Accurate Detection Research Articles

Multi-modal denoised data-driven milling chatter detection using an optimized hybrid neural network architecture

Chatter, a type of self-excited vibration, deteriorates surface quality and reduces tool life and machining efficiency. Chatter detection serves as an effective approach to achieve stable cutting. To address the low accuracy in chatter detection caused by the limitations of both one-dimensional temporal and two-dimensional image modal information, this study proposes a multi-modal denoised data-driven milling chatter detection method using an optimized hybrid neural network architecture. A data denoising model combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Singular Value Decomposition (SVD) is established. The Ivy algorithm is employed to optimize the hyperparameters of CEEMD-SVD. Multi-modal data features of different machining states are then obtained using time–frequency domain methods and Markov transition field methods. Sensitivity analysis of time–frequency domain features is conducted using Pearson correlation coefficient analysis. A hybrid neural network model (DBMA) for chatter detection is constructed by integrating dual-scale parallel convolutional neural networks, bidirectional gated recurrent units, and multi-head attention mechanisms. The Ivy algorithm is utilized to optimize the hyperparameters of DBMA. The t-SNE algorithm is employed to visualize features extracted from different network layers of the chatter detection model. Results demonstrate that effective denoising of machining signals and the use of multi-modal data can significantly improve the accuracy of state detection. Compared with other methods, the proposed model exhibits superior stability and robustness.

Location-Based Technology for Real-Time Artifact Recognition in Businesses

The recognition of historical artifacts play a crucial role in sustaining cultural heritage and advancing tourism. Despite advancements in object detection technologies, accurately identifying artifacts in diverse geographical and environmental contexts remains a significant challenge. Existing models often struggle to adapt to region-specific features and the complexity of historical artifacts, limiting their practical applications. To address these limitations, this study evaluates the potential of YOLOv4, YOLOv7-X, and YOLOv9c models for historical artifact recognition, with a particular focus on location-based segmentation. Geographically distinct datasets were utilized for training and evaluation, enabling the models to achieve higher accuracy in region-specific artifact detection. Among the tested models, YOLOv9c demonstrated superior performance, achieving the highest metrics across accuracy (96%), precision (93%), recall (95%), and mean average precision (mAP, 71%), making it the best-performing model. These results highlight YOLOv9c’s robustness and adaptability to complex datasets and diverse artifact characteristics. A user-friendly application interface was also developed, allowing real-time detection and providing detailed historical information about the artifacts. However, challenges such as the high computational cost of training YOLOv9c on high-resolution datasets were observed, particularly when compared to YOLOv4, which was computationally efficient but less accurate. YOLOv7-X offered a balance between performance and computational efficiency. The results demonstrate that location-based segmentation significantly enhances detection accuracy, making this approach highly effective for real-world applications in cultural heritage preservation and tourism.

Analysis of Different Noise Reduction Effects of Ground Penetrating Radar Image of Cavity Behind Tunnel Primary Support Based on Singular Value Decomposition and Wavelet Transform

The cavity behind the lining structure is a typical disease in tunnel engineering. Cavity seriously affects the interaction between lining and surrounding rock, resulting in uneven bearing of lining structure and stress concentration, which can easily induce lining concrete cracking, water leakage and other diseases. Accurate identification of lining structure cavities is the key to ensure the normal operation of the tunnel. However, the ground penetrating radar detection images of lining cavities often contain interference signals such as background noise, which seriously affects the accuracy and clarity of cavity detection. The application effects of wavelet transform and singular value decomposition in image denoising and resolution of tunnel lining cavity ground penetrating radar are studied. Under this background, the model test of regular and irregular cavity detection behind tunnel lining is carried out, in which the irregular cavity is located in the surrounding rock environment of filling soil to simulate the real situation of tunnel. The measured images of lining cavity under different working conditions are obtained, and the image denoising analysis is carried out. The results show that singular value decomposition can effectively suppress noise. The ground penetrating radar image after singular value decomposition denoising is clearer, and the image resolution is effectively improved, thus realizing the accurate identification of cavity diseases behind the primary support of the tunnel

Research on Blood Cell Image Detection Method Based on Fourier Ptychographic Microscopy

Autonomous Fourier Ptychographic Microscopy (FPM) is a technology widely used in the field of pathology. It is compatible with high resolution and large field-of-view imaging and can observe more image details. Red blood cells play an indispensable role in assessing the oxygen-carrying capacity of the human body and in screening for clinical diagnosis and treatment needs. In this paper, the blood cell data set is constructed based on the FPM system experimental platform. Before training, four enhancement strategies are adopted for the blood cell image data to improve the generalization and robustness of the model. A blood cell detection algorithm based on SCD-YOLOv7 is proposed. Firstly, the C-MP (Convolutional Max Pooling) module and DELAN (Deep Efficient Learning Automotive Network) module are used in the feature extraction network to optimize the feature extraction process and improve the extraction ability of overlapping cell features by considering the characteristics of channels and spatial dimensions. Secondly, through the Sim-Head detection head, the global information of the deep feature map (mean average precision) and the local details of the shallow feature map are fully utilized to improve the performance of the algorithm for small target detection. MAP is a comprehensive indicator for evaluating the performance of object detection algorithms, which measures the accuracy and robustness of a model by calculating the average precision (AP) under different categories or thresholds. Finally, the Focal-EIoU (Focal Extended Intersection over Union) loss function is introduced, which not only improves the convergence speed of the model but also significantly improves the accuracy of blood cell detection. Through quantitative and qualitative analysis of ablation experiments and comparative experimental results, the detection accuracy of the SCD-YOLOv7 algorithm on the blood cell data set reached 92.4%, increased by 7.2%, and the calculation amount was reduced by 14.6 G.

A Review on: DDoS Detection in Cloud-Based Website Hosting Using Covariance Matrix Approach

Abstract: Particularly in cloud and IoT systems, this work investigates how the covariance matrix might improve Distributed Denial of Service (DDoS) detection in challenging network situations. Still a constant difficulty, DDoS attacks take advantage of the linked character of contemporary networks. Improving detection accuracy, reducing false positives, and allowing real-time harmful traffic identification is the aim. The suggested approach analyses interactions between several network traffic characteristics using the covariance matrix, therefore spotting abnormalities suggestive of DDoS activity. This method captures subtle, multi-dimensional patterns usually missed by volume-based or signature-based techniques, therefore transcending conventional approaches. In cloud and IoT environments, comprehensive datasets encompassing both valid & hostile traffic assist the models to be trained. Dynamic adaptation to shifting assault pathways and advanced machine learning methods serve to enhance the detection process. Comparative analysis reveals that by basically achieving superior accuracy and resilience, the covariance matrix-based approach overcomes low-rate and zero-day threats. Strong network security is thus guaranteed even in multi-tenant, high-traffic environments since the results demonstrate a clear decrease in false positives and improved detection rates. This work offers a foundation for sophisticated, customizable anomaly detection techniques, therefore offering a possible way to guard critical infrastructure from sophisticated DDoS attacks.

ENHANCING ONLINE PROCTORING EFFICIENCY

The transition to online examinations has necessitated robust proctoring mechanisms to ensure integrity and fairness. Traditional online proctoring systems often rely on human invigilators or basic monitoring software, both of which are prone to inefficiencies, high operational costs, and scalability challenges. This paper proposes an AI-based proctoring system that addresses two critical challenges: detecting disruptive sounds and identifying pre-existing infractions in online examination environments. The system integrates sound classification using a convolutional neural network (CNN) and visual recognition techniques for infraction detection. Extensive testing across diverse scenarios demonstrated high detection accuracy, low false positive rates, and real-time responsiveness. The results highlight the system's potential to transform online proctoring practices, ensuring reliable examination monitoring while maintaining candidate privacy and convenience.

Framework for Apple Phenotype Feature Extraction Using Instance Segmentation and Edge Attention Mechanism

A method for apple phenotypic feature extraction and growth anomaly identification based on deep learning and natural language processing technologies is proposed in this paper, aiming to enhance the accuracy of apple quality detection and anomaly prediction in agricultural production. This method integrates instance segmentation, edge perception mechanisms, attention mechanisms, and multimodal data fusion to accurately extract an apple’s phenotypic features, such as its shape, color, and surface condition, while identifying potential anomalies which may arise during the growth process. Specifically, the edge transformer segmentation network is employed to combine deep convolutional networks (CNNs) with the Transformer architecture, enhancing feature extraction and modeling long-range dependencies across different regions of an image. The edge perception mechanism improves segmentation accuracy by focusing on the boundary regions of the apple, particularly in the case of complex shapes or surface damage. Additionally, the natural language processing (NLP) module analyzes agricultural domain knowledge, such as planting records and meteorological data, providing insights into potential causes of growth anomalies and enabling more accurate predictions. The experimental results demonstrate that the proposed method significantly outperformed traditional models across multiple metrics. Specifically, in the apple phenotypic feature extraction task, the model achieved exceptional performance, with accuracy of 0.95, recall of 0.91, precision of 0.93, and mean intersection over union (mIoU) of 0.92. Furthermore, in the growth anomaly identification task, the model also performed excellently, with a precision of 0.93, recall of 0.90, accuracy of 0.91, and mIoU of 0.89, further validating its efficiency and robustness in handling complex growth anomaly scenarios. The method’s integration of image data with agricultural knowledge provides a comprehensive approach to both apple quality detection and growth anomaly prediction, offering reliable decision support for agricultural production. The proposed method, by integrating image data with agricultural domain knowledge, provides precise decision support for agricultural production, not only improving the efficiency and accuracy of apple quality detection but also offering reliable technical assurance for agricultural economic analysis.

A Survey on Liver Cancer Detection Using Hyperfusion of CNN and SVM in Machine Learning

Since liver cancer ranks among of the most aggressive renditions of the disease, improving patient outcomes requires early identification. We propose an inventive tactic to liver cancer detection by integrating CNN and SVM. CNNs, known for their powerful feature extraction capabilities, are particularly effective in analysing complex medical images. SVMs, on the other hand, are efficient classifiers that can separate data points in high-dimensional spaces with accuracy. By merging the feature extraction strength of CNN with the classification efficiency of SVM, the proposed model aims to enhance liver cancer detection accuracy and robustness. The experimental results reveal that the fused CNN-SVM model significantly surpasses the performance of standalone CNN and SVM models, achieving a high detection accuracy of 95.2%. This hybrid method offers a promising direction for improving the precision of computer-aided diagnosis systems, contributing to more effective and reliable liver cancer detection methods that can assist healthcare professionals in making timely decisions.

The revolutionary impact of ai in the automotive industry

The automotive industry is undergoing a revolutionary transformation through the integration of Artificial Intelligence (AI) technologies. This article examines the multifaceted impact of AI across manufacturing, autonomous vehicle development, smart factory solutions, and customer experience enhancement. The article highlights significant improvements in manufacturing efficiency, with AI-driven quality control systems achieving high accuracy in defect detection and reducing production costs substantially. In autonomous vehicle development, AI systems have demonstrated remarkable capabilities, with object detection accuracy reaching exceptional levels under optimal conditions and decision-making architectures processing multiple discrete decisions per second. The implementation of AI in smart factories has led to considerable improvement in overall equipment effectiveness, while customer experience enhancements have resulted in a significant increase in satisfaction scores. The integration of AI in supply chain management and market intelligence has yielded substantial operational improvements, with demand forecasting accuracy reaching impressive levels for short-term predictions and enabling meaningful reduction in inventory costs.

Point-of-care ultrasound of the common carotid arteries for detection of large vessel occlusion stroke: Results of the POCUS-LVO study.

Distal arterial occlusions can cause measurable changes in the flow wave profile in proximal segments of the feeding artery. Our objective was to study the diagnostic accuracy of point-of-care ultrasound (POCUS) of the common carotid arteries (CCA) for detection of anterior circulation large vessel occlusion (ac-LVO) in patients with suspected stroke. We conducted a prospective, single-center, observational study of adult patients with suspected stroke admitted in the emergency department. Flow wave profiles of both CCAs were generated by non-specialists using POCUS as soon as possible after admission. ac-LVO was defined as an internal carotid artery or M1 occlusion in CT- or MR-angiography. The diagnostic performances for detection of ac-LVO using flow wave parameters were calculated. Among 283 patients recruited during a 10-month period, 257 patients (91%) had CCA ultrasound images of sufficient quality and were included for analysis. The mean age was 75 years (IQR 62-83), 131 were female (51.0%), median baseline NIHSS was 2 (IQR 0-5). The most frequent final diagnosis was ischemic stroke (49.4%), ac-LVO was present in 30 patients (11.9%). The median duration of POCUS was 3 min (IQR 2-5). Among all flow wave parameters, the highest diagnostic accuracy for ac-LVO detection was found for end-diastolic velocity difference between sides (AUC = 0.90, 95%CI = 0.85-0.93), with a specificity of 83% (95%CI = 78-88%) at a predefined sensitivity threshold of 80%. POCUS of the CCA in patients with suspected stroke can predict the presence of ac-LVO. These results need to be replicated in a prehospital setting.

Pancreatic Stone Protein in patients with liver failure: a prospective pilot cohort study.

Pancreatic Stone Protein (PSP) seems to have higher accuracy for sepsis detection compared to other biomarkers. As PSP has never been studied in patients with liver failure (LF), our purpose was to assess its accuracy for diagnosis of infection and prognosis in this population. We conducted a prospective pilot cohort study on patients with LF consecutively admitted to the Intensive Care Unit of a liver transplant center in 2021-2023. Ongoing overt infection was an exclusion criterion. Daily measurements of biomarkers were performed until discharge, death, or for 21 days. Analysis was performed by adjusting the baseline for the first infection episode (median on D3), which was the reference for those non-infected. Sixteen patients were included, 7 with acute and 9 with acute-on-chronic LF. Median age was 54 (interquartile range 42-64) years, half were female, with admission SOFA score of 10 (IQR 8-12). Hospital mortality was 43.8% (n = 7). An infection was observed in 8 patients, who presented non-significantly higher levels of PSP than non-infected ones during follow-up. Levels were higher in non-survivors than survivors (p < 0.05 from D4 on and since the day of infection considering only infected patients). Similarly, patients under renal replacement therapy had higher PSP levels than others (p < 0.05, D2 to D7 after admission). This pilot study provides early insights into PSP kinetics, suggesting a potential role for prognosis in patients with LF. PSP rises in both ALF and ACLF to levels sustainably higher than those expected for healthy adults. Further research is needed to reassess its diagnostic accuracy for infection and redefine cut-offs in this population.

Challenges and opportunities: Implementing RPA and AI in fraud detection in the banking sector

Integrating Robotic Process Automation (RPA) and Artificial Intelligence (AI) in the banking sector for fraud detection is a significant change, but it comes with challenges and opportunities. With financial institutions subjected to ever more sophisticated fraud attempts, RPA and AI present themselves as a means of increasing capabilities to detect and prevent fraud. With RPA, repetitive tasks like transaction monitoring and alert generation can be automated, freeing human analysts to analyze complex cases. Machine learning and predictive analytics enable AI to learn patterns and anomalies within large amounts of datasets, identify anomalies, and provide warnings early to fraud activity. Yet, these technologies still need to be integrated, and challenges persist. There are key issues of data privacy and security, integration with legacy systems, initial costs of implementation, and the like. Banks must operate in a world of ever-tightening regulatory control while maintaining the integrity and security of their systems. However, the opportunities are great. With RPA and AI implemented, there would additionally be an increase in the accuracy and speed of fraud detection, resulting in less financial loss and more customer faith. In addition, these technologies are scalable and flexible, which means banks can change with the changing threats. There's also a compelling cost case: reductions over time, based on improved efficiency and reduced manual intervention, make these attractive investments. Other best practices and lessons gained from these successful case studies are discussed. Also, the shift in future trends has been kept in mind, such as the future of AI technology and the changing regulatory environment, which will define the next generation of fraud detection in banking. The challenges to utilizing these technologies and their opportunities are revealed in a balanced view for the benefit of stakeholders so that they can utilize these technologies fully.

Object Detection &amp; Analysis with Deep CNN and Yolov8 in Soft Computing Frameworks

Object detection is one of the major roles in deep learning and soft computing which contributes to many real time cases in healthcare, agriculture etc. This study proposes a deep learning-based approach for object detection to detect lung cancer and diagnosis by utilizing deep Convolutional Neural Networks (CNN) and the YOLOv8 model, with DICOM (Digital Imaging and Communications in Medicine) images for robust image analysis. Lung cancer remains one of the leading causes of mortality worldwide, necessitating early and accurate detection to improve patient outcomes. The primary objective of the research is to enhance the accuracy, precision, and recall rates in lung cancer detection, while reducing false positives and false negatives, through advanced machine learning techniques. In the proposed work, CNN is employed for feature extraction, enabling the model to capture the intricate patterns present in the DICOM images. YOLOv8, a cutting-edge object detection algorithm, is integrated to detect cancerous regions with high efficiency and speed. A comparative analysis is conducted with traditional machine learning classifiers such as Support Vector Machine (SVM), AdaBoost, Random Forest, and K-Nearest Neighbors (KNN), demonstrating the superior performance of the proposed deep learning models. The experimental results reveal that the CNN-YOLOv8 model achieves remarkable accuracy of 94 Parsant, with a precision of 93.56 Parsant, recall of 92 Parsant, ROC score of 93 Parsant, and an F-score of 94.60 Parsant. These findings underscore the effectiveness of deep learning in lung cancer detection, significantly outperforming conventional models in terms of accuracy and reliability. The novelty of this research lies in the integration of CNN with YOLOv8, specifically optimized for medical DICOM images, which allows for real-time, accurate identification of lung cancer while maintaining computational efficiency.

Real Time Anomaly Detection and Intrusion Detection for Safeguarding Intra-Vehicle Communication Powered by AI

This study addresses cyber-attacks in Electric Vehicles (EVs) and proposes an intelligent, secure framework to protect both in-vehicle and vehicle-to-vehicle communication systems. The proposed model uses an improved support vector machine (SVM) for anomaly and intrusion detection based on the Controller Area Network (CAN) protocol a critical component in vehicle communication. To further enhance detection speed and accuracy a new optimization algorithm the Social Spider Optimization (SSO) is introduced for reinforcing the offline training process. Simulation results on real-world datasets demonstrate the model's high performance, reliability and ability to defend against denial-of-service (DoS) attacks in EVs.

Enhancing cybersecurity risk management in fintech through advanced analytics and machine learning

The rapid growth of the fintech sector has amplified the need for robust cybersecurity risk management frameworks to safeguard sensitive financial data and ensure operational continuity. This abstract explores the transformative role of advanced analytics and machine learning (ML) in enhancing cybersecurity for fintech companies. By leveraging these technologies, organizations can build proactive defenses, improve threat detection accuracy, and reduce response times to cyber incidents. Advanced analytics enable fintech companies to process large volumes of real-time data, identifying anomalies and potential vulnerabilities with unparalleled precision. Techniques such as predictive modeling and behavior analysis allow for the early detection of sophisticated threats, including phishing, ransomware, and advanced persistent attacks. Machine learning algorithms enhance these capabilities by continuously learning from evolving cyber threats, adapting to new attack vectors, and optimizing detection mechanisms. Incorporating machine learning into cybersecurity risk management frameworks also facilitates automated responses to identified threats. AI-powered systems can assess the severity of attacks, prioritize remediation efforts, and deploy countermeasures with minimal human intervention, significantly reducing downtime and potential financial losses. Additionally, these systems can generate actionable insights, enabling fintech organizations to strengthen their cybersecurity posture and comply with regulatory requirements. Despite its benefits, implementing advanced analytics and ML in cybersecurity presents challenges, such as the risk of algorithmic biases, high resource demands, and the complexity of integrating these tools into existing systems. Addressing these barriers requires a strategic approach, including robust training datasets, investment in scalable technologies, and collaboration between fintech firms and cybersecurity experts. This investigation underscores the critical role of advanced analytics and machine learning in shaping the future of cybersecurity risk management within the fintech ecosystem. By adopting these technologies, fintech companies can enhance their resilience against cyber threats, protect customer trust, and drive sustainable growth in a rapidly digitizing financial landscape.

Feature Learning via Correlation Analysis for Effective Duplicate Detection

With the growing reliance on software, the frequency of software bugs has increased significantly. To address these issues, users or developers typically submit bug reports, which developers analyze and resolve. However, many submitted bug reports are duplicates of previously reported issues, creating inefficiencies in the bug resolution process. To enhance developer productivity, an automatic method for detecting duplicate bug reports is essential. In this study, we present a novel approach for identifying duplicate and nonduplicate bug reports using feature learning through correlation analysis. Our method utilizes bug report features, including product and component information, extracted from bug repositories. The process begins with preprocessing the bug reports to ensure data quality. Next, a feature selection algorithm identifies relevant features, which are then used to train a machine learning model based on bidirectional encoder representations from transformers (BERT). The proposed model’s effectiveness was evaluated across multiple datasets: Apache, JDT, Platform, KDE, Core, Firefox, and Thunderbird. Our results show detection accuracies of 91.41%, 88.66%, 86.08%, 92.94%, 90.68%, 88.25%, and 91.62%, respectively. These outcomes represent a significant improvement of 32% to 41% compared to baseline models, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), convolutional LSTMs (CNN-LSTMs), Naive Bayes classifiers, and random forest classifiers. Our findings show that the proposed model is highly effective for duplicate bug report prediction and offers substantial advancements over existing methods. This approach has the potential to streamline bug management processes and improve overall software development efficiency.

Truck Lifting Accident Detection Method Based on Improved PointNet++ for Container Terminals

In container terminal operations, truck lifting accidents pose a serious threat to the safety and efficiency of automated equipment. Traditional detection methods using visual cameras and single-line Light Detection and Ranging (LiDAR) are insufficient for capturing three-dimensional spatial features, leading to reduced detection accuracy. Moreover, the boundary features of key accident objects, such as containers, truck chassis, and wheels, are often blurred, resulting in frequent false and missed detections. To tackle these challenges, this paper proposes an accident detection method based on multi-line LiDAR and an improved PointNet++ model. This method uses multi-line LiDAR to collect point cloud data from operational lanes in real time and enhances the PointNet++ model by integrating a multi-layer perceptron (MLP) and a mixed attention mechanism (MAM), optimizing the model’s ability to extract local and global features. This results in high-precision semantic segmentation and accident detection of critical structural point clouds, such as containers, truck chassis, and wheels. Experiments confirm that the proposed method achieves superior performance compared to the current mainstream algorithms regarding point cloud segmentation accuracy and stability. In engineering tests across various real-world conditions, the model exhibits strong generalization capability.

Research on the lightweight detection method of rail internal damage based on improved YOLOv8

To address the challenges of high computational costs, large storage demands, and low detection accuracy in internal rail damage identification, we propose a lightweight detection model, GhostMicroNet-YOLOv8n, as an enhancement of YOLOv8n. This model offers efficient and reliable technical support for detecting and classifying internal rail damage in rail flaw detection tasks. The proposed model introduces four key innovations. Firstly, the GhostHGNetV2 network is adopted as the feature extraction backbone, which reduces computational costs through structural optimization. Secondly, the Triplet Attention (TA) mechanism is incorporated to enhance the model’s ability to distinguish internal rail damage from complex backgrounds, thereby improving detection accuracy. Thirdly, the network structure is refined to better capture the scale characteristics of damage in B-scan data, improving small-target detection. Finally, the model is compressed using the Layer-Adaptive Sparsity for Magnitude-Based Pruning (LAMP) technique, significantly reducing model complexity while maintaining performance. Experimental results validate the effectiveness of the proposed model, achieving a 2.67% improvement in detection accuracy, a 91.67% reduction in parameters, a 64.20% decrease in computational cost, and an 84.87% reduction in model size compared to YOLOv8n. The model achieves an optimal balance between detection accuracy and computational efficiency, addressing the railroad industry’s demand for real-time defect detection and ensuring accurate flaw identification in resource-constrained Rail Fault Detection (RFD) vehicles.

Enhanced Lymph Node Detection in Colon Cancer Using Indocyanine Green Fluorescence: A Systematic Review of Studies from 2020 Onwards

Background: Colon cancer is known as one of the most prevalent malignancies in the world. This well-known pathology requires accurate lymph node dissection to achieve effective staging and improved treatment outcomes. Indocyanine green fluorescence imaging has been used as a new technique for enhancing lymph node visualization during surgical intervention. The high rates of local recurrence in colon cancer patients require innovative methods to improve lymphatic mapping and lymph node dissection. This review evaluates the clinical utility and efficacy of ICG imaging in enhancing lymph node accuracy in colon cancer surgery. Materials and methods: A systematic search was conducted in October 2024 (last day of consulting the database was 16 November) across Web of Science, Scopus, and PubMed to identify studies published from 2020 onwards focusing on the use of indocyanine green in colon cancer surgeries. The search terms used were “indocyanine green”, “ICG”, “fluorescent imaging”, “near-infrared imaging”, “colon cancer”, “colorectal cancer”, “colon carcinoma”,” colon neoplasms”, “surgery”, “surgical procedure”, “surgical resection”, surgical precision”. The search followed PRISMA guidelines. The records underwent a two-phase independent screening process conducted by the authors, first based on the title and abstract, followed by full record evaluation. Articles were excluded following certain exclusion criteria: non-human studies; restricted access publications; other publication type than article (review, meta-analysis, questionnaire-based study, case report, etc.), studies focusing on other diseases or studies that focused on the surgical treatment of metastasis from colon cancer; foreign language (non-English); no data of interest for the current review; studies that focused on rectal cancer and that grouped rectal and colon cancer. Data extraction involved both quantitative and qualitative data, such as detection rates, sensitivity, specificity, and other surgical outcomes. Risk of bias was assessed using ROBINS-I, J Joanna Briggs Institute (JBI) Critical Appraisal Checklist, and the Newcastle–Ottawa Scale, depending on study type. The study was not preregistered in PROSPERO. However, to ensure methodological rigor and transparency, it was retrospectively registered in Open Science Framework (OSF). Results: From the 3300 records initially identified, 9 studies were included in this review. Detection rates varied from 55% to 100%, with the highest rate reported in robot-assisted surgeries. The studies showed an improved lymph node detection and lymphatic flow accuracy using ICG fluorescence. Discussion: ICG fluorescence demonstrated substantial benefits, improving staging accuracy and potentially reducing recurrence rates by guiding the lymphadenectomy. The variability observed in detection rates is largely attributed to differences in ICG administration, cancer stage, and surgical approaches. Conclusions: ICG-guided surgery for colon cancer represents a promising advancement, enhancing lymph node detection and staging accuracy. Large-scale randomized trials are essential to establish standardized protocols and validate the efficacy in improving surgical outcomes.

Positional Packet Capture for Anomaly Detection in Multitenant Virtual Networks

ABSTRACTAnomaly detection in multitenant virtual networks presents significant challenges due to the dynamic, ephemeral nature of virtualized environments and the complex traffic patterns they generate. This paper presents a definition of preferable positions within virtual networks to enhance anomaly detection efficacy. Leveraging a combination of overlay and underlay capture positions, this paper examines the strategic impact of network positioning on anomaly detection accuracy, particularly in environments utilizing software‐defined networking (SDN) and network function virtualization (NFV). Through controlled testing with realistic attack scenarios, including data exfiltration, denial of service, and malware infiltration, the advantages and constraints of each capture position are demonstrated. The findings underscore the necessity of adaptable capture mechanisms to address variability in data volume, encapsulation challenges, and privacy concerns unique to virtualized ecosystems. The paper further introduces a cost calculation model that evaluates each capture position by weighting key factors, enabling an optimized trade‐off between detection accuracy and resource efficiency. The derived classification of the positional value significantly improves real‐time detection of both internal and external threats within multitenant networks.