Traditional Detection Methods Research Articles

EC-YOLO: Improved YOLOv7 Model for PCB Electronic Component Detection.

Electronic components are the main components of PCBs (printed circuit boards), so the detection and classification of ECs (electronic components) is an important aspect of recycling used PCBs. However, due to the variety and quantity of ECs, traditional target detection methods for EC classification still have problems such as slow detection speed and low performance, and the accuracy of the detection needs to be improved. To overcome these limitations, this study proposes an enhanced YOLO (you only look once) network (EC-YOLOv7) for detecting EC targets. The network uses ACmix (a mixed model that enjoys the benefits of both self-attention and convolution) as a substitute for the 3 × 3 convolutional modules in the E-ELAN (Extended ELAN) architecture and implements branch links and 1 × 1 convolutional arrays between the ACmix modules to improve the speed of feature retrieval and network inference. Furthermore, the ResNet-ACmix module is engineered to prevent the leakage of function data and to minimise calculation time. Subsequently, the SPPCSPS (spatial pyramid pooling connected spatial pyramid convolution) block has been improved by replacing the serial channels with concurrent channels, which improves the fusion speed of the image features. To effectively capture spatial information and improve detection accuracy, the DyHead (the dynamic head) is utilised to enhance the model's size, mission, and sense of space, which effectively captures spatial information and improves the detection accuracy. A new bounding-box loss regression method, the WIoU-Soft-NMS method, is finally suggested to facilitate prediction regression and improve the localisation accuracy. The experimental results demonstrate that the enhanced YOLOv7 net surpasses the initial YOLOv7 model and other common EC detection methods. The proposed EC-YOLOv7 network reaches a mean accuracy (mAP@0.5) of 94.4% on the PCB dataset and exhibits higher FPS compared to the original YOLOv7 model. In conclusion, it can significantly enhance high-density EC target recognition.

A Network Intrusion Detection Method Based on Bagging Ensemble

The problems of asymmetry in information features and redundant features in datasets, and the asymmetry of network traffic distribution in the field of network intrusion detection, have been identified as a cause of low accuracy and poor generalization of traditional machine learning detection methods in intrusion detection systems (IDSs). In response, a network intrusion detection method based on the integration of bootstrap aggregating (bagging) is proposed. The extreme random tree (ERT) algorithm was employed to calculate the weights of each feature, determine the feature subsets of different machine learning models, then randomly sample the training samples based on the bootstrap sampling method, and integrated classification and regression trees (CART), support vector machine (SVM), and k-nearest neighbor (KNN) as the base estimators of bagging. A comparison of integration methods revealed that the KNN-Bagging integration model exhibited optimal performance. Subsequently, the Bayesian optimization (BO) algorithm was employed for hyper-parameter tuning of the base estimators’ KNN. Finally, the base estimators were integrated through a hard voting approach. The proposed BO-KNN-Bagging model was evaluated on the NSL-KDD dataset, achieving an accuracy of 82.48%. This result was superior to those obtained by traditional machine learning algorithms and demonstrated enhanced performance compared with other methods.

Ultra-selective pyrene-based fluorescent sensor for detection of Fe+3 in neat aqueous medium

Transition metals, particularly Fe+3, play vital roles in biological and environmental systems, yet their chronic elevation can lead to severe diseases. Early detection of Fe+3 is crucial for diagnosing associated ailments. While traditional detection methods have drawbacks, fluorescent sensors offer promising alternatives due to their ease of use, cost-effectiveness, and real-time monitoring capability. Pyrene-based sensors have demonstrated notable sensitivity and selectivity for Fe+3, but challenges remain in achieving applicability in neat aqueous systems. We present two novel turn-off pyrene-based sensors, APTS (sodium 8-aminopyrene-1,3,6-trisulfonate) and APSS (sodium (E)-4-hydroxy-3-((pyren-1-ylimino)methyl)benzenesulfonate), incorporating sulfonate groups to enable their use in pure water samples and achieve nanomolar-level detection limits. Through meticulous experimental and characterization efforts, we elucidate the significance of the imine linkage in enhancing selectivity towards Fe+3 ions. Our sensors exhibit exceptional selectivity and sensitivity, with LODs of 45.6 nM and 45.9 nM for APTS and APSS, respectively, surpassing environmental protection guidelines. Additionally, binding stoichiometry studies reveal insights into the interaction mechanisms, supported by fluorescence lifetime analysis and DFT studies. APSS particularly stands out for its lack of interference from other metal ions, notably Cu2+ as well as Fe2+, offering promising potential for real-time applications and development of paper strip tests. The practical applicability of our sensors is demonstrated through successful Fe+3 detection in various water samples with recoveries ranging from 94 % to 104 %. Overall, our study highlights the effectiveness of APTS and APSS as robust fluorescent probes, with implications for environmental monitoring and biomedical diagnostics.

Anomaly detection in groundwater monitoring data using LSTM-Autoencoder neural networks.

Groundwater monitoring data can be prone to errors and biases due to various factors like borehole and equipment malfunctions, or human mistakes. These inaccuracies can jeopardize the groundwater system, leading to reduced efficiency and potentially causing partial or complete failures in the monitoring system. Traditional anomaly detection methods, which rely on statistical and time-variant techniques, struggle to handle the complex and dynamic nature of anomalies. With advancements in artificial intelligence and the growing need for effective anomaly detection and prevention across different sectors, artificial neural network methods are emerging as capable of identifying more intricate anomalies by considering both temporal and contextual aspects. Nonetheless, there is still a shortage of comprehensive studies on groundwater anomaly detection. The intricate patterns of sequential data from groundwater present numerous challenges, necessitating sophisticated modeling techniques that combine mathematics, statistics, and machine learning for viable solutions. This paper introduces a model designed for high accuracy and efficient computation in detecting anomalies in groundwater monitoring data through a probabilistic approach. We employed the Monte Carlo method and SEAWAT numerical simulation to ascertain the uncertainty in groundwater salinity. Subsequently, a Long Short-Term Memory (LSTM)-Autoencoder model was trained and evaluated, forming the basis of an anomaly detection framework. Each piece of training data was assessed by the LSTM-Autoencoder using the Negative Log Likelihood (NLL) score and a predefined threshold to determine the data's abnormality percentage. The accuracy evaluation of the proposed LSTM-Autoencoder algorithm revealed that this approach achieved commendable performance, with an accuracy of 98.47% in anomaly detection.

Dynamic community detection algorithm based on hyperbolic graph convolution

PurposeCommunity detection is a key factor in analyzing the structural features of complex networks. However, traditional dynamic community detection methods often fail to effectively solve the problems of deep network information loss and computational complexity in hyperbolic space. To address this challenge, a hyperbolic space-based dynamic graph neural network community detection model (HSDCDM) is proposed.Design/methodology/approachHSDCDM first projects the node features into the hyperbolic space and then utilizes the hyperbolic graph convolution module on the Poincaré and Lorentz models to realize feature fusion and information transfer. In addition, the parallel optimized temporal memory module ensures fast and accurate capture of time domain information over extended periods. Finally, the community clustering module divides the community structure by combining the node characteristics of the space domain and the time domain. To evaluate the performance of HSDCDM, experiments are conducted on both artificial and real datasets.FindingsExperimental results on complex networks demonstrate that HSDCDM significantly enhances the quality of community detection in hierarchical networks. It shows an average improvement of 7.29% in NMI and a 9.07% increase in ARI across datasets compared to traditional methods. For complex networks with non-Euclidean geometric structures, the HSDCDM model incorporating hyperbolic geometry can better handle the discontinuity of the metric space, provides a more compact embedding that preserves the data structure, and offers advantages over methods based on Euclidean geometry methods.Originality/valueThis model aggregates the potential information of nodes in space through manifold-preserving distribution mapping and hyperbolic graph topology modules. Moreover, it optimizes the Simple Recurrent Unit (SRU) on the hyperbolic space Lorentz model to effectively extract time series data in hyperbolic space, thereby enhancing computing efficiency by eliminating the reliance on tangent space.

Molecular epidemiology of Brucella abortus isolated from the environment in Ningxia Hui autonomous region, China

Brucellosis is among the key zoonotic infectious diseases in China, and The Ningxia Hui Autonomous Region represents a major endemic area, and it is one of the main causes of poverty in the region due to illness. In Ningxia, there is substantial research on Brucella melitensis, studies on the molecular epidemiology of Brucella abortus are notably scarce. Consequently, this study aims to undertake pathogenic isolation and molecular epidemiological research on Brucella abortus isolated from the environment in Ningxia, providing insights and evidence to advance the prevention and control measures for brucellosis in the region. Building on traditional pathogenic detection methods, this research employs whole-genome sequencing(WGS) techniques and bioinformatics software to conduct a phylogenetic comparison of Ningxia strains and strains of Brucella abortus from various geographical origins. The results indicate that four Brucella abortus strains are classified as biovar 3 and MLST type ST2. It is shown that the local strains were closer phylogenetic relationships with strains from Asian and European countries. The presence of Brucella abortus in certain environmental sectors of Ningxia indicates a risk of transmission from the environment to animals and subsequently to humans. In conclusion, the Brucella abortus exists in some farming environments in Ningxia, and exists for a long time. Therefore, it is necessary to strengthen the monitoring of the disinfection effect of the farming environment to provide a basis for the forward movement of the gate of brucellosis prevention and control.

Intelligent Recognition of Tool Wear with Artificial Intelligence Agent

Tool wear, closely linked to operational efficiency and economic viability, must be detected and managed promptly to prevent significant losses. Traditional methods for tool wear detection, though somewhat effective, often lack precision and require extensive manual effort. Advancements in artificial intelligence (AI), especially through deep learning, have significantly progressed, providing enhanced performance when combined with tool wear management systems. Recent developments have seen a notable increase in the use of AI agents that utilise large language models (LLMs) for specific tasks, indicating a shift towards their integration into manufacturing processes. This paper provides a comprehensive review of the latest advancements in AI-driven tool wear recognition and explores the integration of AI agents in manufacturing. It highlights the LLMS and the various types of AI agents that enhance AI’s autonomous capabilities, discusses the potential benefits, and examines the challenges of this integrative approach. Finally, it outlines future research directions in this rapidly evolving field.

The use of the Isolation Forest model for anomaly detection in measurement data

The subject of the research is the Isolation Forest model, which is a powerful and efficient tool for detecting anomalies in measurement data and outliers, applicable in various fields where ensuring high accuracy and reliability of measurements is important. The goal of the study is to apply the Isolation Forest model to identify unusual or anomalous patterns that differ from typical patterns in the output data. This is achieved by isolating anomalous patterns from normal ones through the construction of multiple different decision trees. The task of the research is to detect outliers in data obtained during the preparation for international comparisons on the state primary standard for mass and volume flow rate of fluid, mass and volume of fluid flowing through a pipeline, by measuring with a сoriolis flowmeter. Data collected during metrological studies undergo processing by the model to detect anomalies. This model analyzes the data and identifies anomalous or outlier values that may indicate systematic or random measurement errors. It enables quick and efficient detection of even the smallest deviations in the data, helping to maintain high accuracy and reliability of measurement results. The main methods for detecting outliers in statistical analysis, which are distribution-independent, are the Grubbs' criterion, interquartile range distribution, and standard deviation. They are sensitive to sample size but are simple and understandable tools. However, the Isolation Forest model also has its limitations, particularly it can be resource-demanding for large datasets. Additionally, it is necessary to consider that using the model requires proper parameter tuning to achieve optimal results. The results of the research include assessment of the Isolation Forest model's effectiveness by comparing it with traditional outlier detection methods. Comparative analysis of the results of different approaches to the same task is an effective method for evaluating the model's performance. Conclusion. The article concludes with the perspective of further research development in this direction. The work will focus on further developing methods for detecting anomalies in measurement data and improving the accuracy and reliability of measurement results in various application fields, which can find broad applications in science and industry.

Flexible Surface-Enhanced Raman Scattering (SERS) sensor for residue-free pesticide detection based on agriculture 4.0 concepts

Given the health and environmental risks associated with pesticide misuse, achieving zero pesticide residues is pivotal in Agriculture 4.0. Traditional detection methods such as HPLC and GC-MS are both complex and time-consuming. In contrast, surface-enhanced Raman scattering (SERS) offers rapid detection and high sensitivity, aligning with the efficiency and accuracy requirements of Agriculture 4.0. In this study, we developed a high-performance flexible substrate using a simple chemical synthesis method. We used material adsorption, anisotropic charge attraction and electron migration to facilitate the growth of the substrate material, which promoted more 'hot spots' and electron transfer, and continued optimisation of the preparation process, which resulted in a significant increase in spectral intensity. The substrate demonstrated outstanding detection capability, with a detection limit of 10−15 M for thiabendazole (TBZ). In practical fruit testing, TBZ residue concentrations on the surface of American cherries could be detected as low as 10−8 mg/mL, well below China's prescribed limit of 5 mg/kg. Relative to previous work in the field of pesticide detection, our study has lower detection limits and concentrations, as well as lower cost and simpler preparation, which provide more options for the detection of very low concentrations of hazardous substances. At the same time, it was confirmed that SERS is consistent with the zero pesticide residue goal of Agriculture 4.0, demonstrating practical efficacy and accuracy within this framework.

Persistent luminescence encoding for rapid and accurate oral-derived bacteria identification

The dysbiosis of oral microbiota contributes to diseases such as periodontitis and certain cancers by triggering the host inflammatory response. Developing methods for the immediate and sensitive identification of oral microorganism is crucial for the rapid diagnosis and early interventions of associated diseases. Traditional methods for microbial detection primarily include the plate culturing, polymerase chain reaction and enzyme-linked immunosorbent assay, which are either time-consuming or laborious. Herein, we reported a persistent luminescence-encoded multiple-channel optical sensing array and achieved the rapid and accurate identification of oral-derived microorganisms. Our results demonstrate that electrostatic attractions and hydrophobic-hydrophobic interactions dominate the binding of the persistent luminescent nanoprobes to oral microorganisms and the microbial identification process can be finished within 30 min. Specifically, a total of 7 oral-derived microorganisms demonstrate their own response patterns and were differentiated by linear discriminant analysis (LDA) with the accuracy up to 100% both in the solution and artificial saliva samples. Moreover, the persistent luminescence encoded array sensor could also discern the microorganism mixtures with the accuracy up to 100%. The proposed persistent luminescence encoding sensor arrays in this work might offer new ideas for rapid and accurate oral-derived microorganism detection, and provide new ways for disease diagnosis associated with microbial metabolism.

The implementation of robotic dogs in automatic detection and surveillance of red imported fire ant nests

AbstractBACKGROUNDThe Red Imported Fire Ant (RIFA), scientifically known as Solenopsis invicta, is a destructive invasive species causing considerable harm to ecosystems and generating substantial economic costs globally. Traditional methods for RIFA nests detection are labor‐intensive and may not be scalable to larger field areas. This study aimed to develop an innovative surveillance system that leverages artificial intelligence (AI) and robotic dogs to automate the detection and geolocation of RIFA nests, thereby improving monitoring and control strategies.RESULTSThe designed surveillance system, through integrating the CyberDog robotic platform with a YOLOX AI model, demonstrated RIFA nest detection precision rates of >90%. The YOLOX model was trained on a dataset containing 1118 images and achieved a final precision rate of 0.95, with an inference time of 20.16 ms per image, indicating real‐time operational suitability. Field tests revealed that the CyberDog system identified three times more nests than trained human inspectors, with significantly lower rates of missed detections and false positives.CONCLUSIONThe findings underscore the potential of AI‐driven robotic systems in advancing pest management. The CyberDog/YOLOX system not only matched human inspectors in speed, but also exceeded them in accuracy and efficiency. This study's results are significant as they highlight how technology can be harnessed to address biological invasions, offering a more effective, ecologically friendly, and scalable solution for RIFA detection. The successful implementation of this system could pave the way for broader applications in environmental monitoring and pest control, ultimately contributing to the preservation of biodiversity and economic stability. © 2024 Society of Chemical Industry.

Event-based secure control for cyber-physical systems against false data injection attacks

This paper mainly studies an event-based secure control problem for cyber-physical systems against false data injection (FDI) attacks. First, a dynamic event-triggered transmission protocol is established to schedule the transmitted data from the sensor to the controller. Subsequently, an imperfect FDI attack model is constructed considering the real network environment, which would bypass the traditional residual-based detection methods. To overcome this drawback, an attack detection strategy based on secure history information is designed, which effectively mitigates the negative impact of uncertainty on the detection results. Based on the detection results, an event-based secure linear quadratic Gaussian control scheme is designed, which can guarantee system performance and reduce the communication load effectively. Finally, a simulation experiment verifies that the proposed control method outperforms other algorithms in the literature, and the detection accuracy is increased by 91% to 98%.

Early and late blight disease identification in tomato plants using a neural network-based model to augmenting agricultural productivity.

Computer-advanced technologies have a significant impact across various fields. It is widely recognized that diseases have a detrimental effect on crop productivity and can significantly impact the economy, particularly in agricultural countries. Tomatoes hold great economic importance among cash crops, second only to potatoes. Globally, tomato production reaches a staggering 160 million tons annually, making it even more crucial for agricultural development. Unfortunately, the tomato crop is susceptible to several diseases, with early blight and late blight as two prominent culprits responsible for a production decrease of around 79%. Traditional disease detection and identification methods are time-consuming, expensive, and destructive, often requiring pathologists' expertise. Thus, the primary research objective is to enhance disease identification accuracy by leveraging deep learning techniques. A model based on the inception-V3 architecture has been devised to classify diseases affecting tomato plant leaves. The model was trained and tested using the PlantVillage dataset, which comprises 6000 sample images of tomato leaves. The training and testing process utilized an 80 : 20 ratio, resulting in an impressive classification accuracy of 97.44% for the proposed model. The proposed solution aims to enable the tomato industry to thrive in the global market by mitigating the impact of tomato leaf diseases. By reducing the prevalence of these diseases, the solution can increase demand and contribute to the industry's growth.

Identification of Cortex Cercis chinensis Decoction Pieces from Different Growth Origins Using Raman Spectroscopy

The complexity of traditional Chinese medicine (TCM) components and the time-consuming of traditional detection methods make it necessary and meaningful to establish rapid and efficient identification techniques. This study explores the potential of Raman spectroscopy, a non-destructive technique offering details of molecular structure, for rapid and accurate identification. Cortex Cercis chinensis (CCC) decoction pieces from diverse geographical origins, Anhui, Sichuan, Zhejiang, and Hubei, were collected and analyzed using Raman spectroscopy at 785 nm, and the Raman characteristic peaks were analyzed. MATLAB software was employed to analyze the similarity between the spectra of CCC decoction pieces, and the original Raman spectral data were transformed into first and second derivative spectra. The results revealed distinct Raman spectral characteristics of carbohydrates and glycosidic bonds (characteristic peaks at 480, 531, 549, 873, 946 and 1086 cm−1). The correlation coefficients of the all the four samples from different origins ranged from 0.9625 to 0.9912, while the coincidence coefficients ranged from 0.9602 to 0.9934. The first and second derivative demonstrated significantly different peaks within specific ranges, 180–200, 280–380, and 680–740 cm−1 for first derivatives, 160–300, 340–400 and 420–480 cm−1 for second derivatives. These obvious differences in first and second derivative spectra of Raman spectra of CCC decoction pieces demonstrated the different growth origins. In conclusion, the study demonstrated the ability of Raman spectroscopy to accurately differentiate CCC decoction pieces from different geographical growth origin. These findings provided a basis for further application of Raman spectra characteristic fingerprints to be used in quality control for rapid identification of the quality and origin of TCM raw materials.

Malware Detection Using Hidden Markov Model

Malware is a broad term for harmful software that poses significant threats by damaging computer systems and spreading across networks. Traditional detection methods include signature-based and heuristic-based techniques, which are effective against known malware but struggle with new, unknown variants, particularly sophisticated ones like metamorphic, encrypted, and polymorphic viruses. Hence, this research aims at improving malware detection, specifically targeting metamorphic malware that can evade traditional detection methods. The study shows the effectiveness of dynamic analysis over static analysis for detecting metamorphic malware due to its ability to adapt to the malware's constant changes. The dynamic analysis involves examining malware behavior during execution, using dynamic software birthmarks and the Hidden Markov Model to identify malicious activities. It therefore recommends the use of dynamic analysis in the detection of patterns of metamorphic malware. Keywords: Hidden markov models, Malware detection, Dynamic analysis and Static analysis

A dataset of drilling site object detection in underground coal mines

Drilling in underground coal mine is an important measure for dealing with gas, water and hidden geological disasters, which can significantly enhance the effectiveness of disaster prevention and control in coal mining operations. In order to monitor the drilling process in real time and improve drilling efficiency, it is necessary to carry out object detection to identify and locate key targets at the drilling site. Compared with traditional object detection method, deep learning-based object detection method can improve accuracy, timeliness and stability of object detection, but it requires high-quality datasets to perform well. At present, research on object detection in underground coal mine drilling sites mainly relies on small-scale private datasets, which are insufficient for providing necessary or reliable data for deep neural network model training. In this study, we constructed a dataset of drilling site object detection using photos taken by intrinsic safety law enforcement recorders. This dataset is developed through several steps, including data cleaning, data labeling, and expert sampling verification. The mainstream YOLO series object detection model is used for data quality assessment. This dataset comprises 70,948 images from drilling sites under different environmental conditions, covering five categories of objects: gripper, chuck, coal miner, mine safety helmet, and drill pipe. It provides annotated files in PASCAL VOC format. This dataset can provide strong data support for object detection research in underground coal mine drilling sites, and plays an important role in promoting intelligent underground coal mine monitoring and early warning.

A Survey on Malware Detection with Graph Representation Learning

Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization to unknown attacks and can be easily circumvented using obfuscation techniques. In recent years, Machine Learning (ML) and notably Deep Learning (DL) achieved impressive results in malware detection by learning useful representations from data and have become a solution preferred over traditional methods. Recently, the application of Graph Representation Learning (GRL) techniques on graph-structured data has demonstrated impressive capabilities in malware detection. This success benefits notably from the robust structure of graphs, which are challenging for attackers to alter, and their intrinsic explainability capabilities. In this survey, we provide an in-depth literature review to summarize and unify existing works under the common approaches and architectures. We notably demonstrate that Graph Neural Networks (GNNs) reach competitive results in learning robust embeddings from malware represented as expressive graph structures such as Function Call Graphs (FCGs) and Control Flow Graphs (CFGs). This study also discusses the robustness of GRL-based methods to adversarial attacks, contrasts their effectiveness with other ML/DL approaches, and outlines future research for practical deployment.

A Portable Automated Microfluidic Platform for Point-of-Care Testing for Multiple Mycotoxins in Wine.

Food safety requires point-of-care testing (POCT) for mycotoxins, since their presence in wine significantly impacts the wine industry and poses a severe threat to human life. Traditional detection methods are usually limited to detecting one mycotoxin and cannot achieve high-throughput, automated, and rapid quantitative analysis of multiple mycotoxins in real samples. Here, we propose a portable automated microfluidic platform (PAMP) integrating a chemiluminescence (CL) imaging system and a microfluidic chip to realize POCT for multiple mycotoxins in real samples, simplifying complex manual operations, shortening the detection time, and improving the detection sensitivity. Specially, silicone films were used as substrates on microfluidic chips to incubate mycotoxin conjugations, and the streptavidin-biotin (SA-B) system and an indirect immunoassay were implemented on silicone films to improve the sensitivity of reaction results. Interestingly, these methods significantly improved detection results, resulting in sensitive detection of mycotoxins, including zearalenone (ZEA) ranging from 1 to 32 ng/mL, aflatoxin B1 (AFB1) ranging from 0.2 to 6.4 ng/mL, and ochratoxin A (OTA) ranging from 2 to 64 ng/mL. The recovery of samples reached 91.39-109.14%, which verified the reliability and practicability of the PAMP. This PAMP enables sensitive and rapid detection of multiple mycotoxins in markets or wineries that lack advanced laboratory facilities. Therefore, it is essential to develop a portable microfluidic platform for POCT to detect mycotoxins in real samples.

Improved faster region convolutional neural network algorithm for UAV target detection in complex environment

With the development of artificial intelligence technology and Unmanned Aerial Vehicle (UAV) technology, the traditional UAV target detection methods are difficult to achieve high detection accuracy in complex and changeable scenes. The paper selects Faster Region Convolutional Neural Network (Faster R-CNN) as the basic algorithm due to its high scalability and excellent classification performance. The convolution mode of the convolution layer in the network structure is adjusted to deformable convolution. Additionally, a hybrid attention mechanism module is added to combine channel attention and spatial attention modules behind the output layer of the Faster R-CNN network. Finally, the Soft Non-Maximum Suppression (Soft-NMS) method is selected to combine the improved Faster R-CNN algorithm of multiple individuals into the final target detection model. The performance of the improved Faster R-CNN algorithm and the original Faster R-CNN algorithm was verified through the VisDrone 2018-DET dataset and the full class average accuracy Mean Average Precision (mAP), accuracy and precision. The accuracy and logarithm of loss values of the improved Faster R-CNN algorithm and the original Faster R-CNN algorithm's Region Proposal Network (RPN) were 0.985, 0.981, 0.018, and 0.052, respectively. The target detection model based on the hybrid attention mechanism of ResNet-50 network fusion Spartial Attention Module (SAM) and Channel Attention Module (CAM) had the best classification performance, with the accuracy, precision and overall average accuracy of 25.8 %, 24.6 % and 22.7 %, respectively. The results are helpful to improve the target detection ability of UAV in complex environment, and contribute to the development of target detection technology in the future.

Approach to Detect Windows Malware Based on Malicious Tendency Image and ResNet Algorithm

Timely detection of self-replicating malware in the high market share Windows operating system can effectively prevent personal or corporate financial losses. The form and characteristics of malware are constantly evolving, leading to a concept drift issue that gradually decreases the effectiveness of traditional detection methods. Therefore, we propose WinMDet, a Windows malware detection method based on malicious tendency image and ResNet algorithm. First, to tackle the complexity and difficulty in accurately characterizing malware features, WinMDet retains detailed malware features and encodes them into malicious tendency images to better describe malware across different periods. Secondly, WinMDet utilizes previously generated malicious tendency images to train the initial detection model. Then, to alleviate the issue of malware concept drift, WinMDet employs Local Maximum Mean Discrepancy (LMMD) as the criterion for model transfer, enhancing the initial detection model’s ability to distinguish between malware and benign software. We conducted a comprehensive evaluation of WinMDet using common metrics such as accuracy, precision and recall. The results indicate that WinMDet performs remarkably well in terms of accuracy, exceeding 82%. Additionally, significant improvements were observed in precision and recall, surpassing 82.42% and 82.06%, respectively. After employing our LMMD-based transfer method, the initial detection model improved the detection accuracy of malware in 2021 and 2022 by approximately 4.22% to 8.06%. The false negative rate decreased by at most 4.34%, and the false positive rate decreased by at most 4.61%.