Traditional Detection Methods Research Articles

A Semantic Spatial Structure-Based Loop Detection Algorithm for Visual Environmental Sensing

Loop closure detection is an important component of the Simultaneous Localization and Mapping (SLAM) algorithm, which is utilized in environmental sensing. It helps to reduce drift errors during long-term operation, improving the accuracy and robustness of localization. Such improvements are sorely needed, as conventional visual-based loop detection algorithms are greatly affected by significant changes in viewpoint and lighting conditions. In this paper, we present a semantic spatial structure-based loop detection algorithm. In place of feature points, robust semantic features are used to cope with the variation in the viewpoint. In consideration of the semantic features, which are region-based, we provide a corresponding matching algorithm. Constraints on semantic information and spatial structure are used to determine the existence of loop-back. A multi-stage pipeline framework is proposed to systematically leverage semantic information at different levels, enabling efficient filtering of potential loop closure candidates. To validate the effectiveness of our algorithm, we conducted experiments using the uHumans2 dataset. Our results demonstrate that, even when there are significant changes in viewpoint, the algorithm exhibits superior robustness compared to that of traditional loop detection methods.

FireXplainNet: Optimizing Convolution Block Architecture for Enhanced Wildfire Detection and Interpretability

The early detection of wildfires is a crucial challenge in environmental monitoring, pivotal for effective disaster management and ecological conservation. Traditional detection methods often fail to detect fires accurately and in a timely manner, resulting in significant adverse consequences. This paper presents FireXplainNet, a Convolutional Neural Network (CNN) base model, designed specifically to address these limitations through enhanced efficiency and precision in wildfire detection. We optimized data input via specialized preprocessing techniques, significantly improving detection accuracy on both the Wildfire Image and FLAME datasets. A distinctive feature of our approach is the integration of Local Interpretable Model-agnostic Explanations (LIME), which facilitates a deeper understanding of and trust in the model’s predictive capabilities. Additionally, we have delved into optimizing pretrained models through transfer learning, enriching our analysis and offering insights into the comparative effectiveness of FireXplainNet. The model achieved an accuracy of 87.32% on the FLAME dataset and 98.70% on the Wildfire Image dataset, with inference times of 0.221 and 0.168 milliseconds, respectively. These performance metrics are critical for the application of real-time fire detection systems, underscoring the potential of FireXplainNet in environmental monitoring and disaster management strategies.

Global structure graph mapping for multimodal change detection

ABSTRACT Multimodal change detection (MCD) combines multiple remote sensing data sources to realize surface change monitoring, which is essential for disaster evaluation and environmental monitoring. However, due to the ‘incomparable’ features in multimodal data, traditional change detection methods for unimodal (homogenous) data no longer apply. To address this issue, this paper proposes a novel MCD method with global structure graph mapping (GSGM) which extracts the ‘comparable’ structural features between multimodal datasets and constructs a global structure graph (GSG) to express the structure information for each of the multi-temporal images, which are then cross-mapped to the other data domain. The change intensity (CI) is determined by measuring the change of GSGs after mapping and the differences between GSGs and mapped GSGs. The forward and backward CI maps (CIMs) are then fused with the latent low-rank representation method (LLRR), and the change map (CM) is obtained by threshold segmentation. Experiments on five multimodal and four unimodal datasets demonstrate the effectiveness and robustness of the proposed method (source code is made available at https://github.com/rshante0426/GSGM).

Microfluidic Immunosensor Platform for Sensitive Detection of Human Epidermal Growth Factor Receptor-2 Based on Enhanced Cathode Electrochemiluminescence of Bimetallic Nanoclusters.

In this work, a microfluidic immunosensor chip was developed by incorporating microfluidic technology with electrochemiluminescence (ECL) for sensitive detection of human epidermal growth factor receptor-2 (HER2). The immunosensor chip can achieve robust reproducibility in mass production by integrating multiple detection units in a series. Notably, nanoscale materials can be better adapted to microfluidic systems, greatly enhancing the accuracy of the immunosensor chip. Ag@Au NCs closed by glutathione (GSH) were introduced in the ECL microfluidic immunosensor system with excellent and stable ECL performance. The synthesized CeO2-Au was applied as a coreaction promoter in the ECL signal amplification system, which made the result of HER2 detection more reliable. In addition, the designed microfluidic immunosensor chip integrated the biosensing system into a microchip, realizing rapid and accurate detection of HER2 by its high throughput and low usage. The developed short peptide ligand NARKFKG (NRK) achieved an effective connection between the antibody and nanocarrier for improving the detection efficiency of the sensor. The immunosensor chip had better storage stability and sensitivity than traditional detection methods, with a wide detection range from 10 fg·mL-1 to 100 ng·mL-1 and a low detection limit (LOD) of 3.29 fg·mL-1. In general, a microfluidic immunosensor platform was successfully constructed, providing a new idea for breast cancer (BC) clinical detection.

CoMadOut—a robust outlier detection algorithm based on CoMAD

AbstractUnsupervised learning methods are well established in the area of anomaly detection and achieve state of the art performances on outlier datasets. Outliers play a significant role, since they bear the potential to distort the predictions of a machine learning algorithm on a given dataset. Especially among PCA-based methods, outliers have an additional destructive potential regarding the result: they may not only distort the orientation and translation of the principal components, they also make it more complicated to detect outliers. To address this problem, we propose the robust outlier detection algorithm CoMadOut, which satisfies two required properties: (1) being robust towards outliers and (2) detecting them. Our CoMadOut outlier detection variants using comedian PCA define, dependent on its variant, an inlier region with a robust noise margin by measures of in-distribution (variant CMO) and optimized scores by measures of out-of-distribution (variants CMO*), e.g. kurtosis-weighting by CMO+k. These measures allow distribution based outlier scoring for each principal component, and thus, an appropriate alignment of the degree of outlierness between normal and abnormal instances. Experiments comparing CoMadOut with traditional, deep and other comparable robust outlier detection methods showed that the performance of the introduced CoMadOut approach is competitive to well established methods related to average precision (AP), area under the precision recall curve (AUPRC) and area under the receiver operating characteristic (AUROC) curve. In summary our approach can be seen as a robust alternative for outlier detection tasks.

Early Detection System for Gas Leakage and Fire in Smart Home Using IOT

Gas leakage is an important environmental, commercial and residential problem that threatens human health, safety and the environment. Traditional gas detection methods often lack the ability to track time and do not provide timely information, leading to accidents and damage. In this context, Internet of Things (IoT) technology has been found to provide a reliable solution for continuous monitoring and remote management of gas detection systems. This paper presents an IoT-based gas leak detection system to improve safety and reduce the risk of gas leakage. leak. The proposed system integrates gas sensors, microcontrollers, communication modules and cloud computing infrastructure to enable real-time monitoring, data analysis and remote monitoring. Gas detectors used in designated areas indicated the presence of hazardous gases, and data was collected directly from the 's central processing unit for analysis. The system provides rapid rescue and intervention by sending instant notifications via mobile applications in case of a gas leak. The main features of the proposed system are scalability, flexibility and interoperability, allowing integration with existing infrastructure and integration of different protocols. Additionally, the use of a cloud-based platform facilitates data storage, analysis and visualization, allowing partners to gain insight intogasleaks, processes and possible consequences

URL Detection Based on Quantum Long Short-Term Memory Neural Network

In today's era, with the continuous development of the internet, surfing the internet has become an inseparable part of our lives. For those who frequently roam the internet, URLs are information that we are constantly exposed to. And YRL is also divided into malicious and normal. Clicking on malicious URLs can cause certain losses to our internet tools, and in severe cases, it can even lead to money theft. Malicious URL detection is an important task in the field of network security, aimed at identifying and preventing potential network attacks and malicious activities. Traditional malicious URL detection methods, such as Blacklist based detection and Machine learning based on feature extraction, have achieved certain results, but face challenges in real-time, accuracy, and data requirements. Therefore, this article proposes the use of Quantum Long Short-Term Memory Neural Network (QLSTM) to solve this problem. By observing the accuracy, recall, and F1 values obtained from training a large amount of data on the QLSTM network, it is shown that this method is feasible.

Algorithmically Generated Domain Names Detection Using Gated Recurrent Unit Deep Learning

The modern malware increasingly employs domain generation algorithms (DGAs) to evade traditional DNS query detection methods, such as blacklisting or reverse engineering of suspicious domain names. These algorithms generate vast numbers of random domain names to establish communication with Command and Control (C&C) servers, posing significant challenges for detection. Previous research has predominantly relied on classical machine learning algorithms, necessitating manual feature extraction and classification, which is both time-consuming and labour-intensive this paper, we propose a deep learning-based architecture for detecting DGA-generated domain names. Our model utilizes recurrent networks with gated recurrent units (GRUs) for domain name detection. By converting domain names into vectors and employing GRUs, the model autonomously learns features, eliminating the need for manual intervention in feature extraction. Compared to traditional methods, our approach reduces time costs associated with feature extraction. The experimental result demonstrates the effectiveness of our proposed GRU achieving 98% accuracy, 94% recall rate, 93% precision, and an Area Under the Curve (AUC) of 99.6%. The GRUarchitecture outperforms LSTM models in terms of recall rate and accuracy while requiring less computational resources, indicating significant performance enhancement.

Dynamic detection of saturated distribution of activated carbon adsorbed volatile organic compounds based on electrical impedance tomography

The electrical impedance tomography (EIT) has been achieved for the dynamic detection of the adsorption saturation distribution in activated carbon. Volatile organic compounds (VOCs) emitted by industries pose a serious threat to human health and environmental quality. Therefore, adsorption method is widely used to treat such organic compounds. Activated carbon, as a commonly used adsorbent material, plays a crucial role in the efficient utilization and management of the adsorption process. Traditional adsorption detection methods suffer from information loss and discontinuity. We can obtain the conductivity distribution information during the current passing through the material by measuring the potential changes on the boundary of interest field, and analyze the saturation distribution information of VOCs adsorbed on activated carbon based on the conductivity distribution image. The feasibility of the technology in monitoring the saturation distribution of the adsorption process in activated carbon was verified by principle and simulation. For experimental verification, fixed bed and fluidized bed experiments were carried out, taking into account the special case of impedance change factors of activated carbon particles in the static and flow states. The experimental results confirm that in fixed-bed adsorption, the adsorption impedance change response can be effectively obtained at an excitation frequency of 1.0 MHz, while in a fluidized bed, a good adsorption impedance change response can be achieved at an excitation frequency of 4 kHz. By selecting the appropriate excitation frequency according to the change in the adsorbent’s state, stable imaging of the saturation distribution can be achieved. This study introduces a new method for visualizing the monitoring process of activated carbon adsorption.

Bolt loosening monitoring with passive wireless-based smart washers

Ensuring sufficient preload of bolted connections is crucial for maintaining the safety of railway operations. Accidents resulting from loose bolts caused by inadequate preload are a recurring concern. Traditional bolt preload detection methods suffer from reliability issues and bulky equipment requirements. To address these challenges, this study proposes a novel solution utilising passive wireless measurement systems. However, the communication effectiveness of these systems can be significantly impacted by the surrounding metal environment. This research introduces an intelligent clover washer design to mitigate the adverse effects of eddy currents induced by the metal environment. The design incorporates a new clover antenna and washer structure, effectively reducing the influence of the metal environment and improving the communication quality. The proposed design has undergone comprehensive prototyping, simulation and experimental verification. The results demonstrate a significant improvement over the traditional circular antenna and washer combination under similar conditions. Specifically, the sensing distance of the new clover washer is enhanced by 60% and the stable communication distance is improved by 75%. The experimental results highlight the ability of the new clover smart washer to generate a stronger spatial magnetic field and exhibit reduced susceptibility to the metal washer, thereby enhancing communication effectiveness.

Shallow mud detection algorithm for submarine channels based on improved YOLOv5s

Submarine mud poses a risk to channel navigation safety. Traditional detection methods lack efficiency efficiency and accuracy. As a result, this paper proposed an enhanced shallow submarine mud detection algorithm, leveraging an improved YOLOv5s model to increase accuracy and effectiveness in identifying such hazards in marine channels. Firstly, the sub-bottom profiler was employed to assess the submarine channel of Lianyungang Port to acquire the image data of the shallow mud sound print. Concurrently, the analysis incorporated the characteristics of changes in sound intensity peaks to precisely identify the shallow mud's location. Furthermore, the incorporation of C2F feature module into the backbone module enhances the gradient flow of the algorithm, augments the feature extraction information, and improves the algorithm's detection performance. Subsequently, Efficient Multi-Scale Attention (EMA) mechanism is incorporated into the neck module, aiming to optimize the algorithm's channel dimensions, minimize computational overhead, and enhance its detection efficiency. Finally, the study introduced Normalized Wasserstein Distance (NWD) loss function into bounding box regression loss function. This integration effectively addresses the issue of multi-scale defects, emphasizes the transformation of target planar position deviation, and improves the accuracy of the algorithm's detection capabilities. The results indicate that the improved YOLOv5s-EF algorithm outperforms the original YOLOv5s algorithm and other widely used detection algorithms. It achieved a validation set precision rate of 97.8%, recall rate of 97.6%, F1 value of 97.7%, mean Average Precision (mAP)@0.5 of 98.2%, mAP@0.95 of 69.6%, and Frames per Second (FPS) of 51.8. YOLOv5s-EF algorithm proposed in this study offers a novel technical approach for detecting mud in submarine channels, which is importance for ensuring the safe operation and maintenance of dredging in such channels.

Enhancing Object Detection Accuracy Through Custom Dataset Using Yolo

Abstract: Potholes pose significant risks to road safety and vehicle maintenance, leading to accidents and costly repairs. Traditional methods of pothole detection are often labour-intensive and time-consuming. In this study, we propose an innovative approach to pothole detection using YOLOv8, a state-of-the-art object detection algorithm. By harnessing the power of deep learning, our system can accurately identify and locate potholes in real-time video streams from traffic cameras and vehicles. We employ YOLOv8, an advanced variant of the You Only Look Once (YOLO) algorithm, known for its speed and accuracy in real-time object detection tasks. Leveraging a large annotated dataset of road images, we fine-tune the YOLOv8 model to specifically detect potholes. Our trained model is capable of identifying various pothole sizes and shapes, even in challenging lighting and weather conditions.

Driver fatigue detection method based on facial features using deep learning

Fatigue driving can result in delayed driver reactions and lack of concentration, increasing the risk of traffic accidents. Therefore, finding effective fatigue detection methods is crucial for accident prevention and ensuring public safety. Traditional driver fatigue detection methods based on facial features are limited by their reliance on manually designed features and classifiers, leading to low accuracy, slow detection speed, and inefficient performance. Hence, methods based on deep learning have gradually become a trend in fatigue-driving detection research. This paper focuses on driver fatigue detection methods based on facial features using deep learning. In this study, we analyze numerous research outcomes on how to accurately extract, analyze, and integrate various facial features, such as eye features, mouth features, facial expressions, and head postures, to precisely determine the driver's fatigue state. Then, we compare the latest deep learning-based technologies. This paper provides an information-rich repository to help researchers select more promising designs, that can drive the widespread development of fatigue-driving detection technologies.

Enhancing Concrete Crack Image Detection Using MobileNetV2 and Transfer Learning

The safety of civil engineering structures such as bridges and buildings is paramount, necessitating the early detection and repair of cracks to prevent structural failures. Traditional crack detection methods, including manual inspections and basic image processing, are often hampered by slow detection speeds and limited accuracy. This study explores the integration of advanced deep learning techniques, specifically MobileNetV2, enhanced through transfer learning, to improve the efficiency and accuracy of crack detection. The "Concrete Crack Images for Classification" dataset was employed, featuring 40,000 images from real-world scenarios. Our approach leverages the lightweight architecture of MobileNetV2 and the efficiency of transfer learning to create a robust model that not only reduces computational demands but also achieves high accuracy, with a validation accuracy rate of 99.38%. This paper details the model training, the innovative use of MobileNetV2, and comparative analyses with other deep learning models, demonstrating significant improvements over traditional methods. The findings underscore the potential of MobileNetV2 and transfer learning in practical applications, offering a promising avenue for future research in automated crack detection across various engineering fields.

Mobile Malware Detection using Machine Learning Classifiers

In today world the mobile malware shows the significant threat to the security and privacy of the society using smartphones. These malware aims to access the sensitive data and harm the devices of users. This paper conducts a comprehensive comparison between the various machine learning and traditional methods for mobile malware detection based on the research papers published by the authors. Signature-based detection depends upon the predefined and common patterns, while the anomaly based techniques analyse the deviation from the regular normal behaviour. This study discusses the strengths and limitations of different approaches and highlights the need for adopting the malware detection methods to fight the growing threats. It also examines the role of machine learning algorithms, like Decision Trees, Random Forests, Convolutional Neural Networks, Support Vector Machines, and Naïve Bayes, for better malware detection. Latest findings and research highlights the importance of the continuing innovation to fight the emerging threat to the user privacy, data and security due to malwares. Keywords: Mobile Malware, Artificial Intelligence, Virus, Signature-based Detection, Machine Learning

Early automated detection system for skin cancer diagnosis using artificial intelligent techniques

Recently, skin cancer is one of the spread and dangerous cancers around the world. Early detection of skin cancer can reduce mortality. Traditional methods for skin cancer detection are painful, time-consuming, expensive, and may cause the disease to spread out. Dermoscopy is used for noninvasive diagnosis of skin cancer. Artificial Intelligence (AI) plays a vital role in diseases’ diagnosis especially in biomedical engineering field. The automated detection systems based on AI reduce the complications in the traditional methods and can improve skin cancer’s diagnosis rate. In this paper, automated early detection system for skin cancer dermoscopic images using artificial intelligent is presented. Adaptive snake (AS) and region growing (RG) algorithms are used for automated segmentation and compared with each other. The results show that AS is accurate and efficient (accuracy = 96%) more than RG algorithm (accuracy = 90%). Artificial Neural networks (ANN) and support vector machine (SVM) algorithms are used for automated classification compared with each other. The proposed system with ANN algorithm shows high accuracy (94%), precision (96%), specificity (95.83%), sensitivity (recall) (92.30%), and F1-score (0.94). The proposed system is easy to use, time consuming, enables patients to make early detection for skin cancer and has high efficiency.

Dense Feature Matching for Hazard Detection and Avoidance Using Machine Learning in Complex Unstructured Scenarios

Exploring the Moon and Mars are crucial steps in advancing space exploration. Numerous missions aim to land and research in various lunar locations, some of which possess challenging surfaces with unchanging features. Some of these areas are cataloged as lunar light plains. Their main characteristics are that they are almost featureless and reflect more light than other lunar surfaces. This poses a challenge during navigation and landing. This paper compares traditional feature matching techniques, specifically scale-invariant feature transform and the oriented FAST and rotated BRIEF, and novel machine learning approaches for dense feature matching in challenging, unstructured scenarios, focusing on lunar light plains. Traditional feature detection methods often need help in environments characterized by uniform terrain and unique lighting conditions, where unique, distinguishable features are rare. Our study addresses these challenges and underscores the robustness of machine learning. The methodology involves an experimental analysis using images that mimic lunar-like landscapes, representing these light plains, to generate and compare feature maps derived from traditional and learning-based methods. These maps are evaluated based on their density and accuracy, which are critical for effective structure-from-motion reconstruction commonly utilized in navigation for landing. The results demonstrate that machine learning techniques enhance feature detection and matching, providing more intricate representations of environments with sparse features. This improvement indicates a significant potential for machine learning to boost hazard detection and avoidance in space exploration and other complex applications.

An Investigative Study for the Role of Bio inspired Algorithms for the Detection of Cyber Bullies

This study delves into cyberbullying detection through the lens of bio inspired algorithms. Cyberbullying poses a significant threat to an individual's mental well being and societal harmony in the digital age. Due to online interactions' dynamic and nuanced nature, traditional detection methods often need to catch up. Drawing inspiration from natural processes, bio inspired algorithms offer a promising avenue to tackle this complex problem. This investigative study explores the efficacy of bio inspired algorithms, such as genetic algorithms, swarm intelligence, and artificial immune systems, in identifying cyberbullying instances across various online platforms. Through empirical analysis and experimentation, the study seeks to evaluate the performance of these algorithms in comparison to conventional approaches. Additionally, it endeavours to uncover the underlying mechanisms driving their effectiveness and potential areas for refinement. The results of this study can help design and develop accurate cyber bullying detection systems, thereby contributing to the creation of safer online environments for individuals of all ages.

Credit Card Fraud Detection System

Globally, credit card fraud is a serious threat to people, businesses, and financial institutions. With the rise of online transactions, fraudsters have developed clever ways to take advantage of loopholes in payment systems. Traditional fraud detection methods based on manual inspections and rules-based systems are unable to counteract this new and evolving risk. As a result, the use of data analytics and machine learning has become a viable option for real-time detection and prevention of credit card fraud. The paper looks at using machine learning algorithms such as logistic regression, decision trees, random forests, neural networks, etc. to detect fraudulent transactions We go over the importance of data sources and components, analytical metrics, and how fraud detection on the effectiveness of examples. In addition, we list the current challenges and directions in which credit card fraud detection is likely to continue, including the use of blockchain technology and sophisticated AI techniques. Overall, this study highlights the importance of credit card theft detection and the promise of machine learning in mitigating this ubiquitous problem financial institutions use advanced machine learning algorithms and analytics function to detect fraudulent behaviour, protect customer interests, and maintain payment environment integrity to improve their capabilities.

Machine vision model for detection of foreign substances at the bottom of empty large volume parenteral.

Empty large volume parenteral (LVP) bottle has irregular shape and narrow opening, and its detection accuracy of the foreign substances at the bottom is higher than that of ordinary packaging bottles. The current traditional detection method for the bottom of LVP bottles is to directly use manual visual inspection, which involves high labor intensity and is prone to visual fatigue and quality fluctuations, resulting in limited applicability for the detection of the bottom of LVP bottles. A geometric constraint-based detection model (GCBDM) has been proposed, which combines the imaging model and the shape characteristics of the bottle to construct a constraint model of the imaging parameters, according to the detection accuracy and the field of view. Then, the imaging model is designed and optimized for the detection. Further, the generalized GCBDM has been adopted to different bottle bottom detection scenarios, such as cough syrup and capsule medicine bottles by changing the target parameters of the model. The GCBDM, on the one hand, can avoid the information at the bottom being blocked by the narrow opening in the imaging optical path. On the other hand, by calculating the maximum position deviation between the center of visual inspection and the center of the bottom, it can provide the basis for the accuracy design of the transmission mechanism in the inspection, thus further ensuring the stability of the detection.