Deep learning-based detection and statistical analysis of equatorial spread F using ionogram data in the African sector

ObjectivesWe aimed to evaluate whether deep learning–based detection and quantification of brain metastasis (BM) may suggest treatment options for patients with BMs.MethodsThe deep learning system (DLS) for detection and quantification of BM was developed in 193 patients and applied to 112 patients that were newly detected on black-blood contrast-enhanced T1-weighted imaging. Patients were assigned to one of 3 treatment suggestion groups according to the European Association of Neuro-Oncology (EANO)-European Society for Medical Oncology (ESMO) recommendations using number and volume of the BMs detected by the DLS: short-term imaging follow-up without treatment (group A), surgery or stereotactic radiosurgery (limited BM, group B), or whole-brain radiotherapy or systemic chemotherapy (extensive BM, group C). The concordance between the DLS-based groups and clinical decisions was analyzed with or without consideration of targeted agents. The performance of distinguishing high-risk (B + C) was calculated.ResultsAmong 112 patients (mean age 64.3 years, 63 men), group C had the largest number and volume of BM, followed by group B (4.4 and 851.6 mm3) and A (1.5 and 15.5 mm3). The DLS-based groups were concordant with the actual clinical decisions, with an accuracy of 76.8% (86 of 112). Modified accuracy considering targeted agents was 81.3% (91 of 112). The DLS showed 95% (82/86) sensitivity and 81% (21/26) specificity for distinguishing the high risk.ConclusionDLS-based detection and quantification of BM have the potential to be helpful in the determination of treatment options for both low- and high-risk groups of limited and extensive BMs.Clinical relevance statementFor patients with newly diagnosed brain metastasis, deep learning–based detection and quantification may be used in clinical settings where prompt and accurate treatment decisions are required, which can lead to better patient outcomes.Key Points• Deep learning–based brain metastasis detection and quantification showed excellent agreement with ground-truth classifications.• By setting an algorithm to suggest treatment based on the number and volume of brain metastases detected by the deep learning system, the concordance was 81.3%.• When dividing patients into low- and high-risk groups, the sensitivity for detecting the latter was 95%.

As a well-established and extensively utilized model organism, Caenorhabditis elegans (C. elegans) serves as a crucial platform for investigating behavioral regulation mechanisms and their biological significance. However, manually tracking the locomotor behavior of large numbers of C. elegans is both cumbersome and inefficient. To address the above challenges, we innovatively propose an automated approach for analyzing C. elegans behavior through deep learning-based detection and tracking. Building upon existing research, we developed an enhanced worm detection framework that integrates YOLOv8 with ByteTrack, enabling real-time, precise tracking of multiple worms. Based on the tracking results, we further established an automated high-throughput method for quantitative analysis of multiple movement parameters, including locomotion velocity, body bending angle, and roll frequency, thereby laying a robust foundation for high-precision, automated analysis of complex worm behaviors. including movement speed, body bending angle, and roll frequency, thereby laying a robust foundation for high-precision, automated analysis of complex worm behaviors. Comparative evaluations demonstrate that the proposed enhanced C. elegans detection framework outperforms existing methods, achieving a precision of 99.5%, recall of 98.7%, and mAP50 of 99.6%, with a processing speed of 153 frames per second (FPS). The established framework for worm detection, tracking, and automated behavioral analysis developed in this study delivers superior detection and tracking accuracy while enhancing tracking continuity and robustness. Unlike traditional labor-intensive measurement approaches, our framework supports simultaneous tracking of multiple worms while maintaining automated extraction of various behavioral parameters with high precision. Furthermore, our approach advances the standardization of C. elegans behavioral parameter analysis, which can analyze the behavioral data of multiple worms at the same time, significantly improving the experimental throughput and providing an efficient tool for drug screening, gene function research and other fields.

Chapter 11 - Deep learning-based detection and classification of adenocarcinoma cell nuclei

The importance of a properly maintained attendance system is very high. Although many systems are already existing, there are many loopholes present. This paper presents an attendance system using face recognition, which is the best foot forward to reduce the loopholes encountered by these systems. The Attendance system using face recognition consists of two phases, face detection and face recognition. The performance of detection algorithms such as Viola Jones and deep learning-based detection was compared and deep learning-based detection was preferred. On the recognition front, deep learning-based face recognition was used and optimum results were obtained. It was found that dlib using Convolutional Neural Networks (CNN) yielded better results than dlib using Histogram Oriented Gradients (HOG) because HOG only detects a frontal image, while CNN detects faces from all angles and ensures no discrepancies during face detection and recognition. Also, it was observed that the speed of training datasets was higher in CNN as it uses GPU as compared to HOG, which uses CPU for training the dataset as well as recognition.

Effect of slab thickness on pulmonary nodule detection using maximum intensity projection in a deep learning-based computer-aided detection system

A bridge inspection needs a lot of costs. It causes a lack of engineers and budget. So, some local governments couldn’t complete the bridge’s aggressive preventive maintenance in Japan. Recently, deep learning-based damage detection methods have been studied by many researchers to reduce the cost of the bridge’s aggressive preventive maintenance. This kind of method could detect the damage to the bridge by photo image with a detection model which has been trained with a large training dataset. In contrast to the increase in dataset size, the effectiveness of the quality of training data is not discussed enough. In this paper, the ratio of negative samples in the training data is regarded as the quality of training data. In this study, we targeted the automatic detection process of the rebar exposure and the peeling on the surface of bridges. The effects of the ratio of the negative example data in a training dataset have been evaluated. In this study, the negative sample means the annotated data with no target damage. The negative sample image is the part of a bridge but does not include target damages. Many previous studies generate this kind of image when generating annotation data from real bridge photo images, but do not utilize it. A three-fold cross-validation method has been adopted to keep the robustness of the detection. The seven different detection models have trained with seven different training dataset which has different negative sample ratios. As a result of comparing the detection results of each model, the influence of the negative example data on the training data was confirmed. There was a peak of the recall curve in the middle of increasing the negative sample ratios. The correct negative sample ratio could improve the accuracy of damage detection.

With the easy availability of numerous low-cost and ready-to-use image editing software, nowadays, the forgery of the digital image has become very common and widespread. The fraudsters can deliberately use the tampered images for various malicious activities. Among different types of digital image manipulation, probably the most popular and effective technique is the splicing of images, which makes a natural-looking composite image by cutting and combining different portions from multiple source images. The detection procedure of image splicing can be categorized into two types, which are - feature engineering and machine learning-based detection, and deep learning-based detection with automatic feature extraction. Usually, deep learning-based mechanisms are robust, and they provide high accuracy. However, the deep learning model requires a vast amount of data for training, and it is also time-consuming and costly for its structural complexity. Therefore, in this work, an effective image splicing detection scheme based on deep learning and transfer learning has been proposed that also overcomes the issues of high training time and complex modelling structure. In this work, a Deep Convolution Neural Network (CNN)-based model has been developed where the initial convolution layers are replaced by a pre-trained CNN model MobileNetV2. Instead of training the developed CNN model from the beginning, a transfer learning technique has been adopted to skip the time-consuming initialization of the network. With the help of experimental results, it is shown that our simplified model can successfully detect spliced images with state-of-the-art accuracy despite removing the requirements of very high training data and time.

This paper presents a novel method for real-time 3D object detection and tracking in monocular images. The method build maps of a user-specified object from a video sequence, and stores the data for 3D object detection and tracking. The main advantage of the method lies in that it does not need existing 3D models of the objects. Instead, it first detects the target object using the state-of-the-art deep learning-based object detection method, and constructs its map using visual Simultaneous Localization and Mapping (vSLAM). The maps only need to be built once and multiple maps of different objects can be stored. A fast method is proposed to recognize the object in the map with the aid of deep learning-based detection. The method needs only one camera and is robust in cluttered environment. The mode of multiple maps allows the reuse of pre-reconstructed maps. Experimental results show that accurate, fast and robust detection and tracking are achieved.

Statistics of spread F characteristics across different sectors and IRI 2016 prediction

Numerous advantages have resulted from the increased integration of cutting-edge technologies in power systems, but it has also brought forth new vulnerabilities, mainly in the form of bogus data injection attacks. The stability and dependability of power systems may be compromised by these assaults, necessitating the creation of efficient detection mechanisms. We provide a unique Deep Reinforcement Learning-Based Detection Framework for False Data Injection Attacks in Power Systems in this academic publication. In order to learn and adapt to dynamic attack patterns, our model makes use of the power of deep reinforcement learning. As a result, it is resilient and able to recognize sophisticated attacks in real-time. We have out extensive tests on a sizable dataset acquired from a realistic power system simulation to assess the efficacy of our proposed framework. With an accuracy score of 97%, precision score of 95%, recall score of 89%, and F1 score of 92% on the test set, the results show how good our model is. The comparison table shows that the proposed framework performs better than a number of current approaches, including Linear Regression, Support Vector Machine, Random Forest, AdaBoost Classifier, and Gradient Boosting Classifier. Our model achieved an impressive ROC curve of 0.99, highlighting its capability to distinguish between normal and adversarial data with high accuracy. The advantages of our proposed model lie in its ability to detect false data injection attacks with high accuracy and its adaptability to evolving attack patterns. Moreover, it demonstrates robustness against adversarial attacks, making it a reliable defense mechanism for modern power systems. Deploying the proposed framework may considerably improve the security and resilience of power systems, assuring the continuation of consumers' access to energy. Hence, our research introduces a powerful Deep Reinforcement Learning-Based Detection Framework for False Data Injection Attacks, contributing a valuable tool for securing power systems against emerging threats. With its remarkable performance and potential for future development, this model represents a crucial step towards establishing cyber-resilient power infrastructures for the years to come.

Statistics by the Federal Highway Administration (FHWA)’s report indicates that 11% of the bridges in the United States are rated as "structurally deficient" and over 30% of existing bridges have exceeded their 50-year design life, meaning that condition assessment and repair programs will require substantial budget in the near future. Current observation-based bridge inspection techniques consist largely of time consuming and subjective measures for quantifying deterioration of bridges. During bridge inspection, simplistic methods for assessing deterioration in concrete bridge decks are only capable of detecting deterioration in its moderate to severe stages. To provide a more thorough assessment of deterioration in concrete bridge decks, advanced technologies should be incorporated into bridge inspection. Some advanced nondestructive testing methods such as Ground Penetrating Radar (GPR), are being implemented that provide sub-surface information. GPR has been successfully used in a wide range of applications. Using advanced technologies like ground penetrating radar (GPR), deterioration hidden from the naked eye or missed using traditional methods, like Chain Dragging and Hammering Sounding, can be more accurately detected. Automatic rebar detection in GPR data is the basic step in an automatic system for GPR-based condition evaluation of bridge decks. Achieving real-time performance on Acorn Reduced Instruction Set Computing Machine (ARM) based platforms for onsite applications still remains a challenge. Development an accurate and cost-effective system for real-time onsite rebar detection in GPR images is goal of this study. The authors proposed a novel computer vision-based method for automatic detection of rebars in complex GPR images in highly deteriorated concrete bridge decks. Extensive experiment performed to develop a reference for selecting a deep learning-based detection architecture that provides the right accuracy, speed, and memory usage balance for real-time detection of rebars on the latest version of ARM-based platforms. A deep learning-based detector is presented that can be deployed on the latest version of ARM-based platforms. State of the art results is obtained on GPRDETN detection task by implementing rebar detection model using Faster R-CNN with ResNet 101 CNN backbone.

In Cognitive Radio, spectrum sensing enables dynamic spectrum access. Conventional energy detector is a blind algorithm, where the detection performance depends on the estimation accuracy of noise variance that deteriorates in a non-Gaussian noise environment. Hence, in this work, we propose median absolute deviation statistics to estimate the noise variance for threshold evaluation. Further, we estimate the energy of the received signal using the Huber cost function to mitigate the effect of non-Gaussian channel noise. The detection performance of the proposed algorithm is compared with conventional energy detector and deep learning-based detectors on the RadioML2016.10A dataset, signal-to-noise ratio (SNR) ranging from −20 dB to +18 dB. The simulation results demonstrate that the proposed algorithm outperforms conventional energy detection and is at par with deep learning-based detection.

Retracted: Deep Learning-Based Detection and Identification Method for Sports Health Video Dissemination

Deep learning-based detection and prediction of trending topics from streaming data

In recent years, agriculture has begun to transform thanks to the arrival of robots and autonomous vehicles capable of performing complex operations such as weeding and spraying in an intelligent and targeted manner. In fact, new-generation agricultural robots use artificial intelligence (AI), cameras, and sensors to recognise weeds, analyse crop conditions, and apply plant protection products only where necessary, thus reducing waste and environmental impact. Some systems combine drones and ground vehicles to achieve even more accurate results. This systematic review synthesises recent advances in agricultural robotics for weed and pest management through a PRISMA-based approach. Literature was collected from major scientific databases (Scopus, Web of Science, IEEE Xplore, Google Scholar) and complementary sources, leading to the inclusion of 83 eligible studies. The selected evidence was structured into four application domains: (i) weed detection and mapping, (ii) robotic and non-chemical weed control (mechanical and laser-based approaches), (iii) selective/variable-rate spraying for pest and disease management, and (iv) integrated weeding–spraying solutions, including cooperative Unmanned Aerial Vehicle–Unmanned Ground Vehicle (UAV–UGV) systems. Overall, the reviewed studies confirm rapid progress in real-time perception (deep learning-based detection), navigation/localization (e.g., GNSS/RTK, LiDAR, sensor fusion) and targeted actuation (spot spraying and precision interventions), while also revealing persistent limitations: heterogeneous evaluation protocols, limited system-level comparisons in terms of work rate, scalability, costs and robustness under variable field conditions, and an often unclear distinction between prototype platforms and solutions close to commercialization. However, the large-scale spread of these technologies is still hampered by high costs, technical complexity, and cultural resistance. The review highlights how the integration of automation, sustainability, and accessibility is key to the agriculture of the future.