Moreover, we introduce for the first time a custom loss that takes in con-sideration pattern correlation for a shallow and a Deep Learning task.We propose some pattern selection criteria and the results on 4 UCIdata-sets for the SVM method. We also report the results on a largerbinary classification data-set based on Twitter, again drawn from UCI,combined with shallow Learning Neural Networks, with and without thegeneralized quadratic loss. At last, we test our loss with a Deep NeuralNetwork within a larger regression task taken from UCI. We comparethe results of our optimizers with the well known solver SVMlightandwith Keras Multi-Layers Neural Networks with standard losses and witha parameterized generalized quadratic loss, and we obtain comparable results.

scholarly journals Financial Information Asymmetry: Using Deep Learning Algorithms to Predict Financial Distress

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 443
Author(s):  
Chyan-long Jan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Information Asymmetry ◽  
Error Rate ◽  
Financial Distress ◽  
Prediction Models ◽  
High Accuracy ◽  
Financial Information ◽  
Financial Distress Prediction ◽  
Distress Prediction

Because of the financial information asymmetry, the stakeholders usually do not know a company’s real financial condition until financial distress occurs. Financial distress not only influences a company’s operational sustainability and damages the rights and interests of its stakeholders, it may also harm the national economy and society; hence, it is very important to build high-accuracy financial distress prediction models. The purpose of this study is to build high-accuracy and effective financial distress prediction models by two representative deep learning algorithms: Deep neural networks (DNN) and convolutional neural networks (CNN). In addition, important variables are selected by the chi-squared automatic interaction detector (CHAID). In this study, the data of Taiwan’s listed and OTC sample companies are taken from the Taiwan Economic Journal (TEJ) database during the period from 2000 to 2019, including 86 companies in financial distress and 258 not in financial distress, for a total of 344 companies. According to the empirical results, with the important variables selected by CHAID and modeling by CNN, the CHAID-CNN model has the highest financial distress prediction accuracy rate of 94.23%, and the lowest type I error rate and type II error rate, which are 0.96% and 4.81%, respectively.

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

scholarly journals Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chiaki Kuwada ◽  
Yoshiko Ariji ◽  
Yoshitaka Kise ◽  
Takuma Funakoshi ◽  
Motoki Fukuda ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cleft Palate ◽  
Panoramic Radiography ◽  
False Positive Rate ◽  
Normal Subjects ◽  
Learning System ◽  
Panoramic Radiographs ◽  
Positive Rate ◽  
Aided Diagnosis

AbstractAlthough panoramic radiography has a role in the examination of patients with cleft alveolus (CA), its appearances is sometimes difficult to interpret. The aims of this study were to develop a computer-aided diagnosis system for diagnosing the CA status on panoramic radiographs using a deep learning object detection technique with and without normal data in the learning process, to verify its performance in comparison to human observers, and to clarify some characteristic appearances probably related to the performance. The panoramic radiographs of 383 CA patients with cleft palate (CA with CP) or without cleft palate (CA only) and 210 patients without CA (normal) were used to create two models on the DetectNet. The models 1 and 2 were developed based on the data without and with normal subjects, respectively, to detect the CAs and classify them into with or without CP. The model 2 reduced the false positive rate (1/30) compared to the model 1 (12/30). The overall accuracy of Model 2 was higher than Model 1 and human observers. The model created in this study appeared to have the potential to detect and classify CAs on panoramic radiographs, and might be useful to assist the human observers.

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