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Software Entity Automated Visual Recognition Method Based on deep learning Algorithm

Procedia Computer Science 2023年 228卷 817-825页

作者： Hui Gao

Automated visual recognition is a process that utilizes computer vision technology to analyze, process, and recognize images and videos, thereby achieving automation. It is an important branch of computer vision technology, bringing great convenience to people's production and life. This article mainly studies software entity automated visual recognition methods based on deep learning algorithms. Combining image processing technology and deep learning theory, a visual recognition method based on convolutional neural networks is proposed. On this basis, the advantages and disadvantages of this method were analyzed, and improvement suggestions were proposed to address its shortcomings. The experimental results show that the visual recognition method based on deep learning algorithm exhibits excellent performance in terms of accuracy and robustness.

关键词： deep learning algorithms Software Entities Automation Methods Visual Recognition

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Anthropogenic Marine Debris assessment with Unmanned Aerial Vehicle imagery and deep learning: A case study along the beaches of the Republic of Maldives

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SCIENCE OF THE TOTAL ENVIRONMENT 2019年 693卷 133581-000页

作者： Fallati, L. Polidori, A. Salvatore, C. Saponari, L. Savini, A. Galli, P. Univ Milano Bicocca Dept Earth & Environm Sci Milan Italy MaRHE Ctr Marine Res & High Educ Ctr Magoodhoo Island Faafu A Maldives DeepTrace Technol SRL Milan Italy

Anthropogenic Marine Debris (AMD) is one of the major environmental issues of our planet to date, and plastic accounts for 80% of total AMD. Beaches represent one of the main marine compartment where AMD accumulates, but few and scattered regional assessments are available from literature reporting quantitative estimation of AMD distributed on the shorelines. However, accessing information on the AMD accumulation rate on beaches, and the associated spatiotemporal oscillations, would be crucial to refining global estimation on the dispersal mechanisms. In our work, we address this issue by proposing an ad-hoc methodology for monitoring and automatically quantifying AMD, based on the combined use of a commercial Unmanned Aerial Vehicle (UAV) (equipped with an RGB high-resolution camera) and a deep-learning based software (i.e.: PlasticFinder). Remote areas were monitored by UAV and were inspected by operators on the ground to check and to categorise all AMD dispersed on the beach. The high-resolution images obtained from UAV allowed to visually detect a percentage of the objects on the shores higher than 87.8%, thus providing suitable images to populate training and testing datasets, as well as gold standards to evaluate the software performance. PlasticFinder reached a Sensitivity of 67%, with a Positive Predictive Value of 94%, in the automatic detection of AMD, but a limitation was found, due to reduced sunlight conditions, thus restricting to the use of the software in its present version. We, therefore, confirmed the efficiency of commercial UAVs as tools for AMD monitoring and demonstrated - for the first time - the potential of deep learning for the automatic detection and quantification of AMD. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Anthropogenic Marine-Debris Unmanned Aerial Vehicles Machine learning deep learning algorithms Maldives Beach

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Integrating social media and deep learning for real-time urban waterlogging monitoring

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JOURNAL OF HYDROLOGY-REGIONAL STUDIES 2024年 56卷

作者： Boota, Muhammad Waseem Soomro, Shan-e-hyder Ahmad, Muhammad Irshad Khan, Sheheryar Xia, Haoming Qin, Yaochen Yan, Chaode Xu, Jikun Yousaf, Ayesha Boota, Muhammad Azeem Ahmed, Bilal Henan Univ Coll Geog & Environm Sci Kaifeng 475004 Peoples R China Henan Univ Henan Key Lab Earth Syst Observat & Modeling Kaifeng 475004 Peoples R China Henan Univ Key Lab Geospatial Technol Middle & Lower Yellow R Minist Educ Kaifeng 475004 Peoples R China Henan Univ Key Res Inst Yellow River Civilizat & Sustainable Kaifeng 475004 Peoples R China Henan Univ Collaborat Innovat Ctr Yellow River Civilizat Join Kaifeng 475004 Peoples R China China Three Gorges Univ Coll Hydraul & Environm Engn Yichang 443002 Peoples R China Zhengzhou Univ Sch Polit & Publ Adm Zhengzhou 450001 Peoples R China Zhengzhou Univ Business Sch Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Water Conservancy & Transportat Zhengzhou 450001 Peoples R China Henan Univ Technol Coll Mech & Elect Engn Zhengzhou 450052 Peoples R China Barani Agr Res Inst Chakwal Pakistan Silesian Tech Univ Fac Civil Engn Doctoral Sch Dept Struct Engn Akad 2 PL-44100 Gliwice Poland

Study region: Swat district, Khyber Pakhtunkhwa (KPK) Province, Pakistan. Study focus: With the rise of social-media data, there is an increasing need to promptly and precisely identify content related to disasters, such as urban waterlogging. Social-media data, being cost-effective and abundant, can offer valuable insights into geographic phenomena by analyzing human behavioral patterns, making it a powerful resource for detailed waterlogging (WLG) analysis in urban settings. Innovative insights: This research introduces a novel framework for precise information retrieval and real-time extraction of WLG points and fine-grained information in disaster-affected areas using the Facebook platform. First, topic modeling and transfer learning techniques were developed to examine the spatiotemporal dynamics of WLG locations. Second, water depth data from textual content and visual representations were extracted using various deep learning frameworks and integrated through decision-making processes. Third, a unique fine-grained location corpus tailored to urban flooding scenarios was created using the named entity recognition (NER) model. Finally, the BERT-BiLSTM-CRF model was employed to extract WLG points accurately. Using the Swat district as a case study, the extracted WLG points covered at least 79% of the officially documented WLG points and were primarily located near roadways, especially in low-elevation areas. This framework provides a viable approach for enhancing situational awareness and conducting spatiotemporal analysis of urban floods and WLG disasters at the municipal level in real-time.

关键词： Urban waterlogging Social-media Water depth information Spatiotemporal evolution deep learning algorithms

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Automatic Defects Segmentation and Identification by deep learning Algorithm with Pulsed Thermography: Synthetic and Experimental Data

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BIG DATA AND COGNITIVE COMPUTING 2021年第1期5卷 9页

作者： Fang, Qiang Ibarra-Castanedo, Clemente Maldague, Xavier Univ Laval Dept Elect & Comp Engn Comp Vis & Syst Lab 1065 Av Med Quebec City PQ G1V 0A6 Canada

In quality evaluation (QE) of the industrial production field, infrared thermography (IRT) is one of the most crucial techniques used for evaluating composite materials due to the properties of low cost, fast inspection of large surfaces, and safety. The application of deep neural networks tends to be a prominent direction in IRT Non-Destructive Testing (NDT). During the training of the neural network, the Achilles heel is the necessity of a large database. The collection of huge amounts of training data is the high expense task. In NDT with deep learning, synthetic data contributing to training in infrared thermography remains relatively unexplored. In this paper, synthetic data from the standard Finite Element Models are combined with experimental data to build repositories with Mask Region based Convolutional Neural Networks (Mask-RCNN) to strengthen the neural network, learning the essential features of objects of interest and achieving defect segmentation automatically. These results indicate the possibility of adapting inexpensive synthetic data merging with a certain amount of the experimental database for training the neural networks in order to achieve the compelling performance from a limited collection of the annotated experimental data of a real-world practical thermography experiment.

关键词： automatic defect detection infrared thermography deep learning algorithms Non-Destructive Evaluation (NDE) supervised learning image processing data augmentation Finite Element Models (FEM)

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A Knowledge-Based deep learning Approach for Automatic Fake News Detection using BERT on Twitter

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Procedia Computer Science 2024年 235卷 1870-1882页

作者： Vinita Nair Dr. Jyoti Pareek Sanskruti Bhatt

Fake news generation and propagation is a major challenge of the digital age, resulting in various social impacts namely bandwagon, validity, echo chamber effects, deceiving the public with spams, misinformation, malicious content and many more. The widespread proliferation of fake news not only fosters misinformation but also undermines the credibility of news sources. The veracity of the information is a major concern at all the stages of generation, publication, and propagation. To comprehend the critical need for addressing this pervasive problem, this research paper presents a framework for automatic detection of fake news using a knowledge-based approach. An automatic fact checking mechanism is applied using concepts of Information Retrieval (IR), Natural Language Processing (NLP) and Graph theory. The knowledge base is generated using Twitter dataset, which basically contains four attributes: Subject-Predicate-Object (SPO) triplet, SPO sentiment polarity, SPO occurrence, and topic modeling. These attributes serve as pivotal indicators for the development of a knowledge base, subsequently employed to detect prevalent patterns and traits linked to deceptive or false information. We have employed Named Entity Recognition (NER) model to extract SPO triples and Latent Dirichlet Allocation (LDA) for topic modeling, thereby contributing to knowledge base generation. To evaluate the efficacy and efficiency of our proposed model, we utilize deep learning algorithms like RNN, GRU, LSTM, GPT-3 and BERT Transformer providing an acceptable level of accuracy. This research paper delivers valuable insights into addressing the proliferation of fake news on Twitter, employing data-driven approaches and advanced deep learning algorithms.

关键词： Fake news Knowledge-based Twitter SPO triple sentiment polarity topic modeling deep learning algorithms BERT Transformer

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Improvement of Brain Tumor Categorization using deep learning: A Comprehensive Investigation and Comparative Analysis

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Procedia Computer Science 2024年 233卷 703-712页

作者： T. Lakshmi Prasanthi N. Neelima Department of Electronics and Communication Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India

A brain tumor is a critically severe health disorder that requires an accurate and timely diagnosis for effective treatment. Advances in medical imaging and deep learning methods have shown potential for enhancing the identification and categorization of brain cancers throughout the years. In the present research, our study compares the accuracy of eight different deep learning models in the classification of brain tumors employing brain MRI data that involve Densenet121, EfficientNet B7, InceptionResNetV2, Inception_V3, RestNet50V2, VGG16, VGG19, and Xception. To further improve performance, we propose integrating a hybrid deep learning technique. Efficient and timely diagnosis of brain tumors is critical for the treatment of patients, and our study aims to achieve high recall, accuracy, and F1-score in this context. With a precision of 96.63%, our innovative convolutional neural network (CNN) technique achieved outstanding results in brain tumor diagnosis. Also, our study investigates the unique capabilities of certain models, such as VGG19 and InceptionResNetV2, and their possibilities for better glioma tumor detection efficiency. Our results, in particular, provide insight into possible uses of deep learning frameworks, including the integration of hybrid techniques, in medical imaging, offering an innovative approach for increased brain tumor detection and identification.

关键词： Brain Tumor deep learning algorithms Medical Imaging Image Classification Neural Network Models

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Yoga Posture Recognition by learning Spatial-Temporal Feature with deep learning Techniques

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2024年第6期24卷

作者： Palanimeera, J. Ponmozhi, K. Kalasalingam Acad Res & Educ Dept Comp Applicat Krishnankoil Tamil Nadu India

Yoga posture recognition remains a difficult issue because of crowded backgrounds, varied settings, occlusions, viewpoint alterations, and camera motions, despite recent promising advances in deep learning. In this paper, the method for accurately detecting various yoga poses using DL (deep learning) algorithms is provided. Using a standard RGB camera, six yoga poses - Sukhasana, Kakasana, Naukasana, Dhanurasana, Tadasana, and Vrikshasana - were captured on ten people, five men and five women. In this study, a brand-new DL model is presented for representing the spatio-temporal (ST) variation of skeleton-based yoga poses in movies. It is advised to use a variety of representation learners to pry video-level temporal recordings, which combine spatio-temporal sampling with long-range time mastering to produce a successful and effective training approach. A novel feature extraction method using Open Pose is described, together with a DenceBi-directional LSTM network to represent spatial-temporal links in both the forward and backward directions. This will increase the efficacy and consistency of modeling long-range action detection. To improve temporal pattern modeling capability, they are stacked and combined with dense skip connections. To improve performance, two modalities from look and motion are fused with a fusion module and compared to other deep learning models are LSTMs including LSTM, Bi-LSTM, Res-LSTM, and Res-BiLSTM. Studies on real-time datasets of yoga poses show that the suggested DenseBi-LSTM model performs better and yields better results than state-of-the-art techniques for yoga pose detection.

关键词： Identifying yoga postures deep learning algorithms RGB camera spatial and temporal open pose Dense-BiLSTM

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deep learning Process and Application for the Detection of Dangerous Goods Passing through Motorway Tunnels

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algorithms 2022年第10期15卷 370-370页

作者： Sisias, George Konstantinidou, Myrto Kontogiannis, Sotirios Univ Western Macedonia Dept Informat Kastoria 52100 Greece NCSR Demokritos Syst Reliabil & Ind Safety Lab Inst Nucl & Radiol Sci Energy Technol & Safety Athens 15341 Greece Univ Ioannina Dept Math Lab Team Distributed Microcomp Syst Ioannina 45110 Greece

Automated deep learning and data mining algorithms can provide accurate detection, frequency patterns, and predictions of dangerous goods passing through motorways and tunnels. This paper presents a post-processing image detection application and a three-stage deep learning detection algorithm that identifies and records dangerous goods' passage through motorways and tunnels. This tool receives low-resolution input from toll camera images and offers timely information on vehicles carrying dangerous goods. According to the authors' experimentation, the mean accuracy achieved by stage 2 of the proposed algorithm in identifying the ADR plates is close to 96% and 92% of both stages 1 and 2 of the algorithm. In addition, for the successful optical character recognition of the ADR numbers, the algorithm's stage 3 mean accuracy is between 90 and 97%, and overall successful detection and Optical Character Recognition accuracy are close to 94%. Regarding execution time, the proposed algorithm can achieve real-time detection capabilities by processing one image in less than 2.69 s.

关键词： risk management transport management systems CNN algorithms image processing algorithms deep learning algorithms distributed management systems

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A semi-supervised deep learning approach for cropped image detection

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 243卷

作者： Hussain, Israr Tan, Shunquan Huang, Jiwu Shenzhen Univ Shenzhen Key Lab Media Secur Shenzhen 518060 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518000 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518000 Peoples R China

deep learning algorithms have shown great performance in multimedia forensics applications using supervised learning on large-scale labeled datasets. However, constructing such extensive labeled datasets can be challenging and costly in several multimedia forensics scenarios. Additionally, heavyweight deep learning models with complex architectures and a large number of parameters require significant hardware resources for training. To address these challenges in the context of image cropping detection, a common multimedia forensics application, we propose a semi-supervised deep learning framework capable of training on a large amount of unlabeled image samples. In this framework, we leverage a teacher model, trained on a small set of labeled image samples, to rank the confidence scores of image samples in a large-scale unlabeled dataset. By utilizing the ranked image samples, we train a student network successfully. To validate the effectiveness of our collaborative training framework across various image cropping detection scenarios, we conduct extensive experiments on a large-scale dataset. The experimental results clearly demonstrate that our semi-supervised learning approach achieved a state-of-the-art performance compared to existing supervised detection frameworks, achieving an accuracy of 91.79% on the BOSSbase dataset and 89.23% on the Alaska dataset. Furthermore, we conducted in-depth research on various factors that influence detection performance in the context of semi-supervised learning. These factors include pairings of teacher-student models, the top K selection approach, the number of unlabeled samples, the number of iterations in self-training, and the proportion of high-confidence samples using in semi-supervised learning.

关键词： Image cropping Semi-supervised learning Multimedia forensics deep learning algorithms Unlabeled data

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Prediction of LncRNA Subcellular Localization with deep learning from Sequence Features

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SCIENTIFIC REPORTS 2018年第1期8卷 16385-16385页

作者： Gudenas, Brian L. Wang, Liangjiang Clemson Univ Dept Genet & Biochem Clemson SC 29634 USA

Long non-coding RNAs are involved in biological processes throughout the cell including the nucleus, chromatin and cytosol. However, most lncRNAs remain unannotated and functional annotation of lncRNAs is difficult due to their low conservation and their tissue and developmentally specific expression. LncRNA subcellular localization is highly informative regarding its biological function, although it is difficult to discover because few prediction methods currently exist. While protein subcellular localization prediction is a well-established research field, lncRNA localization prediction is a novel research problem. We developed deepLncRNA, a deep learning algorithm which predicts lncRNA subcellular localization directly from lncRNA transcript sequences. We analyzed 93 strand-specific RNA-seq samples of nuclear and cytosolic fractions from multiple cell types to identify differentially localized lncRNAs. We then extracted sequence-based features from the lncRNAs to construct our deepLncRNA model, which achieved an accuracy of 72.4%, sensitivity of 83%, specificity of 62.4% and area under the receiver operating characteristic curve of 0.787. Our results suggest that primary sequence motifs are a major driving force in the subcellular localization of lncRNAs.

关键词： Localized lncRNAs Sequence-based Features Protein Subcellular Localization Prediction lncRNA Transcripts deep learning algorithms

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