Deep Learning-based Abnormal Behavior Detection System for Dementia Patients

Background Previous studies assessing the effects of computer-aided detection on observer performance in the reading of chest radiographs used a sequential reading design that may have biased the results because of reading order or recall bias. Purpose To compare observer performance in detecting and localizing major abnormal findings including nodules, consolidation, interstitial opacity, pleural effusion, and pneumothorax on chest radiographs without versus with deep learning-based detection (DLD) system assistance in a randomized crossover design. Materials and Methods This study included retrospectively collected normal and abnormal chest radiographs between January 2016 and December 2017 (https://cris.nih.go.kr/; registration no. KCT0004147). The radiographs were randomized into two groups, and six observers, including thoracic radiologists, interpreted each radiograph without and with use of a commercially available DLD system by using a crossover design with a washout period. Jackknife alternative free-response receiver operating characteristic (JAFROC) figure of merit (FOM), area under the receiver operating characteristic curve (AUC), sensitivity, specificity, false-positive findings per image, and reading times of observers with and without the DLD system were compared by using McNemar and paired t tests. Results A total of 114 normal (mean patient age ± standard deviation, 51 years ± 11; 58 men) and 114 abnormal (mean patient age, 60 years ± 15; 75 men) chest radiographs were evaluated. The radiographs were randomized to two groups: group A (n = 114) and group B (n = 114). Use of the DLD system improved the observers' JAFROC FOM (from 0.90 to 0.95, P = .002), AUC (from 0.93 to 0.98, P = .002), per-lesion sensitivity (from 83% [822 of 990 lesions] to 89.1% [882 of 990 lesions], P = .009), per-image sensitivity (from 80% [548 of 684 radiographs] to 89% [608 of 684 radiographs], P = .009), and specificity (from 89.3% [611 of 684 radiographs] to 96.6% [661 of 684 radiographs], P = .01) and reduced the reading time (from 10-65 seconds to 6-27 seconds, P < .001). The DLD system alone outperformed the pooled observers (JAFROC FOM: 0.96 vs 0.90, respectively, P = .007; AUC: 0.98 vs 0.93, P = .003). Conclusion Observers including thoracic radiologists showed improved performance in the detection and localization of major abnormal findings on chest radiographs and reduced reading time with use of a deep learning-based detection system. © RSNA, 2021 Online supplemental material is available for this article.

A deep learning-based food detection and classification system

A Study on Deep Learning-based Pedestrian Detection and Alarm System

The increased use of surveillance technology and the need for robust security have fueled research into autonomous, intelligent monitoring systems, particularly in shopping centers where theft and vandalism are common. Deep learning, though promising in anomaly detection, is still hampered by the quality and balance of the training data. In this work, we introduce a deep learning-based anomaly detection system that is trained on an optimized dataset where extensive preprocessing was performed to enhance data quality, eliminate redundancies, and balance class distributions.Our DenseNet-121 and CNN classifier-based system uses this optimized dataset to improve abnormal behavior detection in store surveillance. Experimental results confirm that dataset optimization significantly improves anomaly detection accuracy, reduces false positives, and improves generalizability. Our system achieves an 83% total accuracy, outperforming systems trained on raw, imbalanced data. These findings underscore the importance of data preprocessing as an integral part of deep learning-based surveillance systems.

Low back pain (LBP) is a prevalent and disabling musculoskeletal disorder that severely affects patients’ quality of life. Traditional rehabilitation training relies on therapists’ subjective observations, which have issues such as imprecise assessment and untimely feedback. A deep learning-based motion analysis system was developed to deliver an intelligent exercise training program with real-time, precise feedback. Through a randomized controlled trial (single-center design), 118 patients with chronic non-specific LBP were randomly assigned to an experimental group (receiving intelligent training based on the motion analysis system) and a control group (receiving conventional guided training). Outcome measures included the visual analog scale (VAS), Oswestry disability index (ODI), surface electromyography (sEMG) signals of core muscles, and system-generated movement quality scores. System performance tests indicated that the pose estimation model achieved a mean Average Precision (AP@0.5) of 96.7% in keypoint detection, with a mean per joint position error (MPJPE) of 25.3 mm and a pelvic angle estimation error of 3.1°, providing a reliable technical foundation for precise motion analysis. Supported by this system, after the 8-week intervention (T1), improvements in both VAS scores (2.6 ± 1.1 vs. 3.9 ± 1.3) and ODI scores (18.7 ± 6.3 vs. 28.3 ± 7.6) were significantly greater in the experimental group compared to the control group (P &lt; 0.05), and this advantage was maintained at the 1-month follow-up after the intervention ended (T2). The average movement quality score during the entire intervention period was also significantly higher in the experimental group (88.5±4.2 points) than in the control group (76.8±7.5 points). The sEMG results indicated more coordinated activation and less compensation in the core muscles of the experimental group. The training adherence rate was significantly higher in the experimental group (92.5%) compared to the control group (85.3%). The deep learning-based motion analysis system developed in this study is highly accurate and robust, capable of providing precise, real-time feedback for rehabilitation training for patients with LBP. It significantly alleviates pain, improves functional impairments, and optimizes movement patterns and neuromuscular control. This innovative rehabilitation method has good clinical application prospects.

Cities development accelerates with galloping urbanization on the surface on the world [1,2]. They must face significant threats linked to risks of human origin, like terrorism. In this paper, we present our approach for intrusion detection composed of 3 phases. The first one consists in selecting, via a GUI interface, zones supposed to be prohibited zones in an image. The second one, based on a Neural Network method, is applied for the person detection. The third one verifies if the detected person is present in one of the prohibited zones or not. If so, an alarm goes off automatically. Real tests were performed to secure an elementary school in the city of Nice in France. The obtained results showed the efficiency of our method in terms of good detection. Other work is in progress with the aim of deeply analyzing the intrusion to detect the abnormal behavior.

In recent years, the increase in accidents and lonely death in the home has become a social problem due to the increase of the elderly living alone. Therefore this paper proposes an elderly people's abnormal behavior detection system by the floor laying pressure sensor. The system consists of more than 30 belt-like sensors and the sensing data provides many important information such as the subject position and the amount of movement.The feature patterns appeared in daily samples such as housework and sleeping are modeled by fuzzy inference and then the abnormal patterns can be detected as non-existent feature patterns using the possibility distribution estimation. Some experiments are provided to verify the effectiveness of the proposed system.

Intrusion detection systems play an important role in preventing attacks which have been increased rapidly due to the dependence on network and Internet connectivity. Deep learning algorithms are promising techniques, which have been used in many classification problems. In the same way, multi-agent systems become a new useful approach in intrusion detection field. In this paper, we propose a deep learning-based multi-agent system for intrusion detection which combines the desired features of multi-agent system approach with the precision of deep learning algorithms. Therefore, we created a number of autonomous, intelligent and adaptive agents that implanted three algorithms, namely autoencoder, multilayer perceptron and k-nearest neighbors. Autoencoder is used as features reduction tool, and multilayer perceptron and k-nearest neighbors are used as classifiers. The performance of our model is compared against traditional machine learning approaches and other multi-agent system-based systems. The experiments have shown that our hybrid distributed intrusion detection system achieves the detection with better accuracy rate and it reduces considerably the time of detection.

Background Accurate detection of pneumothorax on chest radiographs, the most common complication of percutaneous transthoracic needle biopsies (PTNBs), is not always easy in practice. A computer-aided detection (CAD) system may help detect pneumothorax. Purpose To investigate whether a deep learning-based CAD system can improve detection performance for pneumothorax on chest radiographs after PTNB in clinical practice. Materials and Methods A CAD system for post-PTNB pneumothorax detection on chest radiographs was implemented in an institution in February 2020. This retrospective cohort study consecutively included chest radiographs interpreted with CAD assistance (CAD-applied group; February 2020 to November 2020) and those interpreted before implementation (non-CAD group; January 2018 to January 2020). The reference standard was defined by consensus reading by two radiologists. The diagnostic accuracy for pneumothorax was compared between the two groups using generalized estimating equations. Matching was performed according to whether the radiograph reader and PTNB operator were the same using the greedy method. Results A total of 676 radiographs from 655 patients (mean age: 67 years ± 11; 390 men) in the CAD-applied group and 676 radiographs from 664 patients (mean age: 66 years ± 12; 400 men) in the non-CAD group were included. The incidence of pneumothorax was 18.2% (123 of 676 radiographs) in the CAD-applied group and 22.5% (152 of 676 radiographs) in the non-CAD group (P = .05). The CAD-applied group showed higher sensitivity (85.4% vs 67.1%), negative predictive value (96.8% vs 91.3%), and accuracy (96.8% vs 92.3%) than the non-CAD group (all P < .001). The sensitivity for a small amount of pneumothorax improved in the CAD-applied group (pneumothorax of <10%: 74.5% vs 51.4%, P = .009; pneumothorax of 10%-15%: 92.7% vs 70.2%, P = .008). Among patients with pneumothorax, 34 of 655 (5.0%) in the non-CAD group and 16 of 664 (2.4%) in the CAD-applied group (P = .009) required subsequent drainage catheter insertion. Conclusion A deep learning-based computer-aided detection system improved the detection performance for pneumothorax on chest radiographs after lung biopsy. © RSNA, 2022 See also the editorial by Schiebler and Hartung in this issue.

The Controller Area Network (CAN) is an established standard for inter-connecting onboard Electronic Control Units (ECUs) in a vehicle. Through sensors and actuators, ECUs maintain critical vehicle functions such as transmission and engine control. However, security was never a part of CAN design and hence ECUs are susceptible to a wide range of attacks. Thus, in recent years, several anomaly detection systems have been proposed for the CAN bus in order to detect anomalies caused by adversarial attacks or misbehaving sensors. These systems generally try to detect deviations from individual sensor's expected behavior. As such, they are ineffective against attacks that target multiple sensors to accomplish a collective desired behavior without changing the expected behavior of each individual sensor. In this paper, we focus on detecting such attacks by identifying contextual CAN anomalies in realtime. To this end, we present NeuroCAN, a deep learning-based detection system that utilizes Linear embeddings and Long Short Term Memory (LSTM) units to learn the spatio-temporal correlations among sensor data on the CAN bus at a frame level. By exploiting such correlations, NeuroCAN is able to detect contextual anomalies that are otherwise difficult to detect by analyzing individual sensor data. We evaluate NeuroCAN using two publicly available CAN datasets and compare it against existing approaches. Our results show that NeuroCAN achieves over 95% detection accuracy and performs significantly better than the existing baselines.

Development of Photovoltaic abnormal condition detection system using combined regression and Support Vector Machine

Automatic abnormal detection of video homework is an effective method to improve the efficiency of homework marking. Based on the video homework review of “big data acquisition and processing project of actual combat” and other courses, this paper found some student upload their videos with poor images, face loss or abnormal video direction. However, it is time-consuming for teachers to pick out the abnormal video homework manually, which results in prompt feedback to students. This paper puts forward the AVHADS (Abnormal Video Homework Automatic Detection System). The system uses suffix and parameter identification, Open CV, and the audio classification model based on MFCC feature to realize the automatic detection and feedback of abnormal video homework. Experimental results show the AVHADS is feasible and effective.

A population explosion has resulted in garbage generation on a large scale. The process of proper and automatic garbage collection is a challenging and tedious task for developing countries. This paper proposes a deep learning-based intelligent garbage detection system using an Unmanned Aerial Vehicle (UAV). The main aim of this paper is to provide a low-cost, accurate and easy-to-use solution for handling the garbage effectively. It also helps municipal corporations to detect the garbage areas in remote locations automatically. This automation was derived using two Convolutional Neural Network (CNN) models and images of solid waste were captured by the drone. Both models were trained on the collected image dataset at different learning rates, optimizers and epochs. This research uses symmetry during the sampling of garbage images. Homogeneity regarding resizing of images is generated due to the application of symmetry to extract their characteristics. The performance of two CNN models was evaluated with the state-of-the-art models using different performance evaluation metrics such as precision, recall, F1-score, and accuracy. The CNN1 model achieved better performance for automatic solid waste detection with 94% accuracy.

This paper proposes a deep learning-based physical fitness motion system, named iFitness, which consists of a camera, an AI edge module, and a fitness management platform. iFitness uses a camera to capture images of the elderly when they perform physical fitness exercises. Experimental results demonstrate that iFitness can effectively recognize the fitness exercise status for elderly.

ObjectivesTo investigate the effectiveness of BMAX, a deep learning-based computer-aided detection system for detecting fibrosing interstitial lung disease (ILD) on chest radiographs among non-expert and expert physicians in the real-world...