Detection System Research Articles

Cloud-based machine learning algorithms for anomalies detection

Gradient boosting machines harnesses the inherent capabilities of decision trees and meticulously corrects their errors in a sequential fashion, culminating in remarkably precise predictions. Word2Vec, a prominent word embedding technique, occupies a pivotal role in natural language processing (NLP) tasks. Its proficiency lies in capturing intricate semantic relationships among words, thereby facilitating applications such as sentiment analysis, document classification, and machine translation to discern subtle nuances present in textual data. Bayesian networks introduce probabilistic modeling capabilities, predominantly in contexts marked by uncertainty. Their versatile applications encompass risk assessment, fault diagnosis, and recommendation systems. Gated recurrent units (GRU), a variant of recurrent neural networks, emerges as a formidable asset in modeling sequential data. Both training and testing are crucial to the success of an intrusion detection system (IDS). During the training phase, several models are created, each of which can recognize typical from anomalous patterns within a given dataset. To acquire passwords and credit card details, "phishing" usually entails impersonating a trusted company. Predictions of student performance on academic tasks are improved by hyper parameter optimization of the gradient boosting regression tree using the grid search approach.

Deep learning-based automated tile defect detection system for Portuguese cultural heritage buildings

A prominent feature in Portuguese historic architecture is Portugal’s azulejos or tiles that cover cultural heritage buildings with colorful patterns. However, tiles are prone to deterioration due to the quality of masonry materials, exposure over time, and natural and human factors. A careful approach is necessary to detect and assess tile damage in time to conserve cultural heritage. Deep learning (DL) methods are applied to detect deterioration and damage by automating vision-based monitoring. This study uses the You Only Look Once (YOLO), method to detect deterioration in tiles automatically. To obtain the initial dataset, over 5000 images of damage were collected, including cracks, craters, glaze detachment, and tile lacunae, as well as images with no defects. Additionally, a MobileNet model was used for binary classification of damaged and intact tiles to compare classification and detection approaches. Through the fine-tuning of hyperparameters and updating the dataset, an overall accuracy of over 72% for YOLO (multiple classification) and 97% accuracy for binary classification was achieved, demonstrating the adequacy of the tool for real-world applications.

A review of intrusion detection system and security threat in internet of things enabled environment

Thousands of devices communicate globally to share data and information without any human intervention. A network of physical objects with numerous sensors and other network hardware to exchange data with servers and additional devices that are linked is referred to as the "internet of things (IoT)”. The actions hurting the communication system are known as intrusions. Security features such as (integrity, and confidentiality) within IoT networks are compromised when any kind of intrusion occurs. To identify multiple infiltration types in an environment where IoT is enabled, an intrusion detection system (IDS) is required. In environments where IoT is enabled, security vulnerabilities are now more prevalent than ever. In this study, the IoT architecture is reviewed, and potential security risks at each tier are investigated. It is also hoped that this research will stimulate thought about the expanding risks posed by unprotected IoT devices. The paper also intends to provide an in-depth analysis of intrusion detection systems for identifying and classifying security threats in an IoT-enabled environment. Furthermore, this study investigates a variety of efficient machine learning-based methods for detecting cyberattacks on IoT devices.

Lane Line Detection System for Vehicle Using Python

Abstract: This research presents a real-time collision detection and lane assistance system implemented using Python and OpenCV. The system leverages computer vision techniques to detect vehicles and lane lines, calculate distances to detected objects, and issue timely alerts to prevent collisions. The implementation aims to enhance driver safety by providing real-time feedback about the vehicle's surroundings. This paper details the system's design, methodology, and performance evaluation.

Optimizing Feature Selection Method in Intrusion Detection System Using Thresholding

Optimizing Feature Selection Method in Intrusion Detection System Using Thresholding

The effectiveness of endoscopic diagnostics of colon tumors using artificial intelligence: prospective tandem study

AIM: to estimate the effectiveness of a medical decision support system based on artificial intelligence in the endoscopic diagnosis of benign tumors during tandem study.PATIENTS AND METHODS: from October to December 2023, a single-center comparative tandem study of medical decision support system based on artificial intelligence “ArtInCol” was done. The first stage was a traditional colonoscopy under sedation, the second one — colonoscopy using AI. A pairwise comparison of the main indicators of the effectiveness was made.RESULTS: in the primary endpoint, the polyp detection rate (PDR) in the traditional colonoscopy group was 40.6% vs 56.4% in the AI group, p = 0.0001 (RR = 1.39; 95% CI: 1.04–1.87). The mean number of lesions detected (MPP) was 1.63 (± 1.2) vs 2.47 (± 1.8) in the AI group (mean difference = 0.84; (95% CI: 1.07–0.61).CONCLUSION: the study demonstrated the effectiveness of the original medical decision support system for benign colon tumors detection in real clinical practice. The further stage, a multicenter randomized trial is needed.

Functionalized DNA Origami-Enabled Detection of Biomarkers.

Biomarkers are crucial physiological and pathological indicators in the host. Over the years, numerous detection methods have been developed for biomarkers, given their significant potential in various biological and biomedical applications. Among these, the detection system based on functionalized DNA origami has emerged as a promising approach due to its precise control over sensing modules, enabling sensitive, specific, and programmable biomarker detection. We summarize the advancements in biomarker detection using functionalized DNA origami, focusing on strategies for DNA origami functionalization, mechanisms of biomarker recognition, and applications in disease diagnosis and monitoring. These applications are organized into sections based on the type of biomarkers - nucleic acids, proteins, small molecules, and ions - and concludes with a discussion on the advantages and challenges associated with using functionalized DNA origami systems for biomarker detection.

Analysis of neural network detectors for network attacks

While network attacks play a critical role in many advanced persistent threat (APT) campaigns, an arms race exists between the network defenders and the adversary: to make APT campaigns stealthy, the adversary is strongly motivated to evade the detection system. However, new studies have shown that neural network is likely a game-changer in the arms race: neural network could be applied to achieve accurate, signature-free, and low-false-alarm-rate detection. In this work, we investigate whether the adversary could fight back during the next phase of the arms race. In particular, noticing that none of the existing adversarial example generation methods could generate malicious packets (and sessions) that can simultaneously compromise the target machine and evade the neural network detection model, we propose a novel attack method to achieve this goal. We have designed and implemented the new attack. We have also used Address Resolution Protocol (ARP) Poisoning and Domain Name System (DNS) Cache Poisoning as the case study to demonstrate the effectiveness of the proposed attack.

Design and development of the DE-SPECT system: a clinical SPECT system for broadband multi-isotope imaging of peripheral vascular disease

Objective. Peripheral Vascular Disease (PVD) affects more than 230 million people worldwide and is one of the leading causes of disability among people over age 60. Nowadays, PVD remains largely underdiagnosed and undertreated, and requires the development of tailored diagnostic approaches. We present the full design of the Dynamic Extremity SPECT (DE-SPECT) system, the first organ-dedicated SPECT system for lower extremity imaging, based on 1 cm thick Cadmium Zinc Telluride (CZT) spectrometers and a dynamic dual field-of-view (FOV) synthetic compound-eye (SCE) collimator. Approach. The proposed DE-SPECT detection system consists of 48 1 cm thick 3D-position-sensitive CZT spectrometers arranged in a partial ring of 59 cm in diameter in a checkerboard pattern. The detection system is coupled with a compact dynamic SCE collimator that allows the user to select between two different FOVs at any time during an imaging study: a wide-FOV (28 cm diameter) configuration for dual-leg or scout imaging or a high-resolution and high-sensitivity (HR-HS) FOV (16 cm diameter) for single-leg or focused imaging. Main results. The preliminary experimental data show that the CZT spectrometer achieves a 3D intrinsic spatial resolution of <0.75 mm FWHM and an excellent energy resolution over a broad energy range (2.6 keV FWHM at 218, 3.3 keV at 440 keV). From simulations, the wide-FOV configuration offers a 0.034% averaged sensitivity at 140 keV and <8 mm spatial resolution, whereas the HR-HS configuration presents a peak central sensitivity of 0.07% at 140 keV and a ∼5 mm spatial resolution. The dynamic SCE collimator enables the capability to perform joint reconstructions that would ensure an overall improvement in imaging performance. Significance. The DE-SPECT system is a stationary and high-performance SPECT system that offers an excellent spectroscopic performance with a unique computer-controlled dual-FOV imaging capability, and a relatively high sensitivity for multi-tracer and multi-functional SPECT imaging of the extremities.

BIPOOLNET: An advanced UNet architecture for enhanced lane detection in autonomous vehicles

In the rapidly evolving landscape of autonomous vehicle technology, the imperative to bolster safety within smart urban ecosystems has never been more critical. This endeavor requires the deployment of advanced detection systems capable of navigating the intricacies of pedestrian, near-vehicle, and lane detection challenges, with a particular focus on the nuanced requirements of curved lane navigation – a domain where traditional AI models exhibit notable deficiencies. This paper introduces BIPOOLNET, an innovative encoder-decoder neural architecture, ingeniously augmented with a feature pyramid to facilitate the precise delineation of curved lane geometries. BIPOOLNET integrates max pooling and average pooling to extract critical features and mitigate the complexity of the feature map, redefining the benchmarks for lane detection technology. Rigorous evaluation using the TuSimple dataset underscores BIPOOLNET’s exemplary performance, evidenced by an unprecedented accuracy rate of 98.45%, an F1-score of 98.17%, and notably minimal false positive (1.84%) and false negative (1.09%) rates. These findings not only affirm BIPOOLNET’s supremacy over extant models but also signal a paradigm shift in enhancing the safety and navigational precision of autonomous vehicles, offering a scalable, robust solution to the multifaceted challenges posed by real-world driving dynamics.

Establishment of dual reverse transcriptase-polymerase chain reaction for detection system for Areca palm velarivirus 1.

Areca palm velarivirus 1 (APV1) is one of the main pathogen causing yellow leaf disease, and leading to considerable losses in the Areca palm industry. The detection methods for APV1 are primarily based on phenotype determination and molecular techniques, such as polymerase chain reaction (PCR). However, a single PCR has limitations in accuracy and sensitivity. Therefore, in the present study, we established a dual RT-PCR APV1-detection system with enhanced accuracy and sensitivity using two pairs of specific primers, YLDV2-F/YLDV2-R and YLDV4-F/YLDV4-R. Moreover, two cDNA fragments covering different regions of the viral genome were simultaneously amplified, with PCR amplicon of 311 and 499 bp, respectively. The dual RT-PCR detection system successfully amplified the two target regions of the APV1, demonstrating high specificity and sensitivity and compensating for the limitations of single-primer detection methods. We tested 60 Areca palm samples from different geographical regions, highlighting its advantages in that the dual RT-PCR system efficiently and accurately detected APV1 in samples across diverse areas. The dual RT-PCR APV1 detection system provides a rapid, accurate, and sensitive method for detecting the virus and offers valuable technical support for research in preventing and managing yellow leaf diseases caused by APV1 in Areca palms. Moreover, the findings of this study can serve as a reference for establishing similar plants viral detection systems in the future.

Visual Eosin Y-Based Photosensitization Sensing Systems for Ultrasensitive Detection of Diclofenac with Single-Atom Co─N2O2 Site-Immobilized g-C3N4 Nanosheets.

It is highly desired to develop a visual sensing system for ultrasensitive detection of colorless diclofenac (DCF), yet with a significant challenge. Herein, a novel dye-based photosensitization sensing system has been successfully developed for detecting DCF for the first time, in which the used dye eosin Y (DeY) can strongly absorb visible light and then be decolorized obviously by transferring photogenerated electrons to g-C3N4 nanosheets (CN), while the built single-atomic Co─N2O2 sites on CN by boron-oxygen connection can competitively adsorb DCF to impede the photosensitization decoloration of DeY. This system exhibits a broad detection range from 8ngL-1 to 2 mgL-1 with 535nm light, an exceptionally low detection limit (3.5ngL-1), and remarkable selectivity. Through the time-resolved, in situ technologies, and theoretical calculations, the decolorization of DeY is attributed to the disruption of DeY's conjugated structure caused by the triplet excited state electron transfer from DeY to CN, meanwhile, the adsorbed oxygen facilitates the charge transfer process. The preferential adsorption of DCF mainly depends on the strong interactions between the as-constructed single-atom Co and Cl in DCF. This study opens an innovative light-driven sensing system by combining dye and single-atom metal/nanomaterial for visually intuitive detection of environmental pollutants.

Detection and Warning System for Sudden high Swells on the east Coast of the Korean Peninsula

Detection and Warning System for Sudden high Swells on the east Coast of the Korean Peninsula

Design and Analysis of a High-Gain, Low-Noise, and Low-Power Analog Front End for Electrocardiogram Acquisition in 45 nm Technology Using gm/ID Method

In this work, an analog front-end (AFE) circuit for an electrocardiogram (ECG) detection system has been designed, implemented, and investigated in an industry-standard Cadence simulation framework using an advanced technology node of 45 nm. The AFE consists of an instrumentation amplifier, a Butterworth band-pass filter (with fifth-order low-pass and second-order high-pass sections), and a second-order notch filter—all are based on two-stage, Miller-compensated operational transconductance amplifiers (OTA). The OTAs have been designed employing the gm/ID methodology. Both the pre-layout and post-layout simulation are carried out. The layout consumes an area of 0.00628 mm2 without the resistors and capacitors. Analysis of various simulation results are carried out for the proposed AFE. The circuit demonstrates a post-layout bandwidth of 239 Hz, with a variable gain between 44 and 58 dB, a notch depth of −56.4 dB at 50.1 Hz, a total harmonic distortion (THD) of −59.65 dB (less than 1%), an input-referred noise spectral density of &lt;34 μVrms/Hz at the pass-band, a dynamic range of 52.71 dB, and a total power consumption of 10.88 μW with a supply of ±0.6 V. Hence, the AFE exhibits the promise of high-quality signal acquisition capability required for portable ECG detection systems in modern healthcare.

Fraud Detection in Bank Loans with KYC Verification System

In today's technologically-driven financial landscape, bank loan fraud poses a significant threat to both financial institutions and their customers. While traditional methods of fraud detection have been moderately effective, the increasing sophistication of fraudsters necessitates the implementation of more advanced measures. This research proposes an integrated system for bank loan fraud detection that leverages the robustness of Know Your Customer (KYC) verification. By combining traditional KYC processes with advanced machine learning algorithms, this system seeks to provide a more comprehensive approach to detecting and preventing fraudulent loan applications. Initial results indicate a marked reduction in successful fraud attempts, as well as a decrease in false positives compared to conventional systems. Furthermore, the integrated system offers enhanced customer experience by streamlining the loan application process, reducing verification times, and ensuring greater security of personal data. As financial institutions continue to grapple with the challenges of fraud, this research underscores the importance of integrating traditional verification methods with cutting-edge technological solutions for optimal results

Ftdho Zfnet: Block chain based Fractional Tasmanian Devil Harris optimization enabled deep learning using attack detection and mitigation

Block chain technology is regarded for enhancing the characteristics of security because of decentralized design, safe distributed storage, and privacy. However, in recent times the present situation of block chain technology has experienced some crisis that may delay the quick acceptance and utilization in real-time applications. To conquer this subdues, a blockchain based system for attack detection and mitigation with Deep Learning (DL) named Fractional Tasmanian Devil Harris Optimization_Zeiler and Fergus network (FTDHO_ZFNet) is introduced. In this investigation, the entities utilized are owner, block chain, server, trusted authority and user. Here, authentication phase is done by means of Ethereum block chain by Key Exchange module and privacy preserved data sharing and communication is also done. Then, recorded log file creation is executed by the below mentioned stages. At first, a log file is generated with the basis of communication to record the events. After wards, the features are extracted by BoT-IoT database. Then, feature fusion is done by overlap coefficient utilizing Deep Q-Network (DQN). Moreover, data augmentation (DA) is doneusing bootstrapping method. At last, attack detection is observed by ZFNet tuned by FTDHO. Here, FTDHO is unified by Fractional Tasmanian Devil Optimization (FTDO) and Harris Hawks Optimization (HHO). Additionally, FTDO is integrated by Fractional Calculus (FC) concept and Tasmanian devil optimization (TDO). Furthermore, attack mitigation is performed. The performance measures applied for FTDHO_ZFNet are accuracy, and True Negative rate (TNR), observed supreme values with 92.9%, 93.8% and 92.9%.

Photostimulated Pyrothermoelectric Coupling in Two-Dimensional Tin Monoselenide Enabling Zero-Biased Multimodal Transducers.

Despite the advancement of the Internet of Things (IoT) and portable devices, the development of zero-biased sensing systems for the dual detection of light and gases remains a challenge. As an emerging technology, direct energy conversion driven by intriguing physical properties of two-dimensional (2D) materials can be realized in nanodevices or a zero-biased integrated system. In this study, we unprecedentedly attempted to exploit the photostimulated pyrothermoelectric coupling of two-dimensional SnSe for use in zero-biased multimodal transducers for the dual detection of light and gases. We synthesized homogeneous, large-area 6 in SnSe multilayers via a rational synthetic route based on the thermal decomposition of a solution-processed single-source precursor. Zero-biased SnSe transducers for the dual monitoring of light and gases were realized by exploiting the synergistic coupling of the photostimulated pyroelectric and thermoelectric effects of SnSe. The extracted photoresponsivity at 532 nm and NO2 gas responsivity of the SnSe-based transducers corresponded to 1.07 × 10-6 A/W and 13263.6% at 0 V, respectively. To bring universal applicability of the zero-biased SnSe transducers, the wide operation bandwidth photoelectrical properties (visible to NIR) and dynamic current responses toward two NO2/NH3 gases were systematically evaluated.

Clinical Performance of the Line Immunoassay and Digital Liquid Chip Method for Detecting Autoimmune Liver Disease Autoantibodies.

The identification of autoantibodies associated with autoimmune liver disease (ALD) is crucial for diagnosis and management. Various laboratory methods have been introduced to detect autoantibody profiles. However, the variable performance of these assays may create challenges for clinicians and patients. To investigate the concordance rates and diagnostic performance of 2 commercially available assays, line immunoassay (LIA) and digital liquid chip method (DLCM), in patients with ALD. A total of 291 serum samples were collected, consisting of 180 sera from patients with ALD and 111 sera from controls. The samples were detected through LIA and DLCM. The agreement and diagnostic performance of each assay were analyzed. There was substantial to almost perfect agreement among prevalent autoantibodies (anti-mitochondrial antibody M2, antibodies against gp210, Sp100, and Ro52). Nevertheless, the Cohen κ coefficient of some uncommon autoantibodies (anti-LKM-1, anti-LC-1, and anti-SLA/LP) between the 2 methods was not ideal. LIA showed slightly better sensitivity, accuracy, and negative predictive value, while DLCM exhibited slightly higher specificity and positive predictive value. LIA and DLCM demonstrated comparable performance for the detection of common ALD-related autoantibodies. LIA seemed to be more sensitive, while DLCM displayed more specificity. However, standardization of ALD autoantibody detection still faces challenges between these diverse detection systems. Comprehensive interlaboratory validation is essential to mitigate potential misunderstanding and confusion among patients and clinicians.

X-ray fluorescence detection system for trace elements in oil based on graphite curved crystal technology

Curved crystal technology is a key technology in the field of X-ray fluorescence trace detection. To detect trace sulfur, chlorine, and silicon in oil, the key parameters of Johann-type graphite curved crystal design were calculated based on geometrical optics theory and mathematical modeling, and a curved crystal optical system was designed. Subsequently, the hardware, firmware, software, and mechanical structure were designed according to specific requirements. Following the development of the relevant equipment, laboratory tests were performed on standard petroleum samples. Experimental results show that technology based on bent crystals can significantly reduce the detection limit of the instrument, and the detection limit of sulfur, chlorine, and silicon can reach below 1 ppm. In accordance with the experimental results, possible interference factors were analyzed, and suitable solutions were proposed. The performance of the proposed system was thus directly verified, showcasing its applicability in detecting trace elements.

Systematic literature review on intrusion detection systems: Research trends, algorithms, methods, datasets, and limitations

Abstract Machine learning (ML) and deep learning (DL) techniques have demonstrated significant potential in the development of effective intrusion detection systems. This study presents a systematic review of the utilization of ML, DL, optimization algorithms, and datasets in intrusion detection research from 2018 to 2023. We devised a comprehensive search strategy to identify relevant studies from scientific databases. After screening 393 papers meeting the inclusion criteria, we extracted and analyzed key information using bibliometric analysis techniques. The findings reveal increasing publication trends in this research domain and identify frequently used algorithms, with convolutional neural networks, support vector machines, decision trees, and genetic algorithms emerging as the top methods. The review also discusses the challenges and limitations of current techniques, providing a structured synthesis of the state-of-the-art to guide future intrusion detection research.