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22nd Mediterranean Communication and Computer Networking Conference (MedComNet)

作者： De Rosa, Sara Gringoli, Francesco Bellicini, Gabriele Univ Brescia Dept Informat Engn Brescia Italy Verxo SrL Brescia Italy

ISBN: (纸本)9798350390476;9798350390483

This paper investigates the effectiveness of AI-based tools, with a focus on utilizing ChatGPT as a test platform, in email phishing detection, addressing the pressing need to combat this pervasive threat in the digital landscape. With phishing attacks causing substantial financial losses amounting to billions of dollars annually, innovative approaches are essential to mitigate their impact. We propose leveraging ChatGPT for email phishing detection, capitalizing on its machine learning and natural language processing capabilities to classify emails based on nuanced patterns and variations in phishing techniques. Unlike traditional rule-based approaches, AI-based tools offer a promising avenue for enhanced detection accuracy. Through rigorous testing focusing on binary mail classification, incorporating metrics such as accuracy, specificity, sensitivity, precision, and the F1 Score, ChatGPT demonstrates commendable performance, achieving an overall accuracy of 75.75% and an impressive sensitivity of 98.4%. These findings underscore the potential of AI-based tools as valuable assets in mitigating phishing threats. Comparison with FortiSandbox, a widely-used solution, reveals promising potential for AI-based tools, albeit with recognition of the need for further development and refinement. Our evaluation, encompassing various aspects of .eml file analysis, highlights both the strengths and limitations of AI-based tools in identifying phishing emails. While this research lays a strong foundation for leveraging AI-based tools in email phishing detection, there exist opportunities for improvement. Addressing identified limitations and exploring future research directions will be crucial in advancing the efficacy of AI-based tools and enhancing email security measures against cyber threats.

关键词： ChatGPT phishing email Artificial Intelligence cybersecurity machine learning natural language processing detection algorithms

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Fluctuation-enhanced gas sensing by two-dimensional materials

Fluctuation-enhanced gas sensing by two-dimensional material...

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

作者： Smulko, Janusz Gdansk Univ Technol Fac Elect Telecommun & Informat Gdansk Poland

ISBN: (纸本)9798350380903;9798350380910

The perspectives of gas sensing by flicker noise measurements have been presented for two-dimensional materials characterized by a high surface-to-volume ratio. These materials exhibit a wide range of electrical properties, from almost metallic (e.g., graphene) to semiconducting (e.g., MoS2, WS2, ZrS3, etc.) of various energy bandgaps. Two-dimensional materials exhibit imperfections, partially responsible for the flicker noise generated in their structures. Recent results show how these imperfections can be modulated to advance gas selectivity. Some materials exhibit photocatalytic phenomena induced by irradiation of multiple wavelengths. This effect can adjust the gas sensor to improve its selectivity and sensitivity. Low-frequency noise mechanisms in two-dimensional materials at different operating conditions will be presented, focusing on possible practical applications. The problems of noise measurements will also be enclosed.

关键词： gas sensors two-dimensional materials flicker noise detection algorithms noise measurements

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Multilayer Concept Drift detection Method Based on Model Explainability

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IEEE ACCESS 2024年 12卷 190791-190808页

作者： Zhang, Haolan Chen, Xinyi Hu, Min Sugumaran, Vijayan Shanghai Univ SHU UTS SILC Business Sch Shanghai 201800 Peoples R China Virginia Polytech Inst & State Univ Myers Lawson Sch Construct Blacksburg VA 24060 USA Shanghai Univ SHU SUCG Res Ctr Bldg Industrializat Shanghai 200072 Peoples R China Oakland Univ Sch Business Adm Rochester MI 48309 USA

Timely detection of concept drift plays a vital role in ensuring the stability and reliability of data-driven models. However, existing concept drift detection methods face challenges in achieving a proper balance between accuracy and timeliness while also disregarding the precise localization of the drift point. To address these issues, this paper presents a novel three-layer drift detection algorithm named Hierarchical Concept Drift detection based on SHapley Additive exPlanations (HCDD-SHAP). Initially, concept drift is identified by monitoring changes in the error rate, ensuring the promptness of drift detection. Subsequently, the SHAP method is incorporated to assess the features' contribution to drift, thereby enhancing the precision of drift detection. Lastly, a drift point backtracking mechanism is introduced to accurately pinpoint the initial drift point, facilitating the rapid creation of new models. Experiments show that HCDD-SHAP exhibits superior performance on synthetic and real-world datasets, surpassing conventional drift detectors in terms of precision, recall, and detection delay across diverse detection scenarios. Additionally, in the context of a shield construction project, HCDD-SHAP demonstrates its ability to swiftly identify variations in dynamic engineering conditions, which underscores the considerable advantage of the proposed algorithm in scenarios that require real-time monitoring and control.

关键词： Concept drift Predictive models Adaptation models Accuracy Error analysis detection algorithms Data models Nonhomogeneous media Real-time systems Heuristic algorithms drift detection model explainability SHapley additive exPlanations

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PDD: Post-Disaster Dataset for Human detection and Performance Evaluation

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Song, Haoqian Song, Weiwei Cheng, Long Wei, Yue Cui, Jinqiang Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Guangdong Lab Artificial Intelligence & Digital E Shenzhen 518107 Peoples R China

Human detection is aimed at automatically labeling specific semantic objects in high-resolution images, which is a key problem in the post-disaster search and rescue (SAR) mission with unmanned aerial vehicles (UAVs). However, for large-scale and efficient search and statistics with UAVs, there is a lack of real-world image datasets of post-disaster ruins, resulting in poor performance in practical applications. In this article, we conducted a comprehensive review of current studies and created a post-disaster dataset (PDD) for human detection first, which contains the real-world post-disaster UAV images from multiple scenes, perspectives, and distances. Second, based on the YOLOv5 algorithm, we verified the validity and superiority of PDD by comparing and analyzing its detection performance with other datasets and on different training proportions. Finally, we compared and evaluated the detection performance of 11 state-of-the-art algorithms on PDD, including faster region-based convolutional neural networks (R-CNN), YOLOv5, YOLOv7, YOLOv8, improved YOLOv5 (im-YOLOv5), neural architecture search (YOLO-NAS), detection transformer (DETR), deformable DETR (DDETR), dynamic anchor box DETR (DAB-DETR), denoising DETR (DN-DETR), and DETR with improved denoising anchor box (DINO), and provided a performance analysis of different deep learning algorithms. The results demonstrate that you only look once (YOLO)-based algorithms have a better real-time statistical performance, while the DETR-based algorithms have more accurate box prediction capabilities. The PDD, codes, and models are available at https://***/HaoqianSong/Post-Disaster-Dataset.

关键词： detection algorithms Remote sensing YOLO Cameras Autonomous aerial vehicles Convolutional neural networks Search problems Human detection multiview image performance evaluation post-disaster ruins scene unmanned aerial vehicle (UAV) search and rescue (SAR)

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Heuristic Optimization based Abnormal Posture detection Algorithm

Heuristic Optimization based Abnormal Posture Detection Algo...

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IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW)

作者： Li, Yufeng Shang, Lin Pan, Peng Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

ISBN: (数字)9781665470506

ISBN: (纸本)9781665470506

This paper studies the abnormal posture detection algorithm based on heuristic optimization. Using the data collected by sensors, the features such as acceleration and angular velocity are extracted and put into the classifiers for training. We select the appropriate heuristic algorithm according to different classifier models for optimization. The results demonstrate that, in binary classification experiment, the accuracy ratio of the K-Nearest Neighbor (KNN) model is 99.54%, and the AUC is 0.99. In quad classification experiment, The Support Vector Machine (SVM) model has a 94.32% accuracy ratio and a 0.95 AUC, which has the optimal performance.

关键词： Support vector machines Training Heuristic algorithms Sensor phenomena and characterization Feature extraction Data mining detection algorithms

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Mi-DETR: For Mitosis detection From Breast Histopathology Images an Improved DETR

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IEEE ACCESS 2024年 12卷 179235-179251页

作者： Ardac, Fatma Betul Kara Erdogmus, Pakize Duzce Univ Fac Engn Dept Comp Engn TR-81620 Duzce Turkiye

In histopathological image analysis, the detection and count of mitotic cells are important biomarkers for determining the degree and aggressiveness of cancer prognosis. Manual detection of mitosis by pathologists is a lengthy and challenging process. With advancements in deep learning architectures, numerous automatic mitotic detection methods have been proposed. However, most mitotic detection methods lack generalizability across image areas and are not consistently reproducible in multi-center environments. To overcome these issues, a new automatic mitotic detection approach called the Mi-DETR, based on the DETR architecture, has been proposed. In the proposed Mi-DETR model, the backbone of the original DETR is replaced by CSPResNeXt. The aim of this is to strengthen the learning capacity in feature extraction and increase the variability of the learned features. In this way, information loss and unwanted gradient flow are avoided. In the decoder layer, unnecessary model parameters have been filtered out using a layer reduction strategy to improve model efficiency and reduce computational costs. Additionally, a more stable model has been obtained by using the CIoU loss function instead of the L1+GIoU loss function used in the DETR model. The publicly available ICPR14 and TUPAC16 breast histopathology datasets were used for training, validation, and testing in the experiments. The results provided more precise and compact bounding boxes close to clinically validated ground truth, demonstrating the accuracy and generalizability of the proposed model. As a result, the proposed Mi-DETR model achieved a 0.921 F1-score on the ICPR14 dataset and a 0.950 F1-score on the TUPAC16 dataset. The results obtained on both datasets demonstrate that the proposed model performs well enough to compete with state-of-the-art deep learning architectures.

关键词： Feature extraction Transformers Computer architecture Object oriented modeling Computational modeling Histopathology Breast cancer Solid modeling Accuracy Training detection algorithms DETR mitosis detection transformer

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Date Fruit detection and Classification Based on Its Variety Using Deep Learning Technology

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IEEE ACCESS 2024年 12卷 190666-190677页

作者： Almutairi, Ahad Alharbi, Jawza Alharbi, Shouq Alhasson, Haifa F. Alharbi, Shuaa S. Habib, Shabana Qassim Univ Coll Comp Dept Informat Technol Buraydah 51452 Saudi Arabia

There are many types of date fruits, each with similar physical properties and slight differences in color, shape, and fleshiness. Due to the many attributes to consider, distinguishing between the different types of dates can be challenging and time consuming. Classifying and sorting the post-harvest fully ripe (Rutab) dates red into edible dates, is an essential stage of the date fruit production industries. Date production industries in Saudi Arabia still perform this process manually, which may result in reduced productivity and an increase in the date fruit waste. The purpose of this study was to use Deep Learning technology to detect and classify them by using the You Only Look Once (YOLO) algorithm. Different YOLO models (YOLOv5, YOLOv7, YOLOv8) have been trained using 1735 images with 9 types of date fruits. The accuracy of this study has been investigated using the metrics of Recall, Precision, and F1-score metrics. Based on the experimental results, YOLOv8 achieves a mean Recall of 0.99 % and Precision of 0.991% with an Intersection over Union (IoU) colorred in the range of [0-1], a mean Average Precision of 0.994%, a mean Average Precision of [0.5:0.95] and a mean Average Precision of [0.5:0.85]. Results show that the used model can accurately detect and classify date fruits based on their surface quality, enhancing the productivity as a result of multiple harvests due to uneven ripening of the fruit.

关键词： YOLO Training Accuracy Measurement Deep learning Data models Sorting Artificial neural networks detection algorithms Machine learning Performance evaluation Artificial neural network deep learning detection performance image analysis machine learning models

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Enhanced Leakage detection and Estimation via a Hybrid Genetic Algorithm and High-Order Sliding Modes Observer Approach

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IEEE ACCESS 2024年 12卷 147841-147850页

作者： Pumaricra-Rojas, David Perez-Zuniga, Gustavo Sotomayor-Moriano, Javier Pontif Catholic Univ Peru Engn Dept San Miguel 15088 Lima Peru

This paper introduces a hybrid approach designed for both detecting and estimating the magnitude of leaks in oil pipelines. The method integrates a High Order Sliding Mode Observer(HOSMO) with a Super Twisting Algorithm to serve as an observer for state estimation of the system. A parameterized model based on momentum and mass balance equations with discretization is used, where the parameters are the location and magnitude of the leakage. To find these parameters, it incorporates the Genetic Algorithm to solve an optimization problem that relies on a function cost related to the error norm between measurements and states estimation from HOSMO in order to measure the difference between the model with an assumed leakage and the real leakage. The solution of the minimization problem represents the leak position and magnitude. The feasibility and effectiveness of this method are evaluated using a simulation model representing a 306 km sector of the North-Peruvian Oil Pipeline. The results demonstrate its robustness against noise, showcasing a precision of +/- 250 m in pinpointing the location of leaks.

关键词： Pipelines Mathematical models Genetic algorithms Observers Oils Biological cells Voltage measurement Transmitters Prototypes Measurement uncertainty Leak detection detection algorithms Genetic algorithm leakage detection oil leakage sliding mode observer

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A YOLO-Based Intelligent detection Algorithm for Risk Assess-Ment of Construction Sites

Journal of Intelligent Construction

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Journal of Intelligent Construction 2025年第4期2卷 1-18页

作者： Ruiyang Feng Yu Miao Junxing Zheng School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan China

Construction safety accidents have become increasingly frequent in recent years, leading to numerous casualties and substantial property losses. These in-cidents are often attributed to inadequate supervision on construction sites and workers' low safety awareness. Traditional manual management methods, which are labor-intensive and resource-consuming, are no longer effective. Therefore, this study proposes a novel single-stage model based on YOLOv8s, designed for two primary purposes: detecting workers' personal protective equipment and monitoring and recognizing when workers enter hazardous ar-eas. The model provides real-time feedback on detection results to reduce the incidence of construction accidents. Additionally, a brief design for distance calculation was introduced. The model was trained for 200 iterations on a Roboflow dataset comprising 103,500 annotated images. Experimental results showed that YOLOv8s outperformed YOLOv8n, YOLOv5s, and YOLOv5n in de-tection performance, achieving a mean average precision with the intersection over union (IoU) threshold set to 50% (mAP50) of 84.0%, precision of 85.0%, and recall of 60.5% across 9 detection classes. By leveraging artificial intelligence technology, this study aims to offer an effective method for enhancing construction site safety, which can be further improved with additional images and a more robust network architecture.

关键词： YOLO Training Adaptation models Accuracy Surveillance Refining Real-time systems Safety detection algorithms Accidents

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Anomaly Monitoring Framework in Lane detection With a Generative Adversarial Network

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第3期22卷 1603-1615页

作者： Kim, Hayoung Park, Jongwon Min, Kyushik Huh, Kunsoo Hanyang Univ Dept Automot Engn Seoul 04763 South Korea

The safety of an automated vehicle requires accurate information of surrounding conditions, because a false sensor output can lead to a fatal accident during driving. Thus, monitoring of abnormalities in every sensor is important for robust perception of the environment. Since it is difficult to obtain anomalous data, it is hard to develop a robust detection algorithm using only a relatively small number of anomalies. In this paper, we propose a data augmentation method for oversampling minority anomalies in lane detection. Using a generative adversarial network that makes the generator learn to estimate the distribution of anomalous data, it generates synthesized minority anomalies. The generated anomalies are used to train an anomaly detection network while minimizing latency for use in real situations. During training, the generated anomalies, with various mixed quality, are sampled differently according to their quality. This helps the detection network to be optimized with better quality data. Experimental result shows that when using the proposed anomaly detection framework for monitoring lane abnormality, it improves the performance by 12% when compared to the vanilla recurrent neural network.

关键词： Anomaly detection Monitoring Generative adversarial networks Training Gallium nitride Robustness detection algorithms Anomaly detection deep learning generative adversarial network data augmentation lane abnormality monitoring

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