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A semi-supervised deep learning approach for cropped image detection

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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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

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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Prediction of LncRNA Subcellular Localization with deep learning from Sequence Features

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SCIENTIFIC REPORTS 2018年第1期8卷 1-10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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A novel framework for generating handwritten datasets

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第6期80卷 9657-9669页

作者： Anwar, Sajid Mehrban, Bilal Ali, Musawar Hussain, Farhan Halim, Zahid Ghulam Ishaq Khan Inst Engn Sci & Technol Swabi Pakistan Univ AJK Muzaffarabad Pakistan Natl Univ Sci & Technol Islamabad Pakistan

The performance of deep learning algorithms is highly dependent on the size and diversity of data. However, for handwritten character recognition, dataset creation, segmentation, and labeling are time consuming and laborious tasks and not much researched. This work proposes a novel and generic framework which automates the segmentation and labeling processes for handwritten datasets. First, a user collects handwritten glyphs on the proposed form. Next, based on a priori knowledge, local peaks from horizontal and vertical projection functions are computed. This helps in locating and segmenting individual samples automatically. To show the effectiveness of the proposed framework, a dataset of 160,000 samples is collected for an oriental language. We profile the segmentation of samples from one sheet with three approaches: manual, semi-automatic, and the proposed fully automatic approach. Compared to the manual and semi-automatic processes, the proposed approach is 120 x and 65 x faster, respectively. Further, we also present the classification of this dataset by traditional and state-of-the-art machine learning algorithms.

关键词： Automatic segmentation Handwritten character recognition deep learning algorithms

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Automatic Lane Discovery and Traffic Congestion Detection in a Real-Time Multi-Vehicle Tracking Systems

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IEEE ACCESS 2024年 12卷 161468-161479页

作者： Wang, Lu Law, K. L. Eddie Lam, Chan-Tong Ng, Benjamin Ke, Wei Im, Marcus Macao Polytech Univ Fac Appl Sci Taipa Macau Peoples R China

The Macao Government provides web-based streaming videos for the public to monitor live traffic and road conditions across the city. This allows individuals to review the latest road traffic conditions online before planning their travels. To let road user makes better and faster decisions, it is desirable to design an automated subsystem in an Intelligent Transportation System (ITS). And the subsystem should analyze available live video streams and recommend multiple travel routes, if possible, to each query instantly. In the paper, we propose a real-time road traffic condition evaluation system. Its design is based on a combination of deep learning models (YOLO and BoTSORT), and a modified Non-Maximum Suppression (mNMS) algorithm. The mNMS strategy removes the needs to manually tune the NMS parameters. By deploying YOLO with our mNMS, the object detection efficiency on live videos improves significantly. Together with the BoTSORT method, we can track the moving vehicles, create the corresponding motion trajectories, and identify traffic lanes with high correctness. The generated trajectory then operates as a filtering mechanism in assessing real-time road traffic conditions. By separating the lanes based on observation angles and using a per-lane status score independently, we further enhance the overall system performance. Through thorough experiments on the live videos, our design correctly estimates traffic status with high accuracy and without needing any manual parametric adjustments.

关键词： deep learning algorithms intelligent transportation systems multi-vehicle tracking road traffic status monitoring deep learning algorithms intelligent transportation systems multi-vehicle tracking road traffic status monitoring

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Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods in VANET

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IEEE ACCESS 2022年 10卷 8227-8242页

作者： Sepasgozar, Sanaz Shaker Pierre, Samuel Polytech Montreal Mobile Comp & Networking Res Lab LARIM Dept Comp & Software Engn Montreal PQ H3T 1J4 Canada

Vehicular Ad hoc Networks (VANETs) are established on vehicles that are intelligent and can have Vehicle-to-Vehicle (V2V) and Vehicle-to-Road Side Units (V2R) communications. In this paper, we propose a model for predicting network traffic by considering the parameters that can lead to road traffic happening. The proposed model integrates a Random Forest- Gated Recurrent Unit- Network Traffic Prediction algorithm (RF-GRU-NTP) to predict the network traffic flow based on the traffic in the road and network simultaneously. This model has three phases including network traffic prediction based on V2R communication, road traffic prediction based on V2V communication, and network traffic prediction considering road traffic happening based on V2V and V2R communication. The hybrid proposed model which implements in the third phase, selects the important features from the combined dataset (including V2V and V2R communications), by using the Random Forest (RF) machine learning algorithm, then the deep learning algorithms to predict the network traffic flow apply, where the Gated Recurrent Unit (GRU) algorithm gives the best results. The simulation results show that the proposed RF-GRU-NTP model has better performance in execution time and prediction errors than other algorithms which used for network traffic prediction.

关键词： Prediction algorithms Telecommunication traffic Roads Predictive models Machine learning algorithms deep learning Radio frequency Vehicular network network traffic prediction road traffic prediction regression methods classification methods machine learning algorithms deep learning algorithms

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Unlocking the Potential of Information Modeling for Root Cause Analysis in a Production Environment: A Comprehensive State-of-the-Art Review Using the Kitchenham Methodology

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IEEE ACCESS 2024年 12卷 80266-80282页

作者： Koval, Leonid Knollmeyer, Simon Mathias, Selvine G. Asif, Saara Uzair Akmal, Muhammad Grossmann, Daniel Bregulla, Markus TH Ingolstadt AImotion Bavaria Inst Applicat Cluster Digital Prod D-85049 Ingolstadt Germany

Data from production environments is now available in unprecedented volumes, making the problem-solving of incidents through root cause analysis straightforward. However, the root cause analysis process remains time-consuming. This study employs the Kitchenham standard systematic literature review methodology to explore how information models and deep learning can streamline this process. By conducting a comprehensive search across four major databases, we evaluate the current technological advancements and their application in root cause analysis. The aim of this study is to assesses the impact of information models for root cause analysis in a production environment. Our findings reveal that integrating knowledge graphs, association rule mining, and deep learning algorithms significantly improves the speed and depth of root cause analysis compared to traditional methods. Specifically, the use of neural networks in recent literature shows substantial advancements in analyzing complex datasets, facilitating large-scale data integration, and enabling automated learning capabilities. Comparing our findings with other recent studies highlights the advantages of using information modeling and deep learning technologies in root cause analysis. This comparison underscores the superior accuracy and efficiency of these advanced methodologies over traditional manual interpretation methods. The effective implementation of these technologies requires a robust foundation of clean, standardized data, giving rise to the concept of "Production IT." Furthermore, it is crucial for this data to be openly available to facilitate academic research, thereby enabling the development of new methods for more efficient and effective root cause analysis.

关键词： Data-driven decision making deep learning algorithms industry 4.0 technologies information modeling (IM) machine learning in manufacturing root cause analysis (RCA)

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Gan-based data augmentation to improve breast ultrasound and mammography mass classification

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 94卷

作者： Jimenez-Gaona, Yuliana Carrion-Figueroa, Diana Lakshminarayanan, Vasudevan Rodriguez-Alvarez, Maria Jose Univ Tecn Particular Loja Dept Quim & Ciencias Exactas San Cayetano Alto S-N CP1101608 Loja Ecuador Hosp Gen Sur Quito IESS Calle Moraspungo & Pinllopata Quito 170111 Ecuador Univ Waterloo Sch Optometry & Vis Sci Theoret & Expt Epistemol Lab Waterloo ON N2L 3G1 Canada Univ Waterloo Dept Syst Design Engn Phys & Elect & Comp Engn Waterloo ON N2L 3G1 Canada Univ Politecn Valencia Inst Instrumentac Imagen Mol I3M E-46022 Valencia Spain

Data imbalance is a common problem in breast cancer diagnosis, to address this challenge, the research explores the use of Generative Adversarial Networks (GANs) to generate synthetic medical data. Various GAN methods, including Wasserstein GAN with Gradient Penalty (WGAN-GP), Cycle GAN, Conditional GAN, and Spectral Normalization GAN (SNGAN), were tested for data augmentation in breast regions of interest (ROIs) using mammography and ultrasound databases. The study employed real, synthetic, and hybrid ROIs (128x128 pixels) to train a Resnet network for classifying as benign (B) or malignant (M) classes. The quality and diversity of the synthetic data were assessed using several metrics: Fre chet Inception Distance (FID), Kernel Inception Distance (KID), Structural Similarity Index (SSIM), Multi -Scale SSIM (MS-SSIM), Blind Reference Image Spatial Quality Evaluator (BRISQUE), Naturalness Image Quality Evaluator (NIQE), and Perception -based Image Quality Evaluator (PIQE).Results revealed that the SNGAN model (FID = 52.89) was most effective for augmenting mammography data, while CGAN (FID = 116.03) excelled with ultrasound data. Cycle GAN and WGAN-GP, though demonstrating lower KID values, did not perform better than SNGAN and CGAN. The lower average MS-SSIM values suggested that SNGAN and CGAN produced a high diversity of synthetic images. However, lower SSIM, BRISQUE, NIQE, and PIQE values indicated poor quality in both real and synthetic images. Classification results showed high accuracy without data augmentation in both US (93.1 %B/94.9 %M) and mammography (80.9 %B/76.9 %M). The research concludes that preprocessing and characterizing ROIs by abnormality type is crucial to generate diverse synthetic data and improve accuracy in the classification process using combined GANs and CNN models.

关键词： Breast cancer Data augmentation deep learning algorithms Generative Adversarial Networks (GAN) Mammography Ultrasound

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Toward automatic reformation at the orbitomeatal line in head computed tomography using object detection algorithm

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PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE 2022年第3期45卷 835-845页

作者： Ichikawa, Shota Itadani, Hideki Sugimori, Hiroyuki Hokkaido Univ Grad Sch Hlth Sci Kita Ku Kita 12Nishi 5 Sapporo Hokkaido 0600812 Japan Kurashiki Cent Hosp Dept Radiol Technol 1-1-1 Miwa Kurashiki Okayama 7108602 Japan Hokkaido Univ Fac Hlth Sci Kita Ku Kita 12Nishi 5 Sapporo Hokkaido 0600812 Japan

Consistent cross-sectional imaging is desirable to accurately detect lesions and facilitate follow-up in head computed tomography (CT). However, manual reformation causes image variations among technologists and requires additional time. We therefore developed a system that reformats head CT images at the orbitomeatal (OM) line and evaluated the system performance using real-world clinical data. Retrospective data were obtained for 681 consecutive patients who underwent non-contrast head CT. The datasets were randomly divided into one of three sets for training, validation, or testing. Four landmarks (bilateral eyes and external auditory canal) were detected with the trained You Look Only Once (YOLO)v5 model, and the head CT images were reformatted at the OM line. The precision, recall, and mean average precision at the intersection over union threshold of 0.5 were computed in the validation sets. The reformation quality in testing sets was evaluated by three radiological technologists on a qualitative 4-point scale. The precision, recall, and mean average precision of the trained YOLOv5 model for all categories were 0.688, 0.949, and 0.827, respectively. In our environment, the mean implementation time was 23.5 +/- 2.4 s for each case. The qualitative evaluation in the testing sets showed that post-processed images of automatic reformation had clinically useful quality with scores 3 and 4 in 86.8%, 91.2%, and 94.1% for observers 1, 2, and 3, respectively. Our system demonstrated acceptable quality in reformatting the head CT images at the OM line using an object detection algorithm and was highly time efficient.

关键词： Computed tomography Orbitomeatal line deep learning algorithms Object detection You Look Only Once (YOLO)v5 model

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Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation

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PEERJ COMPUTER SCIENCE 2020年 6卷 e327-e327页

作者： Vaiyapuri, Thavavel Binbusayyis, Adel Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Alkharj Saudi Arabia

The ever-increasing use of internet has opened a new avenue for cybercriminals, alarming the online businesses and organization to stay ahead of evolving thread landscape. To this end, intrusion detection system (IDS) is deemed as a promising defensive mechanism to ensure network security. Recently, deep learning has gained ground in the field of intrusion detection but majority of progress has been witnessed on supervised learning which requires adequate labeled data for training. In real practice, labeling the high volume of network traffic is laborious and error prone. Intuitively, unsupervised deep learning approaches has received gaining momentum. Specifically, the advances in deep learning has endowed autoencoder (AE) with greater ability for data reconstruction to learn the robust feature representation from massive amount of data. Notwithstanding, there is no study that evaluates the potential of different AE variants as one-class classifier for intrusion detection. This study fills this gap of knowledge presenting a comparative evaluation of different AE variants for one-class unsupervised intrusion detection. For this research, the evaluation includes five different variants of AE such as Stacked AE, Sparse AE, Denoising AE, Contractive AE and Convolutional AE. Further, the study intents to conduct a fair comparison establishing a unified network configuration and training scheme for all variants over the common benchmark datasets, NSL-KDD and UNSW-NB15. The comparative evaluation study provides a valuable insight on how different AE variants can be used as one-class classifier to build an effective unsupervised IDS. The outcome of this study will be of great interest to the network security community as it provides a promising path for building effective IDS based on deep learning approaches alleviating the need for adequate and diverse intrusion network traffic behavior.

关键词： Network intrusion detection deep learning algorithms deep autoencoders One-class classifier Unsupervised deep learning

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