Detection System Research Articles

A Threefold Approach for Enhancing Fuzzy Interpolative Reasoning: Case Study on Phishing Attack Detection Using Sparse Rule Bases

Fuzzy systems are powerful modeling systems for uncertainty applications. In contrast to traditional crisp systems, fuzzy systems offer the opportunity to extend the binary decision to continuous space, which could offer benefits for various application areas such as intrusion detection systems (IDSs), because of their ability to measure the degree of attacks instead of making a binary decision. Furthermore, fuzzy systems offer a suitable environment that is able to deal with uncertainty. However, fuzzy systems face a critical challenge represented by the sparse fuzzy rules. Typical fuzzy systems demand complete fuzzy rules in order to offer the required results. Additionally, generating complete fuzzy rules can be difficult due to many factors, such as a lack of knowledge base or limited data availability, such as in IDS applications. Fuzzy rule interpolation (FRI) was introduced to overcome this limitation by generating the required interpolation results in cases with sparse fuzzy rules. This work introduces a threefold approach designed to address the cases of missing fuzzy rules, which uses a few fuzzy rules to handle the limitations of missing fuzzy rules. This is achieved by finding the interpolation condition of neighboring fuzzy rules. This procedure was accomplished based on the concept of factors (which determine the degree to which each neighboring fuzzy rule contributes to the interpolated results, in cases of missing fuzzy rules). The evaluation procedure for the threefold approach was conducted using the following two steps: firstly, using the FRI benchmark numerical metrics, the results demonstrated the ability of the threefold approach to generate the required results for the various benchmark scenarios. Secondly, using a real-life dataset (phishing attacks dataset), the results demonstrated the effectiveness of the suggested approach to handle cases of missing fuzzy rules in the area of phishing attacks. Consequently, the suggested threefold approach offers an opportunity to reduce the number of fuzzy rules effectively and generate the required results using only a few fuzzy rules.

Chain extension-mediated colorimetric and sensitive detection of osteosarcoma-related miRNA based on CHA-cooperated self-priming

The accurate and dynamic monitoring of osteosarcoma, one of the most prevalent cancers, has a substantial impact on the quality of life of numerous individuals. This study proposes a sample, sensitive, and portable approach to miRNA detection that integrates a catalytic hairpin assembly (CHA) circuit with self-priming mediated chain extension. This method employs CHA as a protein enzyme-free isothermal amplification technique to amplify miRNA signals and induce self-priming through a specially designed hairpin probe. The pyrophosphate sensing probe (pp probe) was employed, which enables dual high-efficiency and stable colorimetric signaling, in contrast to traditional colorimetric modes. This novel system for the colorimetric detection of target miRNA is operated with high sensitivity (LOD of 3.5 fM) and selectivity, with the entire detection procedure requiring approximately 60 min. Additionally, this system could detect miRNA from constructed serum samples with high stability and recovery. This system is unique, extremely simple, and rapid, and it is designed to detect osteosarcoma-related miRNA through a highly sensitive color change. It is likely to be useful in applications that necessitate point-of-care detection of osteosarcoma.

The Integration of Advanced AI-Enabled Emotion Detection and Adaptive Learning Systems for Improved Emotional Regulation

The Integration of Advanced AI-Enabled Emotion Detection and Adaptive Learning Systems for Improved Emotional Regulation

Magnetoelastic Immunosensor for the Rapid Detection of SARS-CoV-2 in Bioaerosols.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a protein-coated, single-stranded RNA virus that parasitizes and infects primates, including humans. The current detection methods are mainly carried out using humans as a medium, such as a chest computed tomography (CT) examination, nucleic acid detection, antibody detection, and antigen detection. In addition, SARS-CoV-2 in bioaerosols is an important way of transmission and infection, which has attracted wide attention. In this paper, we simulate a sampling system of SARS-CoV-2 in bioaerosols and detect SARS-CoV-2 in bioaerosols by means of a magnetoelastic (ME) sensing device based on an Android intelligent terminal, which can directly detect SARS-CoV-2 in bioaerosols in a certain unit environment. This approach aims to achieve early detection and prevention. The experimental results show that the sampling system can successfully collect SARS-CoV-2 in bioaerosols. In the linear range of 5-20 ng/mL, the ME sensing detection system results closely match those of the calibration instrument (VNA), with goodness-of-fit values of 0.997 and 0.995, respectively. This work demonstrates that the ME sensing detection system proposed is stable, highly specific, real-time, rapid, and has certain reference values and feasibility.

Synergistic Approaches to Enhance IoT Intrusion Detection: Balancing Features through Combined Learning

The Internet of Things (IoT) plays a crucial role in ensuring security by preventing unauthorized access, malware infections, and malicious activities. IoT monitors network traffic as well as device behaviour to identify potential threats and take appropriate mitigation measures. However, there is a need for an IoT Intrusion Detection system with enhanced generalization capabilities, leveraging deep learning and advanced anomaly detection techniques. This study presents an innovative approach to IoT IDS that combines SMOTE-Tomek link and BTLBO, CNN with XGB classifier which aims to address data imbalances, improve model performance, reduce misclassifications, and improve overall dataset quality. The proposed IoT IDS system, using the IoT-23 dataset, achieves 99.90% accuracy and a low error rate, all while requiring significantly less execution time. This work represents a significant step forward in IoT security, offering a robust and efficient IDS solution tailored to the changing challenges of the interconnected world.

Enhanced Machine Learning Framework for Autonomous Depression Detection Using Modwave Cepstral Fusion and Stochastic Embedding

Depression is a prevalent mental illness that requires autonomous detection systems due to its complexity. Existing machine learning techniques face challenges such as background noise sensitivity, slow adaptation speed, and imbalanced data. To address these limitations, this study proposes a novel ModWave Cepstral Fusion and Stochastic Embedding Framework for depression prediction. Then, the Gain Modulated Wavelet Technique removes background noise and normalises audio signals. Difficulties with generalisation, which results in a lack of interpretability, hinder extracting relevant characteristics from speech. To address these issues, an Auto Cepstral Fusion extracts relevant features from speech, capturing temporal and spectral characteristics caused by background voice. Feature selection becomes imperative when choosing relevant features for classification. Selecting irrelevant features can result in overfitting, the curse of dimensionality, and less robustness to noise. Hence, the Principal Stochastic Embedding technique handles high-dimensional data, minimising noise influence and dimensionality. Furthermore, the XGBoost classifier differentiates between depressed and non-depressed individuals. As a result, the proposed method uses the DAIC-WOZ dataset from USC for detecting depressions, achieving an accuracy of 97.02%, precision of 97.02%, recall of 97.02%, F1-score of 97.02%, RMSE of 2.00, and MAE of 0.9, making it a promising tool for autonomous depression detection.

Two–Stage Detection and Localization of Inter–Frame Tampering in Surveillance Videos Using Texture and Optical Flow

Surveillance cameras provide security and protection through real-time monitoring or through the investigation of recorded videos. The authenticity of surveillance videos cannot be taken for granted, but tampering detection is challenging. Existing techniques face significant limitations, including restricted applicability, poor generalizability, and high computational complexity. This paper presents a robust detection system to meet the challenges of frame duplication (FD) and frame insertion (FI) detection in surveillance videos. The system leverages the alterations in texture patterns and optical flow between consecutive frames and works in two stages; first, suspicious tampered videos are detected using motion residual–based local binary patterns (MR–LBPs) and SVM; second, by eliminating false positives, the precise tampering location is determined using the consistency in the aggregation of optical flow and the variance in MR–LBPs. The system is extensively evaluated on a large COMSATS Structured Video Tampering Evaluation Dataset (CSVTED) comprising challenging videos with varying quality of tampering and complexity levels and cross–validated on benchmark public domain datasets. The system exhibits outstanding performance, achieving 99.5% accuracy in detecting and pinpointing tampered regions. It ensures the generalization and wide applicability of the system while maintaining computational efficiency.

DETER-RT: The new INPE-TropiSCO deforestation monitoring system in the Amazon biome

Abstract. The Amazon biome, undergoing significant deforestation, requires robust monitoring systems for effective management and conservation. This study introduces DETER-RT, a novel deforestation detection system that combines the Synthetic Aperture Radar (SAR)-based DETER-R and the TropiSCO systems to enhance detection capabilities using Sentinel-1 satellite data. DETER-RT utilizes a double-threshold technique to optimize the detection of deforestation by balancing detection accuracy and minimizing false positives across different forest types and conditions in the Amazon. The new system modulates detection thresholds based on the proximity of new disturbances to previously detected deforestation, incorporating a dynamic, regionalized threshold adjustment to cater to the variable characteristics of the Amazon’s diverse forest cover. Initial results indicate that DETER-RT provides more timely and accurate warnings compared to existing methods, especially during the fire season, where its performance is less impacted by smoke and haze that typically hinder optical sensors. This approach exemplifies the integration of advanced remote sensing technologies and analytical techniques in environmental monitoring.

MACHINE LEARNING, DEEP LEARNING AND ENSEMBLE LEARNING BASED APPROACHES FOR INTRUSION DETECTION ENHANCEMENT

With the extensive application of Internet of Things and wireless sensor networks (WSNs) in various real-time fields, they have become increasingly vulnerable to several types of attacks that could have a serious impact in their functionalities. Recently, intrusion detection systems have become one of the crucial security components. We propose in this work an approach based on machine and deep learning for DoS and DDoS attack detection. We have evaluated, analyzed, and compared the efficiency of three learning models separately, including deep neural network (DNN), random forest, and decision tree, using the standard metrics of evaluation such as accuracy, precision, F1-score, and recall. In our contribution, an approach ensemble learning was introduced in order to enhance the accuracy rate of classification. Our study was carried out using two well-known benchmark and real-time datasets, CICIoT-2023 and WSN-DS, intended for wireless sensor networks and IoT. These datasets containing various types of attacks. CICIoT2023 dataset contains 33 attacks divided into 7 classes, while WSN-DS dataset contains four types of DoS attacks, including Blackhole, Grayhole, Flooding, and TDMA. The experiment result demonstrate the effectiveness of our approach in attacks detection with high accuracy achieved close to perfect. Our approach result demonstrates that using the ensemble learning technique works better than each ML architecture independently.

An optimized model for network intrusion detection in the network operating system environment

With the heavy reliance on computers and information technology to send and receive data across networks of various types, there has been concern about securing that data from intrusions and cyber-attacks. The expansion of network usage has led to an increase in hacker attacks, which has led to prioritizing cybersecurity precautions in detecting potential threats. Intrusion detection techniques are a critical security measure to protect networks in both personal and corporate environments that are managed by network operating systems. For this, the paper relies on designing a network intrusion detection model. Since deep neural networks (DNNs) are classic deep learning models known for their strong classification performance, making them popular in intrusion detection along with other machine learning algorithms, they have been chosen to improve intrusion classification models based on datasets for intrusion detection systems. The basic structure of this proposal is to adopt one of the optimization algorithms in extracting features from the dataset to obtain more accurate results in the classification and intrusion detection stage. The developed Corona Virus algorithm is adopted to improve the system performance by identifying optimal features. This algorithm, which consists of several stages, optimally selects individuals based on features from the NSL-KDD dataset used for intrusion detection. The resulting optimization solution acts as a network structure for the intrusion classification model based on machine learning and deep learning algorithms. The test results showed exceptional performance on the NSL-KDD dataset, where the proposed Convolution Neural Network CNN model achieved 99.3% accuracy for multi-class classification, while the Decision Tree (DT) achieved 88.64% accuracy for anomaly detection in bi-class classification.

Automated detection of bone lesions using CT and MRI: a systematic review.

The aim of this study was to systematically review the use of automated detection systems for identifying bone lesions based on CT and MRI, focusing on advancements in artificial intelligence (AI) applications. A literature search was conducted on PubMed and MEDLINE. Data were extracted and grouped into three main categories, namely baseline study characteristics, model validation strategies, and the type of AI algorithms. A total of 10 studies were selected and analyzed, including 2,768 patients overall with a median of 187 per study. These studies utilized various AI algorithms, predominantly deep learning models (6 studies) such as Convolutional Neural Networks. Among machine learning validation strategies, K-fold cross-validation was the mostly used (5 studies). Clinical validation was performed using data from the same institution (internal testing) in 8 studies and from both the same and different (external testing) institutions in 1 study, respectively. AI, particularly deep learning, holds significant promise in enhancing diagnostic accuracy and efficiency. However, the review highlights several limitations, such as the lack of standardized validation methods and the limited use of external datasets for testing. Future research should address these gaps to ensure the reliability and applicability of AI-based detection systems in clinical settings.

TRANSFORMING BANKING SECURITY: THE ROLE OF DEEP LEARNING IN FRAUD DETECTION SYSTEMS

In the digital banking landscape, the increasing volume of online transactions has heightened the risk of fraudulent activities, necessitating the development of more effective detection systems. This study investigates the efficacy of various machine learning and deep learning algorithms in identifying fraudulent transactions, emphasizing Long Short-Term Memory (LSTM) networks. We implemented and evaluated multiple algorithms, including Logistic Regression, Random Forest, Gradient Boosting Machines (GBM), and XGBoost, on a large-scale credit card transaction dataset. Our results demonstrate that the LSTM model outperforms traditional machine learning algorithms, achieving an accuracy of 98.5%, precision of 87.2%, recall of 85.0%, and an Area Under the Curve (AUC) score of 0.94. These findings highlight the superior capability of LSTM networks to capture complex patterns in sequential transaction data, making them an asset for real-time fraud detection in banking. This research underscores the need for financial institutions to adopt advanced deep learning techniques to enhance their fraud detection systems, thereby minimizing financial losses and improving customer trust.

Hidden Danger Detection and Identification System of Power Transmission Tower Based on YOLOV11

Hidden Danger Detection and Identification System of Power Transmission Tower Based on YOLOV11

Wearable device data-driven athlete injury detection and rehabilitation monitoring algorithm

In recent years, sports wearable technology has completely changed the way athletes prepare, compete, and recover. Wearable technology has a lot to offer in the rehabilitation process, which is essential to an athlete’s return to their best performance. Wearable devices for athlete injury detection pose potential challenges like data quality, security, and privacy, impacting accuracy, reliability, and effectiveness. To solve these problems, an innovative injury detection and rehabilitation monitoring (IDRM) system was proposed for athletes. By employing an adjustable recurrent neural network (ARNN) to detect anomalies in injury risks such as abnormal joint movements in athletes. In this study, biomechanics data was collected from sports athletes through wearable devices, and the wearable system provided feedback to the user. A redefined convolutional neural network (RCNN) was utilized to monitor the rehabilitation process. This system tracks athlete’s rehabilitation progress and ensures that progress monitors were performed correctly, and the system, feasibility was evaluated on 10 healthy subjects performing 4 different rehabilitation exercises. Each exercise was performed four times monitoring and validation. The data was preprocessed using a Gaussian filter to remove noise from the obtained data. Then the features are extracted using independent component analysis (ICA) for dimensionality reduction from preprocessed data. The proposed method is implemented using Python software. In comparative analysis, the performance of ARNN showed high performance, with an F1-measure of 91.6%, accuracy of 93.5%, recall of 92.8%, and precision of 91.4%. With a 95% accuracy rate, 98% F1 measure, 94% precision, and 93% recall, the RCNN model functioned effectively. The result showed the proposed method achieved better performance in athlete injury detection and accurately recognizing all rehabilitation monitoring. This study provides a complete approach to athlete health management by highlighting the integration of rehabilitation monitoring and injury detection into an overall structure.

A Multi-Scale Approach to Early Fire Detection in Smart Homes

In recent years, advancements in smart home technologies have underscored the need for the development of early fire and smoke detection systems to enhance safety and security. Traditional fire detection methods relying on thermal or smoke sensors exhibit limitations in terms of response time and environmental adaptability. To address these issues, this paper introduces the multi-scale information transformer–DETR (MITI-DETR) model, which incorporates multi-scale feature extraction and transformer-based attention mechanisms, tailored specifically for fire detection in smart homes. MITI-DETR achieves a precision of 99.00%, a recall of 99.50%, and a mean average precision (mAP) of 99.00% on a custom dataset designed to reflect diverse lighting and spatial conditions in smart homes. Extensive experiments demonstrate that MITI-DETR outperforms state-of-the-art models in terms of these metrics, especially under challenging environmental conditions. This work provides a robust solution for early fire detection in smart homes, combining high accuracy with real-time deployment feasibility.

Enhancing Structured Query Language Injection Detection with Trustworthy Ensemble Learning and Boosting Models Using Local Explanation Techniques

This paper presents a comparative analysis of several decision models for detecting Structured Query Language (SQL) injection attacks, which remain one of the most prevalent and serious security threats to web applications. SQL injection enables attackers to exploit databases, gain unauthorized access, and manipulate data. Traditional detection methods often struggle due to the constantly evolving nature of these attacks, the increasing complexity of modern web applications, and the lack of transparency in the decision-making processes of machine learning models. To address these challenges, we evaluated the performance of various models, including decision tree, random forest, XGBoost, AdaBoost, Gradient Boosting Decision Tree (GBDT), and Histogram Gradient Boosting Decision Tree (HGBDT), using a comprehensive SQL injection dataset. The primary motivation behind our approach is to leverage the strengths of ensemble learning and boosting techniques to enhance detection accuracy and robustness against SQL injection attacks. By systematically comparing these models, we aim to identify the most effective algorithms for SQL injection detection systems. Our experiments show that decision tree, random forest, and AdaBoost achieved the highest performance, with an accuracy of 99.50% and an F1 score of 99.33%. Additionally, we applied SHapley Additive exPlanations (SHAPs) and Local Interpretable Model-agnostic Explanations (LIMEs) for local explainability, illustrating how each model classifies normal and attack cases. This transparency enhances the trustworthiness of our approach to detecting SQL injection attacks. These findings highlight the potential of ensemble methods to provide reliable and efficient solutions for detecting SQL injection attacks, thereby improving the security of web applications.

Congruent Glass Composite Scintillator for Efficient High-Energy Ray Detection.

The scintillator is the most crucial component in the high-energy ray detection system. The available scintillators suffer from the insurmountable drawbacks including poor shaping ability for single crystal and ceramic and low efficiency for glass. Here, a glass composite scintillator is proposed from a congruent crystallization system which possesses both excellent processability and high scintillating yield. It can be fabricated into diverse shapes and sizes including the bulk and tiny fiber. Benefitting from the unique compositing combination with a high crystallization ratio, the glass composite exhibits a giant scintillating light yield of ≈26,000 photons per MeV. The practical application for X-ray imaging is demonstrated and a high spatial resolution of 12 lpmm-1 is achieved. Furthermore, the fiber derived detector is built and the remote and micro-area detection is realized. These findings not only represent a novel design concept for the development of glass composite but also suggest a great step for expanding the scope of scintillators.

A novel road attribute detection system for autonomous vehicles using sensor fusion

A novel road attribute detection system for autonomous vehicles using sensor fusion

Fluorescent primers amplification refractory mutation system qPCR (FP ARMS-qPCR) for MTHFR C677T SNP genotyping.

The currently used methods for the detection of methylene tetrahydrofolic acid reductase (MTHFR) C677T single nucleotide polymorphism (SNP) are either time-consuming or expensive. In this study, we devised an accurate, rapid and easy-to-use SNP detection system based on fluorescent primers amplification refractory mutation system qPCR, known as FP ARMS-qPCR. Fluorescent primers (FPs) modified by fluorescent dye or quencher near the 3' terminal thymine were designed. The reaction conditions were optimized and the performance was evaluated. Using commercial kits as controls, 242 samples were tested in parallel to verify the feasibility of the FP ARMS-qPCR assay. We demonstrated the good sensitivity and specificity of the FP ARMS-qPCR with optimized conditions. The assay was able to accurately distinguish between different SNP sites of MTHFR C677T in less than 2h using as low as 50 pg of template genomic DNA. Completely consistent genotyping results reveal that FP ARMS-qPCR is concordant with commercial kits. We established a specific, sensitive, and rapid FP ARMS-qPCR method for the detection of MTHFR C677T genotype. This could also serve as a potential diagnostic tool for a variety of diseases.

A Lightweight and Efficient Multimodal Feature Fusion Network for Bearing Fault Diagnosis in Industrial Applications

To address the issues of single-structured feature input channels, insufficient feature learning capabilities in noisy environments, and large model parameter sizes in intelligent diagnostic models for mechanical equipment, a lightweight and efficient multimodal feature fusion convolutional neural network (LEMFN) method is proposed. Compared with existing models, LEMFN captures rich fault features at multiple scales by combining time-domain and frequency-domain signals, thereby enhancing the model’s robustness to noise and improving data adaptability under varying operating conditions. Additionally, the convolutional block attention module (CBAM) and random overlapping sampling technology (ROST) are introduced, and through a feature fusion strategy, the accurate diagnosis of mechanical equipment faults is achieved. Experimental results demonstrate that the proposed method not only possesses high diagnostic accuracy and rapid convergence but also exhibits strong robustness in noisy environments. Finally, a graphical user interface (GUI)-based mechanical equipment fault detection system was developed to promote the practical application of intelligent fault diagnosis in mechanical equipment.