Detection System Research Articles

Predicting food adulterants in milk using Support Vector Machine (SVM)

Milk adulteration is a significant global issue, particularly in emerging nations, where inadequate monitoring and unhygienic conditions prevail. The adulteration of milk with various chemicals, such as urea, water, skimmed milk powder, sugar, and detergent, poses serious health risks, including heart problems, diarrhea, CNS disorders, irritation, and gastrointestinal disorders. Traditional detection methods are labor-intensive and require sophisticated equipment, which limits their practical application. This study aims to develop a machine learning-based approach to detect milk adulteration using attributes like Solids- Not-Fat (SNF), fat, Corrected Lactometer Reading (CLR), Total Solids (TS), temperature, and protein content. Various machine learning models were employed and evaluated for their performance, including Logistic Regression, Decision Trees, SVM, and Random Forests. The findings demonstrate that machine learning can effectively identify adulteration types, providing a foundation for the dairy industry’s practical and automated detection systems. This research comprehensively reviews common milk adulterants and highlights advanced detection methods to ensure milk quality and safety.

A review of time-series analysis for cyber security analytics: from intrusion detection to attack prediction

Understanding the current threat landscape as well as timely detection of imminent attacks are primary objectives of cyber security. Through time-series modeling of security data, such as event logs, alerts, or incidents, analysts take a step towards these goals. On the one hand, extrapolating time-series to predict future occurrences of attacks and vulnerabilities is able to support decision-making and preparation against threats. On the other hand, detection of model deviations as anomalies can point to suspicious outliers and thereby disclose cyber attacks. However, since the set of available techniques for time-series analysis is just as diverse as the research domains in the area of cyber security analytics, it can be difficult for analysts to understand which approaches fit the properties of security data at hand. This paper therefore conducts a broad literature review in research domains that leverage time-series analysis for cyber security analytics, with focus on available techniques, data sets, and challenges imposed by applications or feature properties. The results of our study indicate that relevant approaches range from detective systems ingesting short-term and low-level events to models that produce long-term forecasts of high-level attack cases.

Event Operation and Management in Risk Assessment for Outdoor Food Bazaar Events

This study evaluates risk management in organizing a food bazaar held in an open field at the Jakarta International Velodrome from May 31 to June 2, 2024. The research methodology includes direct observation, structured interviews with the organizing committee, and document analysis. The assessment results indicate that the event falls into the Very High Risk category for safety aspects, with potential hazards such as smuggling, theft, and fire. Additionally, the cleanliness of the area and healthcare facilities are deemed inadequate, categorized as High Risk. The limitations of hygiene facilities such as toilets and sinks, as well as the risk of dehydration, were also identified. This study recommends enhanced preparation and attention to risk management for similar events in the future to ensure the safety and comfort of visitors. By identifying, analyzing, and evaluating risks more comprehensively, it is hoped that event organizers can minimize potential hazards and improve the quality of future events. The study also emphasizes the importance of collaboration among various stakeholders, including the government, event organizers, security personnel, and healthcare services, to create a safe and comfortable event environment. Measures such as safety training for staff, adequate placement of healthcare facilities, and improved security systems can reduce the risks faced by visitors. Additionally, the study highlights the need for effective communication with the audience regarding safety and health protocols to be followed during the event. The implementation of technology such as crowd monitoring applications and early detection systems can also be a solution in managing risks more effectively.

IoT Leak Detection System for Onshore Oil Pipeline Based on Thermography

The vast expanses of remote onshore areas in oil-producing countries are home to a network of flow and collection pipelines that are susceptible to leaks. Most of these areas lack the infrastructure to enable the use of remote monitoring systems equipped with sensors and real-time data analysis to provide early detection of anomalies. This paper proposes a proof of concept for a monitoring system based on the Internet of Things (IoT) for real-time detection of pipeline leaks in onshore oil production fields. The proposed system, based on a thermal imaging leak detection method, informs the operator of the system’s operating status via a web page. The leak detection system communicates via a Zigbee network between the IoT devices and a 4G mobile network. The results of the tests carried out show that a visual and automatic IoT-based leak detection system is possible and plausible. The proposed leak detection system enables supervisors at remote stations and field workers to monitor the operating status of pipelines via computers, tablets, or smartphones, regardless of where they are.

A Plantar Pressure Detection and Gait Analysis System Based on Flexible Triboelectric Pressure Sensor Array and Deep Learning.

Gait detection is essential for the assessment of human health status and early diagnosis of diseases. The current gait analysis systems are bulky, limited in the scope of use, and cause interference with the movement of the measured person. Hence, it is necessary to develop a wearable gait detection system that is soft, breathable, lightweight, and self-powered. Here, a plantar pressure sensor array and gait analysis system based on a flexible triboelectric pressure sensor (FTPS) array is developed. Soft, breathable, and wearable electrospinning nanofiber film with excellent triboelectric properties is used as the plantar pressure sensor, achieving a high sensitivity of 45.1 mVkPa-1 in the range of 40-200 kPa and 19.4 mVkPa-1 in the range of 200-400 kpa. 32 FTPSs are integrated into an intelligent insole, which has the characteristics of soft, easy production, good air permeability, long-time stability, no external power supply, and etc. Based on the long short-term memory artificial neural network deep learning model, the accuracy of gait judgment can reach 94.23%. This work provides a feasible solution for real-time gait detection, which will have potential applications in human health assessment and early diagnosis of disease.

Evaluating the effectiveness of quantitative pupillometry in assessing dynamic aerobic training intensity

Sports injuries often arise from improper scheduling of exercise loads, and timely assessment of these loads is essential for minimizing injury risk. This article investigates the validity of using changes in pupillary light reflex (PLR) during dynamic aerobic training as a novel approach to evaluating exercise load. Dynamic aerobic training was conducted on a power bicycle for 15 min. With a 3-minute interval as the demarcation line, PLR measurement was performed before training and heart rate was recorded throughout the process. The Rating of Perceived Exertion (RPE) scale invented by Borg was used for scoring before training and after training. The normal distribution of data was confirmed through the Shapiro-Wilk test. Pearson correlation analysis was used to quantify the correlation between variables. The Receiver Operating Characteristic (ROC) curve and the area under the curve (AUC) analysis were used to determine the indicators of exercise load. The optimal threshold of the indicators was calculated through the Youden index to evaluate sensitivity and specificity. Thirty male second-tier athletes with a mean age of 23.66 ± 2.21 years, a mean height of 175.3 ± 6.5 cm, and a mean weight of 68.99 ± 10.35 kg participated in this study. Based on the RPE scale results, it was confirmed that the 15-minute dynamic aerobic exercise successfully elicited varying levels of perceived exertion among the athletes. The findings of this study indicate significant changes in PLR and heart rate (HR) with increasing exercise duration and external load. There were strong correlations between RPE and maximum constriction velocity (MCV) (|r| = 0.8309, p < 0.001, negative correlation), maximum diameter (INIT) (r = 0.7641, p < 0.001, positive correlation), time to reach 75% recovery (T75) (|r| = 0.7289, p < 0.001, negative correlation), and HR (r = 0.8170, p < 0.001, positive correlation). Additionally, the results suggest that MCV is the most significant potential indicator for detecting internal load, exhibiting high specificity and sensitivity (AUC = 0.8509, p < 0.001). Further analysis using the Youden index identified 5.07 mm/s as the optimal cutoff value for MCV, indicating that when MCV ≤ 5.07 mm/s, the athletes’ internal load has reached an “Intense” state. PLR may be a potential indicator for assessing internal load Further investigation could involve developing a non-invasive exercise load detection system based on pupillary variable indicators, providing a valuable new approach for accurately measuring exercise load.

Sine Cosine Algorithm Based on Optimal Convolutional Autoencoder for Intrusion Detection and Classification Models

Network security comprises a multifaceted method that aims to protect computer networks from malicious activities, unauthorized access, and data breaches. The security mechanism is Intrusion Detection which is an important constituent that is employed to monitor and analyse the network traffic for recognizing and responding to intrusive or suspicious behavior. Innovative methods such as deep learning (DL) are employed to enhance the effectiveness of Intrusion Detection Systems (IDSs). DL is extremely implemented for IDS owing to its proficiency for automatically learning and extracting complex patterns and features from massive and multifaceted network datasets. Neural network (NN) models, permit the system to distinguish between anomalous patterns and normal network behaviors, increasing the accuracy of intrusion detection. The flexibility of DL methods to emerging cyberattacks with their adeptness to handle large - scale and various data, positions them as a strong and efficient tool for proactive and intelligent intrusion detection in existing cybersecurity settings. This article presents a Sine Cosine Algorithm with Optimal Convolutional Autoencoder for Intrusion Detection and Classification (SCAOCAE - IDC) method. The developed SCAOCAE - IDC system presents a wide - ranging strategy to improve the precision and effectiveness of IDSs. The method combines diverse advanced mechanisms like Min - Max scalar normalization for data preprocessing, Sine Cosine Algorithm (SCA) for feature selection (FS), Convolutional Autoencoder (CAE) for better feature extraction and classification, and Heap - Based Optimization (HBO) for hyperparameter tuning. The Min - Max scalar makes sure of robust data normalization, SCA increasingly chooses main features, CAE capably captures complex patterns in the data, and HBO fine - tunes hyperparameters for improved system performance. By employing the synergistic combination of such modules, the presented SCAOCAE - IDC algorithm indicates considerable outcomes for increasing the reliability and accuracy of IDSs and classification systems.

An Efficient Intrusion Detection System using a Multiscale Deep Bi-Directional GRU Network to Detect Blackhole Attacks in IoT based WSNs

An Efficient Intrusion Detection System using a Multiscale Deep Bi-Directional GRU Network to Detect Blackhole Attacks in IoT based WSNs

Challenges and Techniques in Multilingual Fake News Detection

The spread of false information across language and cultural barriers poses a significant threat to global societies. This study investigates the obstacles in detecting misinformation in various languages, including linguistic diversity, limited datasets, cultural nuances, and the ever- changing strategies of disinformation propagators. These complex issues require advanced methods to effectively identify and combat false information across multiple languages. The research evaluates several prominent machine learning approaches, such as Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), Logistic Regression, Decision Trees, and Natural Language Processing (NLP) techniques. By examining their effectiveness in multilingual contexts, this study sheds light on the advantages and drawbacks of each method. It underscores the need for continuous innovation and collaborative efforts in developing robust detection systems that can adapt to the ever-changing landscape of misinformation. These initiatives aim to promote informed public discourse and improve information integrity across diverse cultural contexts. Key Words: Multilingual Fake News Detection, Machine Learning, Machine Learning algorithms, Natural Language Processing, Misinformation.

Comparison of Machine Learning Algorithm in Intrusion Detection Systems: A Review Using Binary Logistic Regression

In the era of increasing cyber threats, the implementation of robust Intrusion Detection Systems (IDS) is crucial for safeguarding network integrity. This study presents a comprehensive comparison of various machine learning algorithms employed in IDS, with a specific focus on binary logistic regression as a comparative tool. We utilized a well-established dataset to evaluate the performance of multiple algorithms, including decision trees, support vector machines, and neural networks. Our findings reveal significant variations in accuracy, precision, and recall across the different algorithms. Binary logistic regression served as an effective benchmark, highlighting the strengths and weaknesses of each model. This research contributes to the ongoing discourse in cybersecurity by providing empirical evidence on the efficacy of machine learning approaches in detecting intrusions, offering insights for future enhancements in IDS design.

Plant Disease Detection and Classification

The Plant Disease Detection and classification project, aims to develop an automated system using machine learning and deep learning techniques to detect and classify plant diseases early on by analyzing leaf images. Early disease detection is crucial for increasing crop yields, reducing pesticide use, and promoting sustainable farming practices. The system will provide detailed descriptions of detected diseases along with recommended prevention and treatment methods. By using advanced machine learning and deep learning algorithms and data augmentation techniques, the system aims to improve the accuracy and efficiency of disease detection. While existing research shows promise, challenges like dataset robustness and environmental variability remain. This project addresses these challenges by integrating diverse datasets and leveraging sophisticated machine learning algorithms. The ultimate goal is to provide farmers with a reliable tool for managing plant health, ensuring food security, and contributing to sustainable agricultural practices. This will help farmers enhance resilience in the face of increasing agricultural challenges. Key Words: Plant Disease Detection, Automated Disease Detection System, Machine Learning, Deep Learning, Data Augmentation, Data Normalization, Support Vector Machine, KNN Classifier, Convolutional Neural Network, Decision Trees, Random Forest.

Development of Low-Stress Double-Pass Filter Device for Methane and Ethane Flammable Gas Detection System

To meet the needs of industrial and environmental monitoring of methane and ethane flammable gases, a low-stress filter device for detecting methane and ethane simultaneously is developed based on the absorption spectra of methane and ethane and the thin-film design theory. In order to improve the sensitivity of the detection system and reduce the influence of environmental noise, the information of methane and ethane gas is extracted simultaneously by using a filter with double bandpass and wide cut-off in the middle-infrared and far-infrared bands. The transmittance of the filter is 91.3%, 93.1%, and 93.02% at the wavelengths of 3.31 μm, 6.71 μm, and 7.67 μm, respectively. The cut-off depth in the cut-off interval is above OD3, which meets the system’s requirements.

HyMNet: A Multimodal Deep Learning System for Hypertension Prediction Using Fundus Images and Cardiometabolic Risk Factors

Study Objectives: This study aimed to develop a multimodal deep learning (MMDL) system called HyMNet, integrating fundus images and cardiometabolic factors (age and sex) to enhance hypertension (HTN) detection. Methods: HyMNet employed RETFound, a model pretrained on 1.6 million retinal images, for the fundus data, in conjunction with a fully connected neural network for age and sex. The two pathways were jointly trained by joining their feature vectors into a fusion network. The system was trained on 5016 retinal images from 1243 individuals provided by the Saudi Ministry of National Guard Health Affairs. The influence of diabetes on HTN detection was also assessed. Results: HyMNet surpassed the unimodal system, achieving an F1 score of 0.771 compared to 0.745 for the unimodal model. For diabetic patients, the F1 score was 0.796, while it was 0.466 for non-diabetic patients. Conclusions: HyMNet exhibited superior performance relative to unimodal approaches, with an F1 score of 0.771 for HyMNet compared to 0.752 for models trained on demographic data alone, underscoring the advantages of MMDL systems in HTN detection. The findings indicate that diabetes significantly impacts HTN prediction, enhancing detection accuracy among diabetic patients. Utilizing MMDL with diverse data sources could improve clinical applicability and generalization.

Machine vision system for automatic defect detection of ultrasound probes

Machine vision system for automatic defect detection of ultrasound probes

In situ growth of L012-reduced gold nanoparticles-loaded graphitic carbon nitride nanocomposite for potential-resolved ratiometric electrochemiluminescence analysis

BackgroundMost electrochemiluminescence (ECL) analytical methods involve only changes in one single signal, which greatly limits their high sensitivity and stability for applied research. Potential-resolved electrochemiluminescence (PRECL), which provides calibrated analysis by self-correcting signal responses from dual channels, has attracted great interest in recent years. However, research on PRECL nanomaterials is still at an early stage. It is critical to develop a novel PRECL nanocomposite with high intensity in light emission, high resolution in potential separation, and reduced complexity in reaction systems for accurate detection. ResultsHere, a novel PRECL nanocomposite, L012-Au/g-C3N4, was synthesized by in situ growth of L012-reduced gold nanoparticles (L012-Au) on the surface of graphitic carbon nitride (g-C3N4) nanosheets. The g-C3N4 nanosheets served as effective carriers for L012 molecules and L012-Au, as well as efficient cathodic ECL emitters. With the addition of a single co-reactant (K2S2O8), two fully separated ECL peaks were observed around −1.3 V (ECL-1) and +0.6 V (ECL-2) at neutral conditions. The catalytic character of the incorporated gold nanoparticles accelerated the anodic ECL reaction and greatly enhanced the ECL intensity. Furthermore, an ECL mechanism based on the competitive relationship between K2S2O8 and dissolved oxygen was proposed. When dopamine (DA) was introduced into the PRECL reaction system, ECL-1 remained essentially unchanged, while ECL-2 intensity was significantly reduced. By analyzing the intensity ratio of ECL-2 to ECL-1 (I2/I1) to the detecting target, a ratiometric DA sensor with high sensitivity and stability was successfully constructed. SignificanceA nanocomposite L012-Au/g-C3N4 that can induce high-intensity ECL with two separated potentials in each polarity has been prepared by a simple synthetic method. The developed ratiometric sensor can eliminate systematic errors and improve accuracy. In addition, the multifunctionality of the nanocomposite and the necessity of only one co-reactant for neutral-condition ECL greatly reduced the reaction complexity. Our work offers new PRECL solutions to design highly sensitive and stable multi-response detection systems for in vitro diagnostics.

Wildfire CNN: An Enhanced Wildfire Detection Model Leveraging CNN and VIIRS in Indian Context

Introduction Wildfires are an unexpected global hazard that significantly impact environmental change. An accurate and affordable method of identifying and monitoring on wildfire areas is to use coarse spatial resolution sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). Compared to MODIS, wildfire observations from VIIRS sensor data are around three times as extensive. Objective The traditional contextual wildfire detection method using VIIRS data mainly depends on the threshold value for classifying the fire or no fire which provides less performance for detecting wildfire areas and also fails in detecting small fires. In this paper, a wildfire detection method using Wildfiredetect Convolution Neural Network model is proposed for an effective wildfire detection and monitoring system using VIIRS data. Methods The proposed method uses the Convolutional Neural Network model and the study area dataset containing fire and non-fire spots is tested. The performance metrics such as recall rate, precision rate, omission error, commission error, F-measure and accuracy rate are considered for the model evaluation. Results The experimental analysis of the study area shows a 99.69% recall rate, 99.79% precision rate, 0.3% omission error, 0.2% commission error, 99.73% F-measure and 99.7% accuracy values for training data. The proposed method also proves to detect small fires in Alaska forest dataset for the testing data with 100% recall rate, 99.2% precision rate, 0% omission error, 0.7% commission error, 99.69% F-measure and 99.3% accuracy values. The proposed model achieves a 26.17% higher accuracy rate than the improved contextual algorithm. Conclusion The experimental findings demonstrate that the proposed model identifies small fires and works well with VIIRS data for wildfire detection and monitoring systems.

Chip Appearance Inspection Using Machine Learning Techniques

In recent years, the increasing demand for chips has put forward higher requirements for chip production quality and production efficiency. After the chip manufacturing is completed before leaving the factory, it needs to be detected after packaging, which makes the development of chip detection and localization system difficult due to the small size and large number of chips and the high precision requirements for them. Therefore, this paper aims to design a fast, highprecision chip detection and positioning system for the positioning and detection needs of the chip packaging process, the chip detection and positioning system for in-depth research, to achieve the accurate positioning of the chip as well as the deformation produced by the detection of the main work is as follows: the first chapter is mainly about why this paper is mainly aimed at discovering the problems in the chip packaging process. The second chapter is about how to take pictures of the chip. Chapter 3 focuses on the pre-processing of the pictures. Chapter 4 focuses on determining whether the chip is deformed through different machine learning and deep learning techniques. Finally, the conclusion section summarizes the whole paper.

Classification of faults in friction stir processed composites using a machine learning and ensemble learning approach.

Abstract Aluminium alloy based surface composites with hard reinforcement particles have wide scope in aerospace and automobile manufacturing industries. In this paper, the aluminium composites, manufactured by friction stir processing (FSP) with varying parameters are investigated for the faults occurred during fabrication process. It explores a machine-learning approach to detect defects of surface hybrid composites with an Al6061 alloy matrix, reinforced with copper and graphene nano-powders, using friction stir processing and a tungsten carbide tool on a milling machine. Multi-sensor time series data (vibration, force, and current) collected during fabrication, is preprocessed and labelled with normal and defective categories (e.g., pin break, brazing break, rough surface, no composite) using visual inspection. The important time domain and frequency domain features are extracted using different libraries in python. Thenafter, various types of feature selection techniques, viz filter, wrapper and embedded methods are implemented to select most relevant features. The selected subset of features from all selection methods used, are applied to different machine learning and ensemble learning classifiers and their performances are evaluated. The optimal combinations of the type of feature selection method and classifier used, are obtained for efficient classification of surface defects in composited formed by FSP. The real time monitoring and defect detection system can be developed in future for the composites developed by FSP using the developed models.

Detecting and evaluating surface defects in additive manufacturing composite materials via image processing technique

Purpose This study aims to offer an image-based robust edge detection system that can estimate, identify, locate and label surface flaws during manufacturing for real-time surface issue diagnostics. Of great concern, this methodology extrapolates surface defect information from scanning electron microscopy (SEM) images of composite fracture surfaces. This study predicts changes in topological intensity of composite fracture surfaces and display them as real-time surface intensity values for the first time. Design/methodology/approach This work, however, introduces a novel robust edge detection method – based image processing – as it is shown to be effective in locating defects, as measured by SEM images of composite fracture surfaces created using additive manufacturing (AM). SEM images, obtained in this study, are related to previous study considering the fracture surfaces of reinforced thermoset composites created via the AM method. These SEM images are of two types: fracture surface of AM of carbon fiber reinforced thermoset composites and fracture surface of AM of syntactic foam reinforced thermoset composites. Initially, MATLAB environment is used for analyzing the SEM images; the technique used, as well as the validity are explained more in the methodology section. Findings The robust surface defect inspection approach used herein is found to be capable of predicting, identifying, localizing and labeling surface defects during production, allowing for real-time surface issue diagnosis. Further, this work makes it possible to use image processing and analysis of these surfaces to anticipate fluctuations in the topological intensity of the fracture surfaces of composites and represent them as values of surface intensity in real time. Originality/value Rising worldwide company rivalry requires a fast, accurate component failure diagnostic method. To create an efficient feature set, a surface defect inspection system must identify product flaws in real time. Thus, this study proposed an image-based robust edge detection system – based on MATLAB environment – that is capable of estimating, identifying, locating and labeling surface faults during production. This paves the way for an extensive set of high-quality tools for dealing with a wide range of problems associated with digital image processing in composites. As a result, the ability to define methodologies and rapidly prototype prospective solutions typically minimizes the cost and time required to implement a successful system during the design phase of an image processing system.

Healthcare with Wearable Devices and Machine Learning

As health management continues to grow in importance globally, driven by the quest for improved health outcomes, longevity, and personalized care, machine learning (ML) has become a popular technique for detecting and analyzing health indicators, playing an essential role in multiple areas of health detection, such as continuous heart and blood pressure monitoring, blood sugar tracking for diabetics, sleep analysis to improve sleep quality, and even disease prediction. With wearable devices, a particular scale of health detection data has been accumulated at this stage. With this data, machine learning can be predictive analytics to assess real-time health conditions, enabling early intervention and personalized treatment plans. However, data quality remains the most critical issue for machine learning-based health detection systems, as inaccuracies in sensor readings can lead to misdiagnoses or incorrect treatment decisions. In addition, privacy concerns are significant, as the sensitive health information collected by these devices requires strong security measures to protect users’ data. Moreover, the interpretability of machine learning algorithms, especially those involving deep learning, can be limited, hindering clinicians’ understanding and trust. This paper addresses these challenges by providing a comprehensive overview of relevant data sets used in health monitoring and examining the latest machine learning algorithms tailored for wearables-based health detection. It further delves into the current limitations and challenges facing this emerging field, providing insights into potential solutions and future directions for research and development.