Development Of Monitoring System Research Articles

Design Science Approach Comparing Ensemble Learning and Artificial Neural Networks for Uncertainty-Aware Helicopter Turbine Engines Health Monitoring

This work presents the development of an uncertainty-aware health monitoring system for helicopter turbine engines, focusing on improving operational availability and reducing maintenance costs. We address the critical issue of uncertainty quantification in data-driven fault detection and prognostics, essential for increasing system reliability. The project follows an iterative development cycle, incorporating multiple techniques for data processing, such as polynomial feature generation and data cleansing, and model development, including ensemble learning and artificial neural networks. Evaluation is performed using K-fold and group-fold cross-validation. The final solution consists of a cascaded ensemble learning model combining bagged linear regression built on polynomial features and random forest. This model demonstrates robust performance, achieving a test score of 0.955719 and a validation score of 0.886953, showcasing the effectiveness of uncertainty-aware machine learning methods in health monitoring systems.

Leather-Based Shoe Soles for Real-Time Gait Recognition and Automatic Remote Assistance Using Machine Learning.

Real-time monitoring of gait characteristics is crucial for applications in health monitoring, patient rehabilitation feedback, and telemedicine. However, the effective and stable acquisition and automatic analysis of gait information remain significant challenges. In this study, we present a flexible sensor based on a carbon nanotube/graphene composite conductive leather (CGL), which uses collagen fiber with a three-dimensional network structure as the flexible substrate. The CGL-based sensor demonstrates a high dynamic range, with notable pressure responses ranging from 0.6 to 14.5 kPa and high sensitivity (S = 0.2465 kPa-1). We further developed a device incorporating the CGL-based sensor to collect foot characteristic signals from human motion and designed smart sports shoes to facilitate effective human-computer interaction. Machine learning was employed to collect and process gait characteristic information in various states, including standing, sitting, walking, and falling. For real-time monitoring of falls, we optimized the K-Nearest Time Series Classifier (KNTC) algorithm, achieving an accuracy of 0.99 and a prediction time of only 13 ms, which highlights the system's excellent intelligent response capabilities. The system maintained a gait recognition accuracy of 90% across diverse populations, with low false-positive (3.3%) and false-negative (3.3%) rates. This work demonstrates stable gait recognition capabilities and provides valuable methods and insights for plantar behavior monitoring and data analysis, contributing to the development of advanced real-time gait monitoring systems.

Development of an IoT-based sleep pattern monitoring system for sleep disorder detection

Development of an IoT-based sleep pattern monitoring system for sleep disorder detection

Development of Self-generated and LPWA-based Crop Growth Environment Monitoring and Bigdata Analysis System

Development of Self-generated and LPWA-based Crop Growth Environment Monitoring and Bigdata Analysis System

Intelligent Sentinel satellite image processing technology for land cover mapping

Purpose. This article proposes to develop an intelligent Sentinel satellite image processing technology for land cover mapping using convolutional neural networks. The result will be an image with improved spatial resolution. Methodology. The paper presents a technology using a combination of biquadratic interpolation, histogram alignment, PCA transform, as well as a parallel residual architecture of convolutional neural networks. The technology increases the information content of Sentinel-2 optical images by combining 10 and 20-meter resolution data, resulting in primary 20-meter images with improved spatial resolution. Findings. The root mean square error (RMSE = 3.64) indicates a high accuracy in reproducing the spectral properties of the images. The correlation coefficient (CC = 0.997) confirms a high linear relationship between the estimated and observed images. The low value of Spectral Angle Mapper (SAM = 0.52) with the high Universal Image Quality Index (UIQI = 0.999) indicates high quality and structural similarity between the synthesized and reference images. These results confirm the proposed technology’s effectiveness in enhancing the spatial resolution of Sentinel satellite images. Originality. Traditional pansharpening methods of multispectral images developed for satellite images with panchromatic channels cannot be directly applied to Sentinel multispectral data, because these images do not contain a panchromatic channel. In addition, atmospheric conditions and the presence of clouds affect the quality of optical images, complicating their further thematic processing. The proposed technology, using biquadratic interpolation, histogram alignment, convolutional neural networks, and PCA transformation, removes clouds and enhances the spatial resolution of the primary 20-meter optical satellite image channels of Sentinel-2. This technology reduces color distortion and increases the detail of digital optical images, which allows for more accurate analysis of the state of the earth’s surface. Practical value. The results obtained can be used to improve the methods for processing Sentinel satellite images, which provide high spatial resolution and accurate preservation of spectral characteristics. It provides the foundation for the development of new geographic information systems for land cover monitoring.

Secure low-cost photovoltaic monitoring system based on LoRaWAN network and artificial intelligence

The advanced study will focus on developing a secure and cost-effective photovoltaic monitoring system using Long Range Wide Area Network (LoRaWAN) and Artificial Intelligence technologies for highly risky missions. With the world shifting to using renewable energy, the development of secure and efficient cost-effective monitoring systems for photovoltaic installations becomes very important. However, most of the solutions in use for PV installation monitoring are characterized by high costs, hence their limited use, especially in developing regions. In this regard, the present work presents country-specific protocols designed for the country of Turkey. The proposed system in this study is expected to offer real-time monitoring and data collection at a very meager cost by utilizing inexpensive microcontrollers and sensors. The accuracy rate demonstrated by the research is highly above board: 97.65%. It costs $210, with a very low power consumption of 0.5W and a range of 10 km. The major features underpin real-time monitoring with wide area coverage for effectiveness and efficiency in PVs monitoring. Test results in many places around Turkey have turned out reliable and accurate to justify a possible reduction in operational costs without compromising the quality of data received. This design is reinforced by a detailed mathematical model that assures one of its thorough analysis in performance. There is custom-built firmware that enhances communication efficiency and ensures data integrity in implementation. Comparative studies carried out with existing technologies indicate that the proposed system is cost-effective and operationally sound. This work gives a scalable, low-cost solution that probably might raise the uptake of PV systems in economically constrained areas.

Mapping the Public Health and Health Promotion Capacity in Oman

Abstract Background An essential condition to enabling people to increase control over their health is to have sufficient capacity for public health (PH) and health promotion (HP). Despite the recognition of its importance, a comprehensive view of the PH and HP capacity in the sultanate of Oman is not yet available. This study provides a mapping of the country’s PH and HP capacity at national, governorate and district levels. Methods An online quantitative survey was performed amongst 70 health policy maker and experts in PH and HP using a questionnaire based on the PH capacity framework of Aluttis et al., yielding data on the country specific context with relevance for PH and 6 PH capacity domains, each divided in subdomains: leadership and governance; organizational structures, workforce, financial resources, partnerships, and knowledge development. Internal consistencies of the scales measuring the capacity subdomains ranged from .75 to .99. Results The survey revealed relatively low scores, representing partial development of capacities, for most of the public health capacity domains. The highest levels of capacity were observed for knowledge development (health information and monitoring systems, reporting systems and knowledge infrastructure), organizational structures (institutional capacity, program delivery structures, PH within health services, and response emergencies) and partnerships (formal consortia, informal partnerships and multisectoral coordination). In contrast, leadership and governance, workforce development, and financial resources score lower. Conclusions Mapping the strengths and weaknesses of PH and HP capacity in Oman allows to make recommendations for improving population health. Whereas all domains of PH and HP capacity are partially developed, there is a need to further improve capacity in all domains. Key messages • PH and HP capacities are partially developed in Oman. • Mapping the PH and HP capacity allows to identify strengths and weaknesses and make recommendations for improving public health.

Design and evaluation of a gas, temperature profiling and data acquisition system to monitor the performance of a batch fixed dome biogas digester

Recovering energy from biomass and treating organic waste can be done effectively through a biogas digester system. However, monitoring the performance and control of the system to produce biogas is possible through the online process. Therefore, this study developed a novel low-cost gas and temperature profiling system (GTPS) and data acquisition system (DAS) used in biogas digester. The GTPS is designed to monitor and record the amount of methane (CH4) and carbon dioxide (CO2), the main constituent of biogas, from 0 to 100% and to measure the slurry temperature in the digester using a K-type thermocouple. To test the developed monitoring system, the biogas yield (%) and temperature were monitored, and the result obtained agreed with that reported and obtained in the literature (50–70% and 30−40%) for CH4 and CO2, respectively, followed by the desired mesophilic temperature condition for biogas production (20–35 °C). The study employed an RS-232/RS-485 converter for data communication and a low-cost thermocouple module (isolated analog input) that was installed in the data acquisition system. Interestingly, the application of the converter equipment has never been used in any study on monitoring a biogas digester.

Development of an online neutron beam monitoring system for accelerator-based boron neutron capture therapy in a hospital.

Boron neutron capture therapy (BNCT) is a next-generation radiotherapy, utilizing both an external neutron beam and a -containing pharmaceutical. A compact accelerator for a high intensity neutron source was installed to conduct BNCT in a hospital. The dose administered to a patient was evaluated by measuring the proton beamcurrent. Neutron intensity should be monitored in real-time by measuring the neutrons emitted from the target during BNCT irradiation. This is crucial due to potential neutron target degradation. Online neutron beam monitoring systems are required for reliable measurements of the administered neutron dose. An online neutron beam monitoring system was developed to monitor neutron intensity irradiating on the patient at the National Cancer Center Hospital (NCCH). The neutron detector comprised a back-illuminated thin Si diode of 40- thickness and an ultrathin natural LiF neutron converter of 0.05- thickness. The neutron detector was installed on the neutron target unit, regardless of whether a patient was present, without any additional modifications to the setup. The response functions for high photon dose rates of upto 100 Gy/h were measured. The pulse heights were measured using the neutron beam monitor during BNCT neutron irradiation. Neutron temporal response measured using the online beam monitor was acquired and compared with the proton beam current and the measurements at a patient position. From this measurement at the patient position, the neutron fluence rate irradiating on a patient wasobtained. The neutron events were separated from the photon events. The neutron counting rates increased rapidly with the starting of proton beam irradiation and dropped to zero upon its termination. During intermittent drops and recoveries in the proton beam, the neutron beam monitor for counting rates responded quickly, synchronizing with the beam current. A scatter plot of the neutron counting rate and proton beam current indicated a good linear correlation. A direct relationship between the online neutron beam monitor's neutron counting rates and those of the patient neutron detector showed a good correlation coefficient of 0.84. A ratio of the both neutron counting rates showed a standard deviation of 6%. The correlation coefficient and standard deviation were improved to 0.94 and 1.5%, by re-binning the neutron temporal response with longer acquisition period than 1 s. Using the online neutron beam monitor, the neutron fluence rate was obtained from the direct relationship within 1.5%. Therefore, real-time monitoring of neutron intensity was achieved within the acceptable level as per the International Commission on Radiation Units and Measurementsreport. The online neutron beam monitoring system was developed to monitor the BNCT neutron beam intensity at NCCH. The temporal response of the neutron beam monitor was synchronized with the neutron counting rate at the patient position. Using the online neutron beam monitor, the neutron fluence rate irradiating on the patient can be monitored from the direct relationship. Fluctuation of the neutron beam intensity through BNCT irradiation and the degradation of the lithium target through the lifespan of the neutron target could be monitored using the neutron beammonitor.

Regulatory and legal component of administrative regulation of probation

The article examines the normative-legal component of the administrative regulation of probation.Itwasdeterminedthattheadministrative and legal regulation of probation is a set of legal norms and mechanisms that determine the order of organization and implementation of probation measures in the state, the competence of state bodies, their duties and rights regarding control over the implementation of probation measures and interaction in this area with the courts , law enforcement agencies and social services. It is noted that probation is a system of supervision, control and social-educational measures applied by court decision to offenders instead of imprisonment or as a substitute for punishment, includes individual supervision, assistance in resocialization and measures aimed at preventing repeated crimes and ensuring safety society, while integrating the offender into social life without isolation. It is emphasized that the NPA system of administrative and legal regulation of probation includes international legal standards, national NPAs, including Acts of central executive bodies, local normative acts, agreements, methodological recommendations, etc., the norms of which are aimed at the development of alternative punishments, provision of rehabilitation and social support of offenders, regulation of the organization and implementation of probation in Ukraine, determine the legal basis for the activity of probation authorities, and also establish the procedure for providing probation services for persons who have committed a criminal offense. It was determined that the shortcomings of the administrative and legal regulation of probation are: the imperfection of the legislative framework, violation of the probation procedure, clear criteria for assessing the convicted person and the system for assessing the risks of recidivism, the mismatch of resources and the burden on the system, the low level of coordination between subjects of probation, the prevalence of ineffective methods of psychological works; society is not aware of the purpose and mechanism of probation, etc. It has been stated that the prospects for improving the normative-legal basis of the administrative-legal regulation of probation in Ukraine are: harmonization of legislation with international standards, institutional strengthening of the probation system, expansion of various approaches to probation, improvement of mechanisms for rehabilitation and support of offenders, involvement of public organizations, development of electronic monitoring systems , expanding educational programs for probation officers, increasing funding, research and analysis of probation effectiveness, etc.

E-Skin and Its Advanced Applications in Ubiquitous Health Monitoring.

E-skin is a bionic device with flexible and intelligent sensing ability that can mimic the touch, temperature, pressure, and other sensing functions of human skin. Because of its flexibility, breathability, biocompatibility, and other characteristics, it is widely used in health management, personalized medicine, disease prevention, and other pan-health fields. With the proposal of new sensing principles, the development of advanced functional materials, the development of microfabrication technology, and the integration of artificial intelligence and algorithms, e-skin has developed rapidly. This paper focuses on the characteristics, fundamentals, new principles, key technologies, and their specific applications in health management, exercise monitoring, emotion and heart monitoring, etc. that advanced e-skin needs to have in the healthcare field. In addition, its significance in infant and child care, elderly care, and assistive devices for the disabled is analyzed. Finally, the current challenges and future directions of the field are discussed. It is expected that this review will generate great interest and inspiration for the development and improvement of novel e-skins and advanced health monitoring systems.

Utilizing IoMT-Based Smart Gloves for Continuous Vital Sign Monitoring to Safeguard Athlete Health and Optimize Training Protocols.

This paper presents the development of a vital sign monitoring system designed specifically for professional athletes, with a focus on runners. The system aims to enhance athletic performance and mitigate health risks associated with intense training regimens. It comprises a wearable glove that monitors key physiological parameters such as heart rate, blood oxygen saturation (SpO2), body temperature, and gyroscope data used to calculate linear speed, among other relevant metrics. Additionally, environmental variables, including ambient temperature, are tracked. To ensure accuracy, the system incorporates an onboard filtering algorithm to minimize false positives, allowing for timely intervention during instances of physiological abnormalities. The study demonstrates the system's potential to optimize performance and protect athlete well-being by facilitating real-time adjustments to training intensity and duration. The experimental results show that the system adheres to the classical "220-age" formula for calculating maximum heart rate, responds promptly to predefined thresholds, and outperforms a moving average filter in noise reduction, with the Gaussian filter delivering superior performance.

Unlocking potential: exploring the interplay between entrepreneurship and research competence across diverse academic disciplines

This study aims to identify and analyze the self-assessment of entrepreneurship sub-competences among students at Latvian higher education institutions. The research questions addressed include: What is the self-assessment of students’ entrepreneurship competence at different levels of education? How and to what extent does the self-assessment of students’ entrepreneurship competence vary across different thematic groups? How and to what extent does the self-assessment of students’ entrepreneurship competence differ between academic and professional programs? Do bachelor’s and Master’s students’ entrepreneurship competences improve during the study process? Additionally, the study explores the correlation between students’ self-assessment of their entrepreneurship competence and their research competence. Data were collected using an assessment tool for students’ transversal competences developed in the ESF project “Development and implementation of the education quality monitoring system”, which consists of an online survey data and were analyzed with SPSS Statistics v.21. The results show that students evaluated their entrepreneurship competence just above average. Bachelor-level students improved their entrepreneurship competences during their studies, but there were no statistically significant differences between Master’s students’ evaluations in their first and last study years. Network analysis indicates that the entrepreneurship sub-competence “Identification, mobilization, and efficient use of internal and external resources” is connected with most research competence dimensions, and improving this sub-competence will therefore have the most effect on influencing research competence.

Advanced power quality monitoring with dilated convolutional neural networks for smart nanogrids

This study presents the development of an advanced power quality monitoring (PQM) system using dilated convolutional deep neural networks (DCDNN) for smart nanogrids. Traditional convolutional neural networks (CNNs) face challenges in efficiently handling high-dimensional data and identifying nuanced features due to their limited receptive field. To overcome these limitations, our DCDNN model employs a novel use of dilated convolutions to expand the receptive field without increasing network complexity, enabling the capture of long-range dependencies essential for accurate classification of power quality disturbances (PQDs). In addition, the integration of multi-channel time-series data processing, including voltage, current, and frequency measurements, is unique to our approach. The model processes multi-channel time-series data, including voltage, current, and frequency measurements, achieving an average accuracy of 98.9% across 29 PQD classes and 100% accuracy in 6 main classes, with a reduced detection time of 47 µs per segment. This approach not only improves real-time disturbance detection but also enhances the reliability and efficiency of electrical systems. The results demonstrate significant improvements over existing methods, underscoring the potential of DCDNNs in advancing PQM practices. Future work will focus on validating the model in real-world conditions and optimizing its computational efficiency.

Silicone Rubber-Packaged FBG Sensing Information and SSI-COV-Recognized Modal Parameters Motivated Damage Identification in Pipe Structures

Pipes are the main structures serving as the lifeline for oil and gas transportation. However, they are prone to cracks, holes and other damages due to harsh working environments, which can lead to leakage incidents and result in significant economic losses. Therefore, the development of structural health monitoring systems with advanced online diagnostic methods is of great importance for identifying local damages and assessing the safety state of pipe structures. These efforts can guide rapid repairs and ensure the continuous, efficient and cost-effective transportation of oil and gas resources. To address this problem, this paper proposes the development of a pipe monitoring system based on quasi-distributed fiber Bragg grating (FBG) sensing technology. The SSI-COV method is employed to process the sensor responses and extract the modal parameters of the structure. Based on this foundation, an enhanced damage identification index is proposed, which mitigates the effects of support and excitation positions on damage identification. The pipe structure can be regarded as a continuous super-statical beam, and based on its structural symmetry, a unit structure, specifically a stainless-steel pipe with fixed ends, is regarded as the experimental subject. Impact experiments have been conducted to analyze its behavior in both undamaged and damaged states. The research indicates that by using the proposed modal parameter identification method and the ASMDI damage index, ASMDI exhibits peak values at damage locations of the pipe structure. This allows for the identification of structural damage with high accuracy, fast processing efficiency and strong robustness. The study provides an effective and reliable damage diagnosis method, which can contribute to the refinement and visualization of pipe structural health monitoring systems.

Evaluation of Autonomous Navigation and Path Accuracy for Unmanned Surface Vehicles Using IMU Sensor and GPS Data

The development of autonomous systems for environmental monitoring is becoming increasingly important. This project focuses on creating a robotic marine vessel, Aqua Sense, for efficient water quality monitoring. Existing methods are often labour-intensive, time-consuming, and lack real-time data capability. The objective is to develop a robotic vessel with autonomous navigation for water quality applications. A comprehensive design approach, including a literature review and careful component selection, was employed. Key components include the Arduino Mega, Blue Robotics T200 thruster, GlobalSat BU-353N5 GPS sensor, BNO055 Inertial Measurement Unit (IMU), and a Li-po 11.1v battery. In this paper, several field trials data collection is presented to evaluate the navigation algorithm. These findings validate the feasibility of Aqua Sense for real-world applications. The varying distance errors at points B and C demonstrate an error range, with a maximum error of approximately 1.130m at point B (about 0.11% of the target location) and a significant variation at point C, reaching up to 1.660m (approximately 0.15%).

Development of a Semi-Autonomous Pulse and Receive Concrete Inspection System

Concrete structures are being subjected to increasing loads and used beyond their intended lifespan. When combined with operators shrinking maintenance budgets it is imperative structures are monitored for damage. Acoustic Emission (AE) and AE Tomography offer a method for damage detection and characterisation. However, they are encumbered by their equipment and setup requirements, and best used on areas where damage is already known to have occurred. An autonomous pulse catch system could be used to identify these areas. To explore the potential of such a system, seven concrete beams differentiated by their aggregate size were tested using an automated pulse and receive system. The effect of aggregate size, and pulse frequency on attenuation and wavespeed are investigated. The pulse regime consisted of narrowband pulses ranging from 30 kHz to 500 kHz. The results show that high frequency pulses experienced greater attenuation than low frequency pulses. Whilst a link appears between the size of the aggregate and the rate of attenuation of high frequency signals. A strong negative correlation between amplitude loss and pulse frequency was observed. For the specimen containing coarse aggregate, pulse velocity was constant between 100 kHz and 500 kHz, whereas specimens containing coarse aggregate experienced frequency dependant attenuation. This testing shows the significant potential for an automated pulse and receive system, which could lead to the development of an autonomous health monitoring system. In addition, the approach can be instrumental in developing large data sets, that can be used in Machine Learning processes, to develop a deeper understanding of AE signal propagation in concrete materials.

Experimental Investigation of Chloride Ion Monitoring in Concrete Considering the Coupling Effect of Temperature and Humidity

Ag/AgCl electrodes have acquired significant research attention due to their crucial role in the electrochemical monitoring of chloride ion concentration. However, the electrode potential is susceptible to changes in temperature and humidity, and comprehensive investigations regarding the influence of moisture are lacking. Existing studies on Ag/AgCl electrodes have been qualitative, primarily focusing on analyzing the trend of electrode potential to detect variations in Cl- concentration. This study proposes an experimental method that enables precise control of temperature and humidity within concrete. The effects of temperature and humidity coupling on Ag/AgCl electrode potential in a concrete environment and a fundamental model were established correlating Ag/AgCl electrode potential with temperature, humidity, and chloride ion concentration, thereby enhancing the sensing accuracy. The developed model corrected errors from temperature and humidity coupling effects on the Ag/AgCl electrode potential. A quantitative investigation on using Ag/AgCl electrodes for monitoring Cl- concentration was conducted, facilitating their application in engineering and the development of non-destructive monitoring systems for chloride ions in concrete structures.

Surface quality prediction in-situ monitoring system: A deep transfer learning-based regression approach with audible signal

Surface roughness plays an indispensable and fundamental role as a leading indicator of the surface quality of machined parts in the manufacturing process. The precise and effective monitoring and prediction of surface roughness is crucial for surface quality control. In this regard, the development of an in-process surface quality monitoring system is necessary, which has the promising potential to achieve this goal. Such a system typically comprises data-driven models for decision-making and sensing techniques for detecting associated process information. However, some challenges still exist in building such systems. Firstly, the architecture design and deployment of data-driven models, specifically deep learning (DL)-based models, demand adequate domain knowledge. Secondly, most models trained on specific tasks with limited datasets are prone to suppressing their versatility and generalization across different machining conditions. Additionally, in most cases, reliance on handcrafted features to represent dynamic information on various signals during model training necessitates extensive expertise in selecting appropriate feature types. Furthermore, due to the nature of their low dimensionality, handcrafted features have difficulty in capturing of overall process-related underlying patterns from dynamics signatures, which is time-varying and often occurs in transient events. To address these challenges, this paper proposes the regression-based pre-trained convolutional neural network (pre-trained CNN) combined with Mel-spectrogram images based on the transfer learning method for surface roughness prediction. Within the context, the architecture of the transfer model is slightly adapted from already well-trained CNNs. Initial weights in each layer of the CNN model are directly inherited and then fine-tuned through the Bayesian optimization tuning method. Besides, the audible sound signals are captured and subsequently converted into 2D Mel-spectrogram images with variant time lengths, which are separately engaged to retrain and validate four existing pre-trained CNN models (VGG16, VGG19, ResNet50V2 and InceptionResNetV2). Eventually, the effectiveness of proposed models and comparison of their predictive capabilities are further validated through a case study in the turning process. The results demonstrate that each applied pre-trained CNN model is capable of effectively predicting surface quality with satisfactory prediction results. Therefore, the proposed method can facilitate the establishment of a machining monitoring system concerning its accuracy, reliability, and robustness.

Artificial Intelligence-Assisted Environmental DNA Metabarcoding and High-Resolution Underwater Optical Imaging for Noninvasive and Innovative Marine Environmental Monitoring

To effectively protect the marine environment, it is crucial to establish effective environ mental monitoring platforms. Traditional marine environmental monitoring methods heavily rely on morphological identification and field expertise, with the sampling process being disruptive and potentially destructive to vulnerable marine environments. In light of emerging biomonitoring needs and biodiversity declines, we reviewed the urgently needed, ongoing advances in developing effective, noninvasive, and innovative monitoring methods and systems to examine the complex marine environment for better strategic conservation and protection, using the coral ecosystem as one of the representative forefront examples in marine protection. This review summarizes current trends and efforts in transitioning into more standardizable and automatable utilizations of environmental DNA metabarcoding-based monitoring strategies and high-resolution underwater optical imaging monitoring systems as two of the promising pillars for the next generation of noninvasive biomonitoring and associated applications. The assistance of artificial intelligence for environmental DNA metabarcoding and high-resolution underwater optical imaging into an empowered, all-rounded monitoring platform for enhanced monitoring capacity is discussed as a highly potent direction for future research exploration. This review will be a cornerstone reference for the future development of artificial intelligence-assisted, noninvasive, and innovative marine environmental monitoring systems.