Detection System Research Articles

Enhancing Automative Safety System for Driver Fatigue Detection by Using Mediapipe’s Framework

Traffic accidents caused by tiredness, exhaustion, and distracted driving are a big concern on a global scale. As per global Road Safety reports drowsy driving was reason for 91,000 road accidents in Tamil Nadu in the year 2023. In this study, I suggest a collective deep learning architecture for automatic driver sleepiness detection while some earlier works are suggested extracting the lips and eyes movements to identify sleepiness, more contemporary computer vision-based systems have performed only somewhat well. This is because they either use extremely large deep learning models with still-poor performance or they use hand-crafted features with traditional methods like K-NN Algotithm and openCV. To overcome such issues in this project I am planing to create a Driver Drowsiness Detection and Alerting System using Mediapipe‘s Face Mesh solution in Python. These systems assess the driver‘s alertness and warn the driver if needed. Continuous driving can be tedious and exhausting. A motorist may get droopy and perhaps nod off due to inactivity. we will create a drowsy driver detection system to address such an issue. For this, we will use Mediapipe‘s Face Mesh solution in python and the Eye Aspect ratio formula using by Navie Bayes Algorithm. My goal is to create a robust and easy-to-use application that detects and alerts users if their eyes are closed for a long time. Keyword: Navie Baye’s, Mediapipe Framework.

Detecting Denial of Service Attacks (DoS) over the Internet of Drones (IoD) Based on Machine Learning

The use of Unmanned Aerial Vehicles (UAVs) or drones has increased lately. This phenomenon is due to UAVs’ wide range of applications in fields such as agriculture, delivery, security and surveillance, and construction. In this context, the security and the continuity of UAV operations becomes a crucial issue. Spoofing, jamming, hijacking, and Denial of Service (DoS) attacks are just a few categories of attacks that threaten drones. The present paper is focused on the security of UAVs against DoS attacks. It illustrates the pros and cons of existing methods and resulting challenges. From here, we develop a novel method to detect DoS attacks in UAV environments. DoS attacks themselves have many sub-categories and can be executed using many techniques. Consequently, there is a need for robust protection and mitigation systems to shield UAVs from DoS attacks. One promising security solution is intrusion detection systems (IDSs). IDs paired with machine learning (ML) techniques provide the ability to greatly reduce the risk, as attacks can be detected before they happen. ML plays an important part in improving the performance of IDSs. The many existing ML models that detect DoS attacks on UAVs each carry their own strengths and limitations.

Optimasi Deteksi Tumor Otak Menggunakan Adaptive Multiscale Retinex dan YOLOV10 Pada Citra Digital

The incidence of brain tumors in Indonesia continues to rise annually, affecting both adults and children. Therefore, an effective detection system is required to differentiate MRI images showing the presence of brain tumors from those that do not. This distinction is crucial in the analysis and diagnosis of medical images. This study aims to optimize brain tumor detection in digital images by combining the Adaptive Multiscale Retinex (AMSR) method with the YOLOv10 algorithm. AMSR enhances the quality and contrast of brain images, making critical details more visible. The method adjusts the scale and intensity of image lighting to address uneven illumination and improve the visibility of brain structures. YOLOv10 is recognized for its speed and accuracy in real-time object detection. This algorithm employs deep learning techniques to identify and classify objects with high precision on enhanced brain image datasets. The model's performance was evaluated using metrics such as accuracy, sensitivity, specificity, and computation time. Results indicate that the combination of AMSR and YOLOv10 improves the accuracy and efficiency of brain tumor detection compared to conventional methods. The YOLOv10 model achieved a detection accuracy of 92%. This study provides significant contributions to early brain tumor detection technology, with important implications for the healthcare sector.

Blockchain-Based Fraud Detection System for Healthcare Insurance Claims

Blockchain-Based Fraud Detection System for Healthcare Insurance Claims

Implementasi Algoritma You Only Look Once (YOLOv8) untuk Mendeteksi Pelanggaran Lalu Lintas Berupa Tidak Menggunakan Helm (Studi Kasus di Jatiasih, Bekasi)

This research aims to implement the You Only Look Once (YOLO) algorithm in detecting traffic violations in the form of riders who do not use helmets in Jatiasih, Bekasi. The problem studied is the high number of violations in the form of riders who do not use helmets which play a vital role in protecting riders from danger. The expected solution is the development of an automatic detection system that is able to identify this offence with a high level of accuracy. The object of the research is motorcyclists in Jatiasih, Bekasi. The research method used includes applying the YOLO algorithm to video recordings from surveillance cameras at several monitoring points. Video data processing is done by a laptop and YOLO algorithm-based software. The results of this research are expected to support law enforcement efforts, increase awareness of helmet use, and improve road safety by building an automatic detection system that can identify offences such as not wearing a helmet. The results of this research will show how effective the YOLO algorithm is for spotting traffic offences.

Evaluation of DIAGNOdent pen for initial occlusal caries diagnosis in permanent teeth

BackgroundTo verify the validity of diagnosing initial caries on occlusal surface of permanent posterior teeth by laser fluorescence instrument DIAGNOdent pen.MethodsPatients from School of Stomatology in Wuhan University were selected and their posterior teeth were examined using DIAGNOdent pen and the International Caries Detection and Assessment System (ICDAS II) by an experienced dentist. After teeth extraction, histological criteria were used to determine the severity of the lesions. The sensitivity, specificity, accuracy, the area under the curve (AUC), and correlation of DIAGNOdent pen and ICDAS II were analyzed compared with histological criteria. Examiners’ agreement was measured.ResultsThe sensitivity range was 0.440-1 while that of specificity was 0.750–0.994. The accuracy and AUC were above 80% and 0.7 respectively. Consistency of examiners’ kappa values of ICDAS II, DIAGNOdent pen, and histological criteria were ranged from 0.629 to 0.840.ConclusionsICDAS II and DIAGNOdent pen can be effectively used in tandem or independently for the assessment of initial caries.

The Correction Method for Wavefront Aberration Caused by Spectrum-Splitting Filters in Multi-Modal Optical Imaging System

In current biomedical and environmental detection, multi-modal optical imaging technology is playing an increasingly important role. By utilizing information from dimensions such as spectra and polarization, it reflects the detailed characteristics and material properties of the targets. However, as detection system performance becomes more complex, issues such as aberrations introduced by multilayered lenses, signal attenuation, decreased polarization sensitivity, and latency can no longer be ignored. These factors directly affect the assessment of image details, influencing subsequent analyses. In this paper, we propose a method for designing and optimizing spectrum-splitting filters that considers the wavefront aberration and transmittance of the multi-modal optical imaging system. The method of optimizing coating phases to minimize scalar phase aberrations while maximizing system transmission leads to substantially improved imaging performance. Simulation and experimental results demonstrate that the method can improve the imaging performance. The proposed approach has potential applications in fields such as biomedical field, multi-spectral, remote sensing and microscopy.

An FPGA-Based YOLOv5 Accelerator for Real-Time Industrial Vision Applications

The You Only Look Once (YOLO) object detection network has garnered widespread adoption in various industries, owing to its superior inference speed and robust detection capabilities. This model has proven invaluable in automating production processes such as material processing, machining, and quality inspection. However, as market competition intensifies, there is a constant demand for higher detection speed and accuracy. Current FPGA accelerators based on 8-bit quantization have struggled to meet these increasingly stringent performance requirements. In response, we present a novel 4-bit quantization-based neural network accelerator for the YOLOv5 model, designed to enhance real-time processing capabilities while maintaining high detection accuracy. To achieve effective model compression, we introduce an optimized quantization scheme that reduces the bit-width of the entire YOLO network—including the first layer—to 4 bits, with only a 1.5% degradation in mean Average Precision (mAP). For the hardware implementation, we propose a unified Digital Signal Processor (DSP) packing scheme, coupled with a novel parity adder tree architecture that accommodates the proposed quantization strategies. This approach efficiently reduces on-chip DSP utilization by 50%, offering a significant improvement in performance and resource efficiency. Experimental results show that the industrial object detection system based on the proposed FPGA accelerator achieves a throughput of 808.6 GOPS and an efficiency of 0.49 GOPS/DSP for YOLOv5s on the ZCU102 board, which is 29% higher than a commercial FPGA accelerator design (Xilinx’s Vitis AI).

Blind assistance system for appliance control and public transport safety using CNN, MobileNet V2 and Yolo V8

The purpose of this research is to address the challenges faced by visually impaired individuals, particularly in handling household appliances independently. With approximately 285 million visually impaired individuals worldwide, technological solutions are crucial to enhancing their accessibility and independence. This paper introduces a Smart Assistance System designed to empower visually impaired individuals to interact with household appliances in real-time without assistance. In this study, three Convolutional Neural Network (CNN) algorithms are compared to develop the system. The evaluation metrics include accuracy, precision, recall, F1 score, and hamming loss on validation images. The performance comparison reveals that the custom architecture CNN, MobileNetv2, and YOLO models achieve F1 scores of 0.43, 0.63, and 0.24, respectively. To enhance object detection and classification, the paper suggests implementing bounding box buttons categorization using YOLOv8, which demonstrates superior performance with a 95% classification accuracy on testing images of home appliance buttons. They face similar difficult while in public and accessing public property. Expanding upon the proposed system’s capabilities, the paper introduces the concept of panic button detection and activation in a bus environment tailored for blind individuals. This system relies on various factors such as the number of people onboard, heart rate monitoring, and the detection of distress signals or SOS sounds emitted by the user. By integrating advanced sensing technologies and intelligent algorithms, this panic button detection system aims to provide prompt assistance and ensure the safety of visually impaired passengers in public transportation settings.

Optimal demodulation domain for microwave SQUID multiplexers in presence of readout system noise

The Microwave SQUID Multiplexer (μMUX) is the device of choice for the readout of a large number of low-temperature detectors in a wide variety of experiments within the fields of astronomy and particle physics. While it offers large multiplexing factors, the system noise performance is highly dependent on the cold- and warm-readout electronic systems used to read it out, as well as the demodulation domain and parameters chosen. In order to understand the impact of the readout systems in the overall detection system noise performance, first, we extended the available μMUX simulation frameworks, including additive and multiplicative noise sources in the probing tones (i.e., phase and amplitude noise), along with the capability of demodulating the scientific data, either in the resonator’s phase or the scattering amplitude. Then, considering the additive noise as a dominant noise source, the optimum readout parameters to achieve minimum system noise were found for both open-loop and flux-ramp demodulation schemes in the aforementioned domains. Later, we evaluated the system noise sensitivity to multiplicative noise sources under the optimum readout parameters. Finally, as a case study, we evaluated the optimal demodulation domain and the expected system noise level for a typical software-defined radio readout system. This work leads to an improved system performance prediction and noise engineering based on the available readout electronics and the selected demodulation domain.

Improved YOLOv5m model based on Swin Transformer, K-means++, and Efficient Intersection over Union (EIoU) loss function for cocoa tree (Theobroma cacao) disease detection

The cocoa tree is prone to diverse diseases such as stem borer, stem canker, swollen shot, and root rot disease which impedes high yield. Early disease detection is a critical component of diverse management processes that are implemented throughout the life cycle of cocoa plants. Consequently, several studies on the application of detection techniques to recognize diseases have been proposed by several researchers. This study proposes the YOLOv5m network for cocoa tree disease detection. The development of cocoa disease detection systems will aid farmers in early identification prompt response, and efficient management of related cocoa tree diseases which will ultimately increase yield and sustainability. To improve the performance of the YOLOv5m network, a Swin Transformer (Swin-T) was added to the backbone network to improve cocoa tree disease detection accuracy. By obtaining global information, the K-means++ algorithm was added to modify the choice of initial clustering locations, and Efficient Intersection over Union Loss (EIoU) was used as a bounding box regression loss function to speed up the bounding box regression rate, resulting in higher precision of the YOLOv5m network. The experimental assessment outcome of this study showed that the proposed method YOLOv5m (Swin-T, K-means++, EIoU) achieved 96% precision, mAP of 92%, and recall of 94%. Compared to the original YOLOv5m, precision improved by 5%, mAP improved by 6%, and recall by 5%. Comparing the proposed method to the conventional YOLOv5m, the latter showed improved performance and better accuracy with a high detection speed and compactness. This improvement offers a useful and effective method for detecting diseases related to cocoa trees.

Fluorescent Iron-Doped Polymer Dot Nanozyme-Based Cascade System for Dual-Mode Detection of Acetylcholinesterase Activity and Its Inhibitors.

The advancement of acetylcholinesterase (AChE) activity and its inhibitor assays is crucial for clinical diagnosis, drug screening, and environmental monitoring. A nanozyme-mediated cascade reaction system could offer promising prospects for a wide range of applications in such biosensing; however, the creation of nanozyme catalysts with diverse functionalities remains a significant challenge. Herein, we have proposed a multifunctional iron-doped polymer dots (Fe-PDs) nanozyme possessing excellent fluorescence and peroxidase (POD)-mimicking activity. Notably, the Fe-PDs nanozyme is capable of catalyzing H2O2 to produce a series of reactive oxygen species, which can simultaneously quench the fluorescence of Fe-PDs and induce a chromogenic reaction of 3,3',5,5'-tetramethylbenzidine (TMB), enabling the dual-mode detection of H2O2 through both fluorescence turn-off and absorbance turn-on signals. Furthermore, by integrating acetylcholine (ACh) and choline oxidase (ChOx), we have developed a three-enzyme (AChE-ChOx-POD) cascade-based fluorometric and colorimetric dual-mode sensing platform for monitoring AChE activity and its inhibitors. The sensitive and convenient dual-mode sensor has achieved low limits of detection with 0.5 mU/mL (fluorometry) and 0.014 mU/mL (colorimetry) for AChE, respectively, which are superior to the traditional Ellman's assay. More significantly, this sensor can also be extended to detect the reversible and irreversible inhibitors of AChE, such as tacrine (IC50 = 23.3 nM) and carbaryl (LOD = 0.8 nM). We firmly believe that this innovative dual-mode nanozyme-involved multienzyme cascade system-based sensing strategy will stimulate further exploration and serve as a versatile and practical tool for biochemical sensing applications.

Survey of federated learning in intrusion detection

Intrusion detection methods are crucial means to mitigate network security issues. However, the challenges posed by large-scale complex network environments include local information islands, regional privacy leaks, communication burdens, difficulties in handling heterogeneous data, and storage resource bottlenecks. Federated learning has the potential to address these challenges by leveraging widely distributed and heterogeneous data, achieving load balancing of storage and computing resources across multiple nodes, and reducing the risks of privacy leaks and bandwidth resource demands. This paper reviews the process of constructing federated learning based intrusion detection system from the perspective of intrusion detection. Specifically, it outlines six main aspects: application scenario analysis, federated learning methods, privacy and security protection, selection of classification models, data sources and client data distribution, and evaluation metrics, establishing them as key research content. Subsequently, six research topics are extracted based on these aspects. These topics include expanding application scenarios, enhancing aggregation algorithm, enhancing security, enhancing classification models, personalizing model and utilizing unlabeled data. Furthermore, the paper delves into research content related to each of these topics through in-depth investigation and analysis. Finally, the paper discusses the current challenges faced by research, and suggests promising directions for future exploration.

Determination of objective taste perception among Iranian medical sciences students during COVID‑19 pandemic in Yazd, Eastern Iran: a case-control pilot study

BackgroundTaste disorders in patients suffering from COVID-19 were popular. Some people even after recovery report residual effects of loss of smell and taste. This study aimed to investigate the taste Perception of Iranian Medical Sciences students during the COVID-19 pandemic.MethodsThe case-control study evaluated 50 Medical Sciences students with a history of COVID-19 infection, as confirmed by the Novel Coronavirus (2019-nCOV) CFX96™ Real-Time PCR Detection System. The other participants consisted of 50 volunteer students with COVID-19 negative. The taste perception was measured with 4 different concentrations of the basic tastes. The stimuli were applied to the protruded tongue. Subjects were asked to identify the researcher when they felt the taste. Data are expressed as frequency distribution and analyzed with the Chi-Square test (P < 0.05).ResultsIn this survey, 54% of participants were male and 46% were female, the mean age of participants was 22.96 ± 5 years. The results showed a significant difference in the sweet and bitter taste perception score according to the history of dietary supplement use. The bitter taste perception score declared a considerable difference since COVID-19 infection. There was no significant difference between the taste perception in the two studied groups for all 4 basic tastes according to gender, COVID-19 infection status, history of taste and smell disorders, and the elapsed time since COVID-19 infection.ConclusionOur findings could provide important insights into taste perception. The history of dietary supplementation may influence how sweetness and saltiness are perceived. There was a noticeable difference in bitter taste perception depending on the time that had elapsed since the most recent COVID-19 infection.

A Novel Invasive Weed Optimization and its Variant for the Detection of Polycystic Ovary Syndrome

Introduction: This study intends to provide a novel Invasive Weed Optimization (IWO) algorithm for the detection of Polycystic Ovary Syndrome (PCOS) from ultrasound ovarian images. PCOS is an intricate anarchy described by hyperandrogenemia and irregular menstruation. Indian women are increasingly finding reproductive disorders, namely PCOS. Methods: The women having PCOS grow more small follicles in their ovaries. The radiologists take a look into women's ovaries by use of ultrasound scanning equipment to manually count the number of follicles and their size for fertility treatment. These may lead to error diagnosis. Results: This paper proposed an automatic follicle detection system for identifying PCOS in the ovary using IWO. The performance of IWO is improved in Modified Invasive Weed Optimization (MIWO). This algorithm imitates the biological weeds' behavior. The MIWO is employed to obtain the optimal threshold by maximizing the between-class variance of the modified Otsu method. The efficiency of the proposed method has been compared with the well-known optimization technique called Particle Swarm Optimization (PSO) and with IWO. Conclusion: Experimental results proved that the MIWO finds an optimal threshold higher than that of IWO and PSO.

Online transaction fraud detection in the banking sector using machine learning techniques

In this paper Nowadays, online transactions are increasing rapidly and payment scams are also increasing. A hacker is a person who gains access to another person's financial information. Improving feature selection is important to detect transaction risk in large, multidimensional data. Fraud detection and classification performance is divided into two parts: training data and test data. During this process we have focused on data analysis and pre-processing. In a transaction’s dataset, the predictive variables influence how well a machine learning algorithm for transaction risk identification classifies and identifies fraudulent transactions. This study examines the application of machine learning techniques in improving fraud detection in the banking industry. Using advanced algorithms and historical transaction data, the model aims to identify anomalous patterns that indicate fraudulent activity. Feature engineering, ensemble methods, and anomaly detection algorithms are used to improve the accuracy and performance of fraud detection systems. reducing false positives and provides an adaptive security mechanism. By harnessing the power of machine learning, this approach aims to strengthen the banking sectors defines against sophisticated fraud schemes. This study not only contributes to the academic understanding of fraud detection, but also provides industry practitioners with a practical framework for implementing proactive measures. In navigating the complex landscape of financial security, this research serves as a guide for building agile and responsive systems that stay ahead of the threats that evolve in the ever-changing landscape of the banking industry.

Copper abietate/polyvinyl acetate composite film for enhanced humidity sensing in Chinese herbal medicine monitoring systems

Sensing materials derived from natural and biodegradable sources are increasingly being developed and applied to realize environmentally friendly technologies. In this paper, we present the synthesis, characterization, and application of a novel copper abietate/polyvinyl acetate (CuA/PVAc) composite film, specifically engineered to enhance the humidity-sensing capabilities of Chinese herbal medicine monitoring systems. The sensing material was synthesized by strategically melding copper abietate, a natural renewable material, with PVAc to exploit their combined synergistic properties for optimal humidity-sensing performance. Comprehensive characterization techniques, including inductively coupled plasma optical emission spectrometry (ICP-OES), field-emission scanning electron microscopy (FESEM), water contact angle measurements, and Fourier transform infrared (FT-IR) spectroscopy, were employed to confirm the formation of CuA/PVAc composites. These analyses confirmed the homogeneity and intended chemical composition of the composite films.The resultant CuA-1/PVAc-4 (0.4 μg/μL) composite film exhibited a good logarithmic relation (0.9988) with relative humidity (RH) over a wide range (11 %–97 %) and high sensitivity (24.55 Hz/%RH). Moreover, this study introduces a novel, eco-friendly approach by integrating the CuA/PVAc composite with a quartz crystal microbalance (QCM) sensor and a sophisticated wireless circuit that enables real-time, Wi-Fi-based humidity monitoring tailored for the preservation of Chinese herbal medicines. The implementation of this wireless humidity detection system represents a significant advancement in the application of environmentally friendly materials in sensor technology and offers a practical and scalable approach for precise environmental monitoring.

Explainable deep learning approach for advanced persistent threats (APTs) detection in cybersecurity: a review

In recent years, Advanced Persistent Threat (APT) attacks on network systems have increased through sophisticated fraud tactics. Traditional Intrusion Detection Systems (IDSs) suffer from low detection accuracy, high false-positive rates, and difficulty identifying unknown attacks such as remote-to-local (R2L) and user-to-root (U2R) attacks. This paper addresses these challenges by providing a foundational discussion of APTs and the limitations of existing detection methods. It then pivots to explore the novel integration of deep learning techniques and Explainable Artificial Intelligence (XAI) to improve APT detection. This paper aims to fill the gaps in the current research by providing a thorough analysis of how XAI methods, such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), can make black-box models more transparent and interpretable. The objective is to demonstrate the necessity of explainability in APT detection and propose solutions that enhance the trustworthiness and effectiveness of these models. It offers a critical analysis of existing approaches, highlights their strengths and limitations, and identifies open issues that require further research. This paper also suggests future research directions to combat evolving threats, paving the way for more effective and reliable cybersecurity solutions. Overall, this paper emphasizes the importance of explainability in enhancing the performance and trustworthiness of cybersecurity systems.

Shielding networks: enhancing intrusion detection with hybrid feature selection and stack ensemble learning

The frequent usage of computer networks and the Internet has made computer networks vulnerable to numerous attacks, highlighting the critical need to enhance the precision of security mechanisms. One of the most essential measures to safeguard networking resources and infrastructures is an intrusion detection system (IDS). IDSs are widely used to detect, identify, and track malicious threats. Although various machine learning algorithms have been used successfully in IDSs, they are still suffering from low prediction performances. One reason behind the low accuracy of IDSs is that existing network traffic datasets have high computational complexities that are mainly caused by redundant, incomplete, and irrelevant features. Furthermore, standalone classifiers exhibit restricted classification performance and typically fail to produce satisfactory outcomes when dealing with imbalanced, multi-category traffic data. To address these issues, we propose an efficient intrusion detection model, which is based on hybrid feature selection and stack ensemble learning. Our hybrid feature selection method, called MI-Boruta, combines mutual information (MI) as a filter method and the Boruta algorithm as a wrapper method to determine optimal features from our datasets. Then, we apply stacked ensemble learning by using random forest (RF), Catboost, and XGBoost algorithms as base learners with multilayer perceptron (MLP) as meta-learner. We test our intrusion detection model on two widely recognized benchmark datasets, namely UNSW-NB15 and CICIDS2017. We show that our proposed IDS outperforms existing IDSs in almost all performance criteria, including accuracy, recall, precision, F1-Score, false positive rate, true positive rate, and error rate.

A Low-Power, High-Resolution Analog Front-End Circuit for Carbon-Based SWIR Photodetector

Carbon nanotube field-effect transistors (CNT-FETs) have shown great promise in infrared image detection due to their high mobility, low cost, and compatibility with silicon-based technologies. This paper presents the design and simulation of a column-level analog front-end (AFE) circuit tailored for carbon-based short-wave infrared (SWIR) photodetectors. The AFE integrates a Capacitor Trans-impedance Amplifier (CTIA) for current-to-voltage conversion, coupled with Correlated Double Sampling (CDS) for noise reduction and operational amplifier offset suppression. A 10-bit/125 kHz Successive Approximation analog-to-digital converter (SAR ADC) completes the signal processing chain, achieving rail-to-rail input/output with minimized component count. Fabricated using 0.18 μm CMOS technology, the AFE demonstrates a high signal-to-noise ratio (SNR) of 59.27 dB and an Effective Number of Bits (ENOB) of 9.35, with a detectable current range from 500 pA to 100.5 nA and a total power consumption of 7.5 mW. These results confirm the suitability of the proposed AFE for high-precision, low-power SWIR detection systems, with potential applications in medical imaging, night vision, and autonomous driving systems.