Detection Method Research Articles

Research on Software Vulnerability Detection Methods Based on Deep Learning

This paper aims to investigate software vulnerability detection methods based on deep learning to address the ever-growing challenges in software security. With the rapid development of information technology, software vulnerabilities have become the primary targets of cyberattacks, posing severe threats to economic, military, and social security. By analyzing the limitations of existing software vulnerability detection methods, this paper explores the potential applications of deep learning technology in this field. Through a literature review, relevant theories of software vulnerabilities are introduced, including the concept, types, and impacts of vulnerabilities. Subsequently, detailed descriptions of deep learning-based vulnerability detection methods are provided, encompassing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and their variant LSTM, as well as the application of program slicing techniques in vulnerability detection. By evaluating and improving existing deep learning models, a novel deep learning-based vulnerability detection framework is proposed, with its workflow and technical principles elaborated. The proposed deep learning-based vulnerability detection method can effectively enhance the accuracy and efficiency of software vulnerability detection, reduce reliance on expert knowledge, and lower human resource costs. Experimental results demonstrate that this method performs exceptionally well on multiple datasets, indicating broad application prospects and significant research value. The deep learning-based software vulnerability detection method offers new insights and tools for addressing current software security challenges. In the future, with the continuous development and improvement of deep learning technology, this method will play an even more crucial role in the field of software vulnerability detection.

Gas-sensitive synergistic effect of Au-decorated ZnO nano-structured materials for rapid ethanol detection based on simulated sunlight activation

Local surface plasmon resonance (LSPR) and noble metal modification/doping are classical methods for improving the performance of metal-oxide-semiconductor (MOS)-based gas sensors. However, relatively less attention is paid to their synergies. In particular, research on synergistic gas-sensing technology that combines sunlight activation with other methods is significant for using friendly energy. In this study, Au-decorated ZnO (Au/ZnO) nano-structured materials (NMs) are successfully synthesised using a cost-effective nano-seed-assisted chemical bath and UV irradiation growth methods. The sensor based on this material exhibits superior performance in ethanol vapour under simulated sunlight, maintaining high sensitivity and repeatability at a low optimal working temperature. In particular, the sensitivity to 100 ppm of ethanol is 7.5 times better and the response time is 20 times shorter (tres < 1 s) in simulated sunlight than in the dark environment. The limit of detection (LOD) of ethanol is as low as 97 ppb, which is much lower than the concentration in exhaled breath of driving under the influence of alcohol according to Chinese law (20–80 ppm). This study provides a reliable and ultra-fast ethanol detection method, with potential applications in environmental monitoring and traffic safety. A possible gas-sensitive mechanism of the Au/ZnO ethanol vapour sensor is proposed based on the synergistic effect between simulated sunlight activation (LSPR, humidity resistance and thermal activation) and noble metal modification (electron sensitisation and chemical sensitisation). It provides a promising method for exploring the utilisation of sunlight for rapid gas detection.

A position-based method for detecting orbital angular momentum modes

Abstract Due to the inherent limitations of current Orbital Angular Momentum (OAM) mode detection methods and constraints posed by the physical size of receiving antennas, accurately identifying higher-order OAM modes represents a significant challenge. Therefore, there is a pressing necessity to overcome these hurdles. This paper introduces a new approach to OAM mode detection that exploits positional information. Our methodology ascertains the OAM mode by examining the phase and position data of the electric field at any two randomly chosen points within the energy's spatial distribution radiated by vortex electromagnetic waves. This innovation promises not only to precisely detect higher-order OAM modes but also grants greater versatility in the placement of receiving antennas. Moreover, the study delves into the correlation between the electric field phase of vortex electromagnetic waves, the azimuth angle, and the receiving distance. The validity of our approach is confirmed through both simulation outcomes and experimental data derived from physical tests.&amp;#xD;

Object Detection Algorithms for Video Surveillance Applications

Abstract—Object Detection algorithms find applications in various fields, such as defense, security, and healthcare. In this paper, various Object Detection Algorithms, such as face detection, skin detection, color detection, shape detection, and target detection, are simulated and implemented using MATLAB 2017b to detect various types of objects with improved accuracy for video surveillance applications. Further, various challenges and applications of object detection methods are elaborated.

A clenbuterol detection method based on magnetic separation up-conversion fluorescent probe

In this study, a fluorescence detection method combining aptamer-modified up-conversion nanoparticles (UCNPs) and magnetic nanoparticles (MNPs) was developed for detection of Clenbuterol (CLB). The aptamer-modified magnetic NPs captured CLB, which reacted with the aptamer-modified UCNPs and generated a sandwich complex. The aptamer-modified UCNPs acted as a fluorescence source. The MNP-CLB-UCNP complex was retrieved from the solution using an magnetic field, and the fluorescence intensity was detected by fluorescence spectrophotometry with excitation and emission spectra at 980 nm and in the 400–800 nm region, respectively. The results showed that the fluorescence intensity gradually increased with increasing concentrations of CLB with a good specify. The method was highly sensitive for the quantification of CLB, with a limit of detection of 0.304 ng mL−1. The recovery rate of CLB from pork samples ranged from 84 % to 94.87 %. This fluorescence method enables the sensitive, precise, and accurate quantification of CLB residues in pork samples.

A Candy Defect Detection Method Based on StyleGAN2 and Improved YOLOv7 for Imbalanced Data.

Quality management in the candy industry is a vital part of food quality management. Defective candies significantly affect subsequent packaging and consumption, impacting the efficiency of candy manufacturers and the consumer experience. However, challenges exist in candy defect detection on food production lines due to the small size of the targets and defects, as well as the difficulty of batch sampling defects from automated production lines. A high-precision candy defect detection method based on deep learning is proposed in this paper. Initially, pseudo-defective candy images are generated based on Style Generative Adversarial Network-v2 (StyleGAN2), thereby enhancing the authenticity of these synthetic defect images. Following the separation of the background based on the color characteristics of the defective candies on the conveyor belt, a GAN is utilized for negative sample data enhancement. This effectively reduces the impact of data imbalance between complete and defective candies on the model's detection performance. Secondly, considering the challenges brought by the small size and random shape of candy defects to target detection, the efficient target detection method YOLOv7 is improved. The Spatial Pyramid Pooling Fast Cross Stage Partial Connection (SPPFCSPC) module, the C3C2 module, and the global attention mechanism are introduced to enhance feature extraction precision. The improved model achieves a 3.0% increase in recognition accuracy and a 3.7% increase in recall rate while supporting real-time recognition scenery. This method not only enhances the efficiency of food quality management but also promotes the application of computer vision and deep learning in industrial production.

A hybrid approach of vision transformers and CNNs for detection of ulcerative colitis

Ulcerative Colitis is an Inflammatory Bowel disease caused by a variety of factors that lead to a serious impact on the quality of life of the patients if left untreated. Due to complexities in the identification procedures of this disease, the treatment timeline and quality can be severely affected, leading to further consequences for the sufferer. The difficulties in identification are due to high patients to healthcare professionals ratio. Researchers have proposed variety of machine/deep learning methods for automated detection of ulcerative colitis, however, several challenges exists including class imbalance problem, comprehensive feature extraction and accurate classification. We propose a novel method for accurate detection of ulcerative colitis with augmentation techniques to overcome class imbalance issue, a comprehensive feature vector extraction using custom architecture of Vision Transformer (ViT) and accurate classification using customized Convolutional Neural Network (CNN). We used the TMC-UCM and LIMUC datasets in this research for training and testing of proposed method and achieved accuracy of 90% with AUC-ROC scores of 0.91, 0.81, 0.94, and 0.94 for the endoscopic classes of Mayo 0, Mayo 1, Mayo 2, and Mayo 3 respectively. We have compared the proposed method with existing state of the art methods and conclude that the proposed method outperforms the existing methods.

Detection of Mycotoxin Contamination in Foods Using Artificial Intelligence: A Review.

Mycotoxin contamination of foods is a major concern for food safety and public health worldwide. The contamination of agricultural commodities employed by humankind with mycotoxins (toxic secondary metabolites of fungi) is a major risk to the health of the human population. Common methods for mycotoxin detection include chromatographic separation, often combined with mass spectrometry (accurate but time-consuming to prepare the sample and requiring skilled technicians). Artificial intelligence (AI) has been introduced as a new technique for mycotoxin detection in food, providing high credibility and accuracy. This review article provides an overview of recent studies on the use of AI methods for the discovery of mycotoxins in food. The new approach demonstrated that a variety of AI technologies could be correlated. Deep learning models, machine learning algorithms, and neural networks were implemented to analyze elaborate datasets from different analytical platforms. In addition, this review focuses on the advancement of AI to work concomitantly with smart sensing technologies or other non-conventional techniques such as spectroscopy, biosensors, and imaging techniques for rapid and less damaging mycotoxin detection. We question the requirement for large and diverse datasets to train AI models, discuss the standardization of analytical methodologies, and discuss avenues for regulatory approval of AI-based approaches, among other top-of-mind issues in this domain. In addition, this research provides some interesting use cases and real commercial applications where AI has been able to outperform other traditional methods in terms of sensitivity, specificity, and time required. This review aims to provide insights for future directions in AI-enabled mycotoxin detection by incorporating the latest research results and stressing the necessity of multidisciplinary collaboration among food scientists, engineers, and computer scientists. Ultimately, the use of AI could revolutionize systems monitoring mycotoxins, improving food safety and safeguarding global public health.

Construction of heterojunction based on Nd2S3 and tin dioxide for rapid detection of ethanol

The gas-sensing properties of Nd2S3 have not been previously explored. In this study, a novel composite material comprising rare-earth metal sulfide (Nd2S3) and metal oxide (SnO2) was successfully prepared using a two-step hydrothermal method for the rapid detection of ethanol. Characterization results revealed that the morphology and specific surface area of Nd2S3-SnO2 remained largely unchanged after composite formation. However, the built-in electric field established between Nd2S3 and SnO2 significantly enhanced carrier transport and separation. Additionally, the introduction of extra oxygen vacancies increased the content of chemisorbed oxygen, providing more active sites for ethanol molecules, thereby improving the gas-sensing performance. The Nd2S3-SnO2 sensor demonstrated extremely high detection accuracy (R2=0.9965) over the 20–300 ppm ethanol concentration range, with response/recovery time of 8/274 s at 100 ppm ethanol concentration and response value of 93. It also possesses excellent selectivity to ethanol, long-term stability and reversibility. This work represents a preliminary exploration of the gas-sensing properties of Nd2S3 and its underlying mechanisms through the construction of a heterojunction structure between metal sulfide and metal oxide.

Glycan-Evocated Metallization for Amplification-Free Electrochemical Detection of Glycoproteins at Low Concentration Levels.

Glycoproteins have become the most often screened tumor markers in the in vitro diagnostics. Although a large number of electrochemical methods have been proposed to sensitively detect glycoproteins, most of them involve the aid of laborious signal amplification. Herein, we report the use of glycan-evocated metallization (GlyMetal) for the amplification-free electrochemical detection of glycoproteins at low concentration levels. Briefly, the glycoproteins of interest are captured by an aptamer recognition layer, and then the glycans of targets are oxidized by NaIO4 to convert the 1,2-diol sites into aldehyde groups for the silver deposition-based metallization, followed by the electrochemical stripping assay of the deposited metallic silver for glycoprotein quantification via the established solid-state Ag/AgCl voltammetric process. As GlyMetal can enable the deposition of a large amount of metallic silver and a high signal-to-background ratio can be obtained for the solid-state Ag/AgCl voltammetric stripping assay, the developed GlyMetal-based electrochemical method is applicable to the amplification-free detection of glycoproteins. As a proof of concept, a detection limit of 1.65 pg/mL has been achieved for carcinoembryonic antigen (CEA) detection. In addition to the high selectivity, desirable results have been obtained with respect to the use of the method for CEA detection in serum samples. In consideration of the desirable simplicity, short assay time, and cost-effectiveness of the amplification-free approach, the GlyMetal-based electrochemical method shows great promise in the point-of-care detection of glycoproteins.

Explainable AI-based innovative hybrid ensemble model for intrusion detection

AbstractCybersecurity threats have become more worldly, demanding advanced detection mechanisms with the exponential growth in digital data and network services. Intrusion Detection Systems (IDSs) are crucial in identifying illegitimate access or anomalous behaviour within computer network systems, consequently opposing sensitive information. Traditional IDS approaches often struggle with high false positive rates and the ability to adapt embryonic attack patterns. This work asserts a novel Hybrid Adaptive Ensemble for Intrusion Detection (HAEnID), an innovative and powerful method to enhance intrusion detection, different from the conventional techniques. HAEnID is composed of a string of multi-layered ensemble, which consists of a Stacking Ensemble (SEM), a Bayesian Model Averaging (BMA), and a Conditional Ensemble method (CEM). HAEnID combines the best of these three ensemble techniques for ultimate success in detection with a considerable cut in false alarms. A key feature of HAEnID is an adaptive mechanism that allows ensemble components to change over time as network traffic patterns vary and new threats appear. This way, HAEnID would provide adequate protection as attack vectors change. Furthermore, the model would become more interpretable and explainable using Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). The proposed Ensemble model for intrusion detection on CIC-IDS 2017 achieves excellent accuracy (97-98%), demonstrating effectiveness and consistency across various configurations. Feature selection further enhances performance, with BMA-M (20) reaching 98.79% accuracy. These results highlight the potential of the ensemble model for accurate and reliable intrusion detection and, hence, is a state-of-the-art choice for accuracy and explainability.

Carbon dots for pathogen detection and imaging: recent breakthroughs and future trends.

As a class of carbon-based nanomaterials, carbon dots (CDs) have gained a lot of interest for a variety of applications. They offer distinctive optical, chemical, and structural characteristics along with favourable attributes such as low cost, availability of abundant functional groups, remarkable chemical inertness, high stability, exceptional biocompatibility, and ecofriendliness. This review discusses synthesis methods, structural characteristics, and surface modifications of CDs, specific for pathogen detection. Furthermore, it delves into the mechanisms that govern the interaction between pathogens and CDs. In addition, the study explores the use of CDs in a number of detection modalities, such as optical, electrochemical, and electrochemiluminescence, emphasising real-time pathogen monitoring. Moreover, both the challenges and opportunities related to the application of CDs-based detection and imaging methods are highlighted in field and clinical contexts.

Presence of Echinococcus eggs in the environment and food: a review of current data and future prospects.

Cystic and alveolar echinococcosis are considered the second and third most significant foodborne parasitic diseases worldwide. The microscopic eggs excreted in the feces of the definitive host are the only source of contamination for intermediate and dead-end hosts, including humans. However, estimating the respective contribution of the environment, fomites, animals or food in the transmission of Echinococcus eggs is still challenging. Echinococcus granulosus and E. multilocularis seem to have a similar survival capacity regarding temperature under laboratory conditions. In addition, field experiments have reported that the eggs can survive several weeks to years outdoors, with confirmation of the relative susceptibility of Echinococcus eggs to desiccation. Bad weather (such as rain and wind), invertebrates and birds help scatter Echinococcus eggs in the environment and may thus impact human exposure. Contamination of food and the environment by taeniid eggs has been the subject of renewed interest in the past decade. Various matrices from endemic regions have been found to be contaminated by Echinococcus eggs. These include water, soil, vegetables and berries, with heterogeneous rates highlighting the need to acquire more robust data so as to obtain an accurate assessment of the risk of human infection. In this context, it is essential to use efficient methods of detection and to develop methods for evaluating the viability of eggs in the environment and food.

Damage identification method for jacket platform based on dual-channel model

To address the challenges posed by noisy vibration signals and underutilized time-series data in jacket platforms, this paper proposes a dual-channel damage detection method that integrates Temporal Convolutional Networks (TCN) and Gated Recurrent Units (GRU) in parallel. A multi-head attention mechanism (MA) is employed to reassign feature weights, improving detection accuracy. The optimized features are fused using the Concatenate function for the final output. Two experimental scenarios—isolated noise and ocean noise—were designed to evaluate the method. The results demonstrate that the combination of TCN, GRU and MA effectively detects damage in offshore platforms, surpassing other deep learning models. Although the method shows strong potential for real-world applications, further testing in more complex ocean environments is required to address potential limitations in handling highly variable noise patterns.

Evaluation of some anions in groundwater in Riyadh, Saudi Arabia, and human health risk assessment of nitrate and fluoride.

Groundwater is a vital source of water for human and agricultural use in many parts of the world. The purpose of this research was to establish the quality of groundwater in Riyadh, Saudi Arabia, as well as the human health concerns associated with it. We collected and examined groundwater samples for pH, EC, TDS, CaCO3, fluoride (F-), chloride (Cl-), sulfate (SO42-), and nitrate (NO3-). The ion chromatography conductometric detection method was constructed to determine fluoride, chloride, sulfate, and nitrate in groundwater. The suggested method worked well for the anions that were being studied; it had a high coefficient of determination (r2 > 0.998) and average recoveries for all analytes that were between 97.5% and 99.0%, with a range of error of 0.77 to 2.37%. Fluoride concentrations were detected between 0.001 and 0.14mg/L, which are within the acceptable limit by several organizations. Chloride was measured in the range of 17.1 to 966.5mg/L, with some samples above the limits. The influence on sulfate ranged from 2.0 to 1136.0mg/L, with several samples exceeding the limits. In contrast, with nitrate levels ranging from 1.4 to 5.0mg/L, the majority of the samples fall within the acceptable range. The overall intake of fluoride, chloride, sulfate, and nitrate is 0.00605, 138.911, 65.515, and 1.19, respectively, which is lower than the recommended daily consumption except for chloride. The groundwater sample contains fluoride and nitrate with HQ values less than one: 0.000064-0.0641 and 0.033654-0.120192. Humans in Riyadh, Saudi Arabia, do not pose a health risk when digesting or absorbing groundwater fluoride or nitrate.

Multi-Modal Object Detection Method Based on Dual-Branch Asymmetric Attention Backbone and Feature Fusion Pyramid Network

With the simultaneous acquisition of the infrared and optical remote sensing images of the same target becoming increasingly easy, using multi-modal data for high-performance object detection has become a research focus. In remote sensing multi-modal data, infrared images lack color information, it is hard to detect difficult targets with low contrast, and optical images are easily affected by illuminance. One of the most effective ways to solve this problem is to integrate multi-modal images for high-performance object detection. The challenge of fusion object detection lies in how to fully integrate multi-modal image features with significant modal differences and avoid introducing interference information while taking advantage of complementary advantages. To solve these problems, a new multi-modal fusion object detection method is proposed. In this paper, the method is improved in terms of two aspects: firstly, a new dual-branch asymmetric attention backbone network (DAAB) is designed, which uses a semantic information supplement module (SISM) and a detail information supplement module (DISM) to supplement and enhance infrared and RGB image information, respectively. Secondly, we propose a feature fusion pyramid network (FFPN), which uses a Transformer-like strategy to carry out multi-modal feature fusion and suppress features that are not conducive to fusion during the fusion process. This method is a state-of-the-art process for both FLIR-aligned and DroneVehicle datasets. Experiments show that this method has strong competitiveness and generalization performance.

Adverse events associated with carbamazepine: a pharmacovigilance study using the FDA Adverse Event Reporting System

ABSTRACT Introduction Carbamazepine (CBZ) is a commonly used antiseizures medications (ASM), but its adverse drug reactions (ADRs) can impact the successful management of epilepsy. There are currently limited systematic studies on ADRs related to CBZ, necessitating further investigation. Areas covered Using the FDA Adverse Event Reporting System (FAERS) database , we extracted reports where CBZ was the primary suspect, conducting subgroup analyses stratified by sex and age. Four risk signal detection methods ROR, PRR, BCPNN, and EGBM were employed to systematically analyze the ADRs associated with CBZ. Expert opinion In the epilepsy population, ADRs related to CBZ often involve examinations, hereditary disorders, and infections. Subgroup analysis showed differences in ADR signals among male, female, elderly, and young patients. Human Herpesvirus 6 Infection and Dermatitis Exfoliative were consistent CBZ-induced ADRs, unaffected by age or sex. The study also identified previously overlooked ADRs such as bone metabolism abnormalities, ocular toxicity, and ototoxicity. Many ADRs linked to CBZ remain underreported. It is crucial to enhance monitoring and information dissemination about CBZ use in epileptic patients. Adjusting medication regimens for high-risk individuals, potentially incorporating acupuncture, traditional Chinese medicine, or alternative anti-seizure medications, should be considered when necessary.

Significance of Viable But Non-culturable (VBNC) State in Vibrios and Other Pathogenic Bacteria: Induction, Detection and the Role of Resuscitation Promoting Factors (Rpf).

Still, it remains a debate after four decades of research on surviving cells, several bacterial species were naturally inducted and found to exist in a viable but non-culturable (VBNC) state, an adaptive strategy executed by most bacterial species under different stressful conditions. VBNC state are generally attributed when the cells lose its culturability on standard culture media, diminish in conventional detection methods, but retaining its viability, virulence and antibiotic resistance over a period of years and may poses a risk to marine animals as well as public health and food safety. In this present review, we mainly focus the VBNC state of Vibrios and other human bacterial pathogens. Exposure to several factors like nutrient depletion, temperature fluctuation, changes in salinity and oxidative stress, antibiotic and other chemical stress can induce the cells to VBNC state. The transcriptomic and proteomic changes during VBNC, modification in detection techniques and the most significant role of Rpf in conversion of VBNC into culturable cells. Altogether, detection of unculturable VBNC forms has significant importance, since it may not only regain its culturability, but also reactivate its putative virulence determinants causing serious outbreaks and illness to the individual.

Enhancing spatial domain detection in spatial transcriptomics with EnSDD

Advancements in spatial transcriptomics have transformed our understanding of organ function and tissue microenvironment. However, accurately identifying spatial domains to depict genome heterogeneity and cellular interactions remains a challenge. In this study, we propose EnSDD (Ensemble-learning for Spatial Domain Detection), a method that ingeniously integrates eight state-of-the-art spatial domain detection methods to automatically identify spatial domains. A key innovation of EnSDD is its dynamic weighting mechanism within the ensemble learning process, which optimizes the contribution of each base model and provides a performance evaluation metric without the need for ground truth data. By leveraging the spatial domains identified through EnSDD, we incorporate the detection of domain-specific spatially variable genes and the spatial distribution of cell types, thereby providing deeper insights into tissue heterogeneity. We validate EnSDD across diverse spatial transcriptomics datasets from various tissue organizational structures. Our results demonstrate that EnSDD significantly enhances spatial domain identification accuracy, identifies genes with spatial expression patterns, and reveals domain-specific cell type enrichment patterns, offering invaluable insights into tissue spatial heterogeneity and regionalization.

Mouse Models of HER2+ Human Breast Cancer: A Literature Review

Introduction: HER2+ breast cancer (BC) makes up 15-20% of BC cases, where HER2 overexpression drives BC progression via proliferative signals. Targeted therapies inhibit HER2's activity but face challenges like resistance and metastasis. Thus, HER2+ BC is a developing research area where many preclinical studies are being conducted, and mice are often used due to their genetic and physiological similarities to humans. This study aims to review and compare existing mouse models to determine the best model of HER2+ BC. Methods: A literature search on PubMed in February 2024 using search terms including HER2, neu, neuNT, MMTV, and mammary tumors identified 16 primary research articles that used Neu-overexpressing mice. The papers were categorized under five models: MMTV-Neu, MMTV-NeuNT, FloxNeo-NeuNT, MMTV-NIC, and MMTV-HER2. Results: Among examined studies, Andrechek et al.'s FloxNeo-NeuNT model had the longest average tumor onset at 447 days, while Kuang et al.'s MMTV-NIC had the shortest at 126 days. Ursini-Segal et al.'s MMTV-NIC mice showed 100% mammary tumor incidence. Most models resulted in mammary adenocarcinomas, with lung metastases commonly observed, especially in papers that used MMTV-NIC. Discussion: Human HER2+ BC is commonly adenocarcinoma and often metastasizes to lungs, bones, liver, and brain. The mouse models were also found to be adenocarcinomas, but they primarily showed lung metastasis, possibly due to insufficient tumor detection methods for brain and bone metastasis. Neu copy numbers in Andrechek et al.’s FloxNeo-NeuNT model also align with findings on HER2 copy number amplification in human HER2+ BC. Conclusion: We have found that MMTV-NIC is the optimal mouse model for HER2+ BC research due to its short latency, 100% tumor incidence, and high rate of lung metastasis.