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E-Health and Bioengineering Conference (EHB)

作者： Adrian Ciobanu Mihaela Luca Radu Vulpoi Oana Barboi Vasile Drug Institute of Computer Science Romanian Academy - Iasi Branch Iasi Romania University of Medicine and Pharmacy “Gr.T. Popa” Iasi Institute of Gastroenterology and Hepatology Iasi Iasi Romania

ISBN: (数字)9781665485579

ISBN: (纸本)9781665485586

deep learning in automatic video colonoscopy processing may result in missing small polyps or detecting them with low confidence. We conducted a study to demonstrate that we can overcome such problems by fitting the dimensions of the input image to the dimensions of the input layer of a trained Mobilenet deep learning network and by grouping polyps in classes according to their size. The results show that it is possible to substantially improve the confidence rates and we propose a scheme to increase small polyps detection in real-time video colonoscopies based on two trained Mobilenet networks.

关键词： deep learning Fitting Colonoscopy Streaming media real-time systems Biomedical engineering

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Towards real time Interpretable Object Detection for UAV Platform by Saliency Maps

Towards Real Time Interpretable Object Detection for UAV Pla...

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IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO)

作者： Hogan, Maxwell Aouf, Nabil Univ London Dept Elect & Elect Engn London England Univ London Robot & Autonomous Syst Dept Elect & Elect Engn London England

ISBN: (纸本)9781665405355

On-board object detection is an important requirement for Unmanned Aerial Vehicles (UAVs) when carrying out a variety of tasks such as obstacle avoidance, search and rescue, and automatic target recognition. One of the main difficulties of conducting object detection for a UAV is that, because the objects of interest are observed from altitude, this causes them to appear very small in images acquired from an onboard camera. In this work, we attempt to provide a solution to this difficulty, while also seeking to meet two other criteria that are important to the deployment of Artificial Intelligence with UAVs: firstly, the capability to operate in real-time;and secondly, the ability of the user to be able to trust the predictions it makes. To meet the challenge of small object detection we present the use of an image Tile Loader to enhance the capability of deep Neural Network (DNN) style detectors, while minimising the processing time costs. Furthermore, we also introduce the practice of using Grad-CAM to provide better insights into a detection style architecture as a means to enhance trustworthiness.

关键词： UAV Object Detection deep learning Explainable AI

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Object Detection in Unmanned Aerial Vehicle Camera Stream Using deep Neural Network 14

Object Detection in Unmanned Aerial Vehicle Camera Stream Us...

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14th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2022

作者： Kiac, Martin Riha, Kamil Prinosil, Jiri Mrnustik, Jiri Brno University of Technology Dept. of Telecommunications Brno Czech Republic Freehorizons S.r.o. Brno Czech Republic

ISBN: (数字)9798350398663

ISBN: (纸本)9798350398663

Nowadays, the world is experiencing an increasing boom in applications of artificial intelligence, especially deep learning. This is more and more used in many areas such as industry, medicine and security systems, etc. This article deals with object detection from Unmanned Aerial Vehicle (UAV) perspective. The whole system uses one camera, which is suitably positioned on the UAV to capture the scene. image processing and subsequent object detection using the YOLOv4 model are performed on the Jetson Nano device. The device itself is relatively powerful, but to save the computing power of the device, the YOLOv4 neural network model was modified. The YOLOv4 model was trained on our own dataset. This training set was created specifically for UAV applications. The result of this work is a learned YOLOv4 neural network model designed for UAVs with regard to the used training set. The modified network model is also able to run in real-time and save computing power for possibly other UAV operations. All materials, dataset and scripts used in this work, are available at https://***/KicoSVK/object-detection-in-uav-using-yolov4. © 2022 IEEE.

关键词： Object detection

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deep learning-Based Bottle Caps Inspection in Beverage Manufacturing and Packaging Process 9

Deep Learning-Based Bottle Caps Inspection in Beverage Manuf...

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9th International Electrical Engineering Congress, iEECON 2021

作者： Horputra, Pinkaew Phrajonthong, Rateepat Kaewprapha, Phisan Thammasat University Department of Electrical and Computer Engineering Khlong Neung12120 Thailand

ISBN: (纸本)9781728195841

In the beverage manufacturing industry-the process of closing and sealing the bottle caps by an automated machine could cause defects due to imperfect equipment alignment. Small businesses rely on humans as a means to perform quality control, which is prone to errors due to long working hours. In this paper, we propose our real-time deep learning-based technique to automatically detect and classify bottle caps into 2 categories: good and defective. Good bottle caps should be aligned perfectly with bottle rings and close to the bottlenecks while defective bottle caps include loose caps, tilted caps, and missing caps. Our approach consists of combining image processing with Convolutional Neural Network. This machine learning approach is versatile, allowing the system to work even with different light and background settings. The system is suitable to perform at low to moderate conveyer speed capable of processing 24 frames per second with 96.40% overall accuracy. © 2021 IEEE.

关键词： Bottles

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RGB+D and deep learning-based real-time detection of suspicious event in Bank-ATMs

RGB+D and deep learning-based real-time detection of suspici...

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作者： Khaire, Pushpajit A. Kumar, Praveen Department of Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur India

real-time detection of human activities has become very important in terms of surveillance and security of Bank-Automated Teller Machines (ATMs), public offices because of the day-to-day increase in criminal activities. The current way of monitoring such constrained environments is done through monocular CCTV cameras which capture only RGB video. The RGB+D sensor provides depth data of the scene in addition to RGB data. To address the problem of online detection of abnormal activities in Bank ATMs, we propose a supervised deep learning framework based on multi-stream CNNs and RGB+D sensor. From the online video stream of RGB+D data, motion templates are created from RGB and depth video segments and then trained on CNNs to detect a suspicious event in ongoing activity. Moreover, due to the unavailability of any dataset for analyzing human activities in ATMs, we also contributed a novel RGB+D dataset in this paper. The proposed deep learning-based framework is evaluated on qualitative and quantitative statistical evaluation parameters and detect suspicious event with the precision of 0.932 and accuracy of 94.2%. Detailed statistical analysis of results shows that the proposed framework can detect the suspicious event in a real-time online manner before the abnormal activity gets completed. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： deep learning

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deep learning Based image Classification for Automated Face Spoofing Detection Using Machine learning: Convolutional Neural Network

Deep Learning Based Image Classification for Automated Face ...

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Communication & Computing (WCONF), World Conference on

作者： Biresh Kumar Kumari Manisha Anurag Sinha Abhishek Kumar Jeevan Kumar Amity Institute of Information Technology Amity University Ranchi Jharkhand India School of Computing and information Science IGNOU New Delhi India Computer Science and Engineering Jharkhand University of Technology Ranchi India RVS College of Engineering & Technology RVSCET Jamshedpur India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Facial recognition technology has gained widespread use in various applications, raising concerns about the weakness of frameworks to confront mocking assaults. This study presents an implementation of face spoofing detection using machine learning techniques. The exploration utilizes a far-reaching system that envelops data combination, preprocessing, incorporate extraction, and model readiness. A diverse dataset comprising genuine and spoofed facial images, representing various spoofing techniques, is utilized. Feature extraction leverages Convolutional Brain Organizations (CNNs) to catch discriminative facial elements. The selected machine learning model is trained and fine-tuned, with a focus on achieving robustness against evolving spoofing methods. The evaluation of the implemented system involves rigorous testing on a separate dataset, utilizing estimations like precision, exactness, survey, and F1-score. The study investigates post-processing techniques and considerations for real-time deployment, ensuring practical applicability is done by the method convolutional neural network (CNN). Cross-approval is performed to evaluate the model's speculation capacities, and the deployment phase explores integration into real-world scenarios. Ethical considerations, user feedback, and compliance with data privacy regulations are integral components of the study.

关键词： Surveys Technological innovation Face recognition Feature extraction Brain modeling Robustness Regulation Convolutional neural networks Security Testing

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Wireless Capsule Endoscopy Bleeding images Classification Using CNN Based Model

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IEEE ACCESS 2021年 9卷 33675-33688页

作者： Rustam, Furqan Siddique, Muhammad Abubakar Siddiqui, Hafeez Ur Rehman Ullah, Saleem Mehmood, Arif Ashraf, Imran Choi, Gyu Sang Khwaja Fareed Univ Engn & Informat Technol Dept Comp Sci Rahim Yar Khan 64200 Pakistan Islamia Univ Bahawalpur Dept Comp Sci & IT Punjab 63100 Pakistan Yeungnam Univ Dept Informat & Commun Engn Gyeongbuk 38541 South Korea

Wireless capsule endoscopy (WCE) is an efficient tool to investigate gastrointestinal tract disorders and perform painless imaging of the intestine. Despite that, several concerns make its wide applicability and adaptation challenging like efficacy, tolerance, safety, and performance. Besides, automatic analysis of the WCE provided dataset is of great importance for detecting abnormalities. Imaging of the patient's digestive tract through WCE produces a large dataset that requires a substantial amount of time and a special skill set from a medical practitioner for analysis. Several computer-aided and vision-based solutions have been proposed to resolve these issues, yet, they do not provide the desired level of accuracy and further improvements are still needed. The current study aims to devise a system that can perform the task of automatic analysis of WCE images to identify abnormalities and assist practitioners for robust diagnosis. This study adopts a deep neural network approach and proposes a model name BIR (bleedy image recognizer) that combines the MobileNet with a custom-built convolutional neural network (CNN) model to classify WCE bleedy images. BIR uses the MobileNet model for initial-level computation for its lower computation power requirement and subsequently the output is fed to the CNN for further processing. A dataset of 1650 WCE images is used to train and test the model using the measures of accuracy, precision, recall, F1 score, and Cohen's kappa to evaluate the performance of the BIR. Results indicate the promising outcomes with achieved accuracy, precision, recall, F1 score, and Cohen's kappa of 0.993, 1.000, 0.994, 0.997, and 0.995 respectively. The accuracy of the BIR model is 0.978 with the Google collected WCE image dataset which is better than the state-of-art approaches.

关键词： Hemorrhaging Gastrointestinal tract Medical diagnostic imaging Endoscopes Feature extraction deep learning Wireless communication Wireless capsule endoscopy deep learning computer vision gastrointestinal tract infection classification convolutional neural networks

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Optimizing Length of Stay Prediction After Intubation: An Advanced Machine learning Model with real-time Vital Sign Integration

Optimizing Length of Stay Prediction After Intubation: An Ad...

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International Conference on image Information processing

作者： Amit Sundas Gurpreet Singh Sumit Badotra Amit Verma Balwinder Kaur Department of Computer Science and Engineering Lovely Professional University Phagwara Punjab India School of Computer Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering University Center for Research and Development Chandigarh India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

In the critical care unit, bedside monitors track patients’ vital signs to help physicians make choices. With increased storage and analysis capability, huge datasets may be processed faster. Lately, physiological data have showed promise for identifying specific consequences and occurrences. At the critical care unit. Our method was aimed to Live bedside vital sign data may be automatically retrieved. Devices that can predict patient mechanical ventilation for more than 4 days in a pediatric critical care unit. 284 ICU patients had their vital signs and clinical history documented continuously from admission. 24 hours of artificial breathing in Boston Children’s Hospital’s pediatric medical-surgical critical care unit. They trained many machine learning models using subsets. Assess the likelihood of each person staying long. We examined our systems’ capacity to anticipate outcomes for unknown persons. Adding vital sign data increased model performance to 90% (area under the curve) from >83% when using just vital sign data. static participant clinical data in electronic health records. For research group representative sample reliability, we employed this approach on 300 individually trained tests employing a variety of sets for training and hold-out validation. Other deep learning methods failed to forecast like ours. Gaining equivalent work outcomes prediction skills. We may be able to help create scalable ICU-based prediction systems that leverage real-time patient monitor data.

关键词： Training Pediatrics Predictive models Ventilation real-time systems Data models Reliability

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Inverse Quantum Fourier Transform Inspired Algorithm for Unsupervised image Segmentation

Inverse Quantum Fourier Transform Inspired Algorithm for Uns...

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IEEE International Symposium on Parallel and Distributed processing Workshops and Phd Forum (IPDPSW)

作者： Taoreed Akinola Xiangfang Li Richard Wilkins Pamela Obiomon Lijun Qian Department of Electrical and Computer Engineering Prairie View A&M University Prairie View Texas USA

image segmentation is a very popular and important task in computer vision. In this paper, inverse quantum Fourier transform (IQFT) for image segmentation has been explored and a novel IQFT-inspired algorithm is proposed and implemented by leveraging the underlying mathematical structure of the IQFT. Specifically, the proposed method takes advantage of the phase information of the pixels in the image by encoding the pixels’ intensity into qubit relative phases and applying IQFT to classify the pixels into different segments automatically and efficiently. To the best of our knowledge, this is the first attempt of using IQFT for unsupervised image segmentation. The proposed method has low computational cost comparing to the deep learning based methods and more importantly it does not require training, thus make it suitable for real-time applications. The performance of the proposed method is compared with K-means and Otsu-thresholding. The proposed method outperforms both of them on the PASCAL VOC 2012 segmentation benchmark and the xVIEW2 challenge dataset by as much as 50% in terms of mean Intersection-Over-Union (mIOU).

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An Approach to Classify the Shot Selection by Batsmen in Cricket Matches Using deep Neural Network on image Data 25

An Approach to Classify the Shot Selection by Batsmen in Cri...

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25th International Conference on Computer and Information Technology, ICCIT 2022

作者： Khan, Afsana Nabila, Fariha Haque Mohiuddin, Masud Mollah, Mahadi Alam, Ashraful Tanzim Reza, Md Brac University Department of Computer Science and Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798350346022

In recent times, technological advancement has brought a tremendous change in the field o f c ricket, which is a popular sport in many countries. Technology is being utilized to figure out projected score prediction, wicket prediction, winning probability, run rate, and many other parameters. In this research, our primary goal is to use Machine learning in the field of Cricket, where we aim to classify the shot played by the batsman, which can help in applications such as automated broadcasting systems or statistical data generation systems. For implementing our proposed model, we have generated our own dataset of cricket shot images by taking real-time photos from various cricket matches. We collected 1000 images of 10 different types of shots being played. For the classification task, we trained VGG-19 and Inception v3 model architecture and we got a better result by using VGG19. Before classification, the images had to go through several pre-processing methods such as background removal through Mask R-CNN, batsman segmentation through YOLO v3, etc. Then we used 83% of the total images to train the models and 17% to test the models. Finally, we achieved desired accuracy of 84.71% from VGG-19 and 82.35% from Inception-V3. © 2022 IEEE.

关键词： deep neural networks

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