Real-time Monitoring Data Research Articles

Real-Time Industrial Data Analysis and Visualization

In the era of Industry 4.0, real-time data monitoring and analysis have become essential for enhancing operational efficiency and decision-making in industrial environments. This project focuses on the development of a comprehensive system for visualizing and analyzing real-time industrial data using a DHT11 sensor, which measures temperature and humidity. The collected data is transmitted and visualized through a dynamic web interface built on MyPHPAdmin, enabling users to access and interpret data seamlessly. The visualization aspect of the project aims to present the data in an intuitive format, allowing stakeholders to monitor environmental conditions effectively. Following the visualization phase, the data will undergo thorough analysis using advanced tools like Tableau and Excel, providing insights into patterns, trends, and anomalies in the industrial setting. This project not only demonstrates the integration of hardware and software technologies for real-time data management but also emphasizes the importance of data visualization and analysis in optimizing industrial processes. Ultimately, it serves as a foundation for further research and development in the field of industrial automation and data-driven decision-making.

A framework for data-driven decision making in advanced manufacturing systems: Development and implementation

Integration of sophisticated technologies such as Internet of Things, cyber physical systems and big data analytics have revolutionized the advanced manufacturing systems (AMS). However, implementation of data-driven decision making in AMS still remains challenging due to data heterogeneity, real-time processing demands, and integration complexities. This paper overcomes this challenge by presenting a novel framework for adoption of DDDM in AMS to enhance its decision-making capabilities. This framework consists of six stages: manufacturing stage, sensing stage, data stage, knowledge stage, decision stage, and application stage. The proposed framework leverages big data analytics to extract actionable insights from diverse datasets, integrates CPS to create a seamless interaction between physical and digital systems, and employs IoT technologies for real-time data acquisition and monitoring. The framework is validated through a comprehensive case study involving a CNC milling machine dataset, demonstrating significant improvements in operational efficiency, decision accuracy, and response time. The case study involves detailed data collection steps, preprocessing, and analysis, showcasing the framework’s practical implementation and effectiveness. The results show that the proposed framework addresses existing challenges and provides a scalable solution for DDDM implementation in AMS.

Comprehensive evaluation of solar floating photovoltaic prospective in Saudi Arabia: Comparative experimental investigation and thermal performance analysis

Recently, floating photovoltaic systems have been regarded as a promising technology for producing clean energy by utilizing the surface of water bodies, such as lakes, rivers and oceans. This study introduces a comparative experimental study and energy performance evaluation of a 1.0 kW offshore floating photovoltaic (FPV) system and a nearby traditional ground-based PV system (GPV) installed in the eastern province of Saudi Arabia. The FPV system was deployed in the Arabian Gulf, 25 m off the coast, at an average depth of 1 to 1.5 m depending on tide, wave, and current intensity. The FPV system employs a strong novel eco-friendly platform structure made of recycled buoyant materials such as engineered plastic drums and wood. This system is anchored with tension cables and concrete blocks that can withstand the changing sea water conditions. The GPV system, on the other hand, was installed 150 m inland from the shore. Real-time monthly data monitoring and assessment indicated that FPV systems outperformed GPV systems in terms of lower PV panel surface temperatures, higher power output, and panel efficiency. Based on daily average back surface temperatures, FPV system temperature was decreased by 7.5 % to 21.34 % when compared to GPV. Moreover, the FPV system efficiency was also increased by 12.2 % when compared to the GPV system. This study aims to assist in promoting the applicability of solar floating photovoltaic systems to synergistically fulfill the requirements of sustainable electricity production for the arid costal community in Saudi Arabia and similar areas.

IOT-Enabled Smart Healthcare: Developing Predictive Models for Chronic Disease Management Using Wearable Sensors

Abstract: Real-time monitoring and prediction models will be used to assess the efficiency of the IoT-enabled smart healthcare solutions in managing chronic diseases. From this study, in addition to the quantitative data, including the performance of the model, patient engagement, and cost savings, qualitative information obtained from the patients and healthcare professionals will be employed using a mixed-methods research approach. This collected rich data from surveys, performance measurements, real-time monitoring data, and cost analyses with 50 health professionals and 200 chronic disease patients. The results of the study indicate that predictive models, more particularly the Neural Network, enhanced the ability to identify cases of chronic illness with an accuracy of 92.4%. Extremely high patient satisfaction rates: 9.0 out of 10. Real-time monitoring shows hospital admissions falling by as much as 50% among those suffering with respiratory disease. Cost-benefit analysis also documented financial sustainability, with overall annual cost reduction at 43.3%. Soon after implementation, usage metrics also revealed that the average user uses it three times a day, while patient-provider interactions increased by 150%. This study highlights how IoT technologies would benefit patients' clinical conditions, improve chronic disease management, and save healthcare expenses.

Societal Adaptation to Pandemic Challenges: Analysing the Role of Local Governance and Healthcare Systems in Madhya Pradesh

The COVID-19 pandemic posed unprecedented challenges, compelling communities globally to adapt rapidly to protect lives and maintain societal functions. This study examines the role of local governance and healthcare systems in Madhya Pradesh in addressing these challenges and fostering societal resilience. Drawing on quantitative data from government health agencies and qualitative insights from community feedback, this research explores how local governance structures facilitated public health response, resource allocation, and pandemic preparedness. Findings indicate that decentralized decision-making and robust healthcare infrastructure were instrumental in managing health crises, providing essential services, and supporting economic resilience at the grassroots level. Key results show that regions with stronger local governance exhibited a 30% lower infection rate compared to less governed areas, while areas with well-resourced healthcare systems saw a 20% higher recovery rate. Resource distribution, however, varied significantly, with urban areas receiving 60% more medical supplies than rural regions, creating disparities in healthcare access. Data analysis revealed that healthcare facilities that integrated digital solutions, such as telemedicine, were able to serve 45% more patients remotely, mitigating the risk of virus spread and reducing hospital overload. The study identifies the need for capacity-building initiatives, infrastructure strengthening, and data-driven decision-making to enhance future pandemic preparedness. Future prospects include continuous training programs for healthcare workers, increasing community awareness of health protocols, and leveraging technology for real-time data monitoring. These insights contribute to a comprehensive framework for building resilient healthcare systems in Madhya Pradesh, better prepared for future public health crises.

Use of Nanotechnology in Cosmetics and Cosmeceuticals

Nanotechnology has revolutionized the cosmetic and cosmeceutical industries by enhancing product efficacy and enabling innovative formulations. This review paper delves into the various applications of nanotechnology in skincare and cosmetic products, highlighting its ability to improve the delivery of active ingredients, provide enhanced protection, and increase the bioavailability of beneficial compounds. Nanomaterials such as liposomes, nanoemulsions, nanocapsules, and solid lipid nanoparticles are now extensively utilized for their superior skin penetration capabilities, ensuring targeted delivery and prolonged effects. The paper explores the potential benefits and safety concerns associated with these nano-based cosmetic products. It also provides a comprehensive analysis of recent advancements, regulatory considerations, and future trends in the field, emphasizing the need for further research to fully understand the long-term implications of nanotechnology in cosmetics and cosmeceuticals. This review aims to provide a critical overview for researchers and industry professionals interested in the evolving landscape of nanotechnology in beauty and skincare. Findings Our research highlights the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on the drug discovery and development process, particularly in enhancing efficiency and precision from initial screening to clinical trials. The integration of AI/ML has shown significant advancements in early-stage drug discovery, where data-driven algorithms enable rapid identification of potential drug candidates, reducing reliance on traditional, labor-intensive methods. In the drug design and optimization phase, AI-driven predictive models have streamlined the process, minimizing the need for extensive physical testing by accurately simulating drug interactions and predicting possible side effects. Additionally, AI and ML are revolutionizing clinical trials by optimizing trial design, improving patient recruitment and retention, and enhancing real-time data monitoring, leading to faster and more reliable trial outcomes. These technologies also support personalized medicine approaches and have proven essential in reducing both the time and cost associated with bringing new therapies to market. Overall, our findings underscore the critical role of AI and ML in reshaping the pharmaceutical landscape, making drug development faster, more cost-effective, and ultimately, more successful in delivering effective treatments to patients.

Profiles of peripheral regulatory T cells forecasts the feasibility of steroid withdrawal after liver transplantation based on cellular thermal modeling

After liver transplantation, steroid therapy is often used to prevent rejection. However, long-term steroid use can lead to serious side effects, therefore, this study aimed to evaluate the feasibility of peripheral regulatory T cell profiles for steroid discontinuation after liver transplantation through cellular thermal modeling and real-time monitoring of intracellular thermodynamics. In this study, cellular thermal modeling techniques were used to simulate the thermodynamic characteristics of peripheral TREgs under different conditions. The dynamic changes of peripheral Treg in liver transplantation animal models were monitored by flow cytometry and molecular biology. Significant changes in peripheral Treg profiles were observed after initiation of steroid therapy, especially in the discontinuation group, and these changes were strongly associated with the restoration of immune homeostasis. Real-time monitoring of the thermodynamic data revealed that peripheral Treg activity showed a specific temperature dependence in the cellular thermal model. Cell thermal modeling combined with real-time monitoring of intracellular thermodynamics provides a new perspective for evaluating the feasibility of peripheral regulatory T cell profiles for steroid discontinuation after liver transplantation. Strengthening the dynamic monitoring of peripheral Treg spectrum provides an important basis for the formulation of personalized immunosuppression strategies, and improves the therapeutic effect and quality of life of liver transplant patients.

Machine Learning Algorithms Based on Time Series Pre-Clustering for Nocturnal Glucose Prediction in People with Type 1 Diabetes

Background: Machine learning offers new options for glucose prediction and real-time glucose management. The aim of this study was to develop a machine learning-based algorithm that takes into account glucose dynamics patterns for predicting nocturnal glucose in individuals with type 1 diabetes. Methods: To identify glucose patterns, we applied a hierarchical clustering algorithm to real-time continuous glucose monitoring data obtained from 570 adult patients. Machine learning algorithms with or without pre-clustering were used for modeling. Results: Eight clusters without nocturnal hypoglycemia and six clusters with at least one low-glucose episode were identified by the cluster analysis. When forecasting time series without hypoglycemia with a prediction horizon (PH) of 15 or 30 min, gradient boosting trees (GBTs) with pre-clustering and random forest (RF) with pre-clustering outperformed algorithms based on medoids of time series clusters, the Holt model, and GBTs without pre-clustering. When forecasting time series with low-glucose episodes, a model based on the pre-clustering and GBTs provided the highest predictive accuracy at PH = 15 min, and a model based on RF with pre-clustering was the best at PH = 30 min. Conclusions: The results indicate that the clustering of glucose dynamics can enhance the efficacy of machine learning algorithms used for glucose prediction.

Strengths and weaknesses of computerized clinical decision support systems: insights from a digital control center (C3 COVID-19) for early and personalized treatment for COVID-19.

Clinical Decision Support Systems (CDSS) are computer-based tools that leverage the analysis of large volumes of health data to assist healthcare professionals in making clinical decisions, whether preventive, diagnostic, or therapeutic. This review examines the impact of CDSS on clinical practice, highlighting both their potential benefits and their limitations and challenges. We detail the experience of clinical medical professionals in the development of a virtual control center for COVID-19 patients (C3 COVID-19) in Spain during the SARS-CoV-2 pandemic. This tool enabled real-time monitoring of clinical data for hospitalized COVID-19 patients, optimizing personalized and informed medical decision-making. CDSS can offer significant advantages, such as improving the quality of inpatient care, promoting evidence-based clinical and therapeutic decision-making, facilitating treatment personalization, and enhancing healthcare system efficiency and productivity. However, the implementation of CDSS presents challenges, including the need for physicians to become familiar with the systems and software, and the necessity for ongoing updates and technical support of the systems.

IoT based smart emergency response system (SERS) for monitoring vehicle, home and health status

This paper presents an innovative Internet of Things (IoT)-based smart emergency response system that aims to improve safety and efficiency across vehicles, homes and healthcare settings. The proposed system combines advanced sensors, communication technologies, and intelligent algorithms to provide a comprehensive solution for real-time data monitoring and emergency management. Here, the vehicle device is equipped with temperature, gas, flame, GPS, piezo, and accelerometer sensor and the system can detect accidents, hazardous conditions, precise location automatically notifying emergency services. The home devices incorporate Flame Sensor, humidity sensor, DHT11, MQ-2 gas sensor, MQ-4 Gas Sensor, MQ-135 gas sensor, and intrusion detection to ensure immediate alerts and response to potential security threats. Lastly the health device is equipped with temperature, humidity, stress levels, heart rate, oxygen saturation, and fetal heart rate sensor which can monitor vital signs and detect anomalies, triggering alerts for medical emergencies and facilitating timely intervention. The novelty of the proposed study is it incorporates the three devices, processes the data and automatically notifies the emergency responders for further emergency assistance. Moreover, the system utilizes a cloud-based platform to collect, analyze, and disseminate data, enabling seamless coordination between emergency responders such as ambulance for health and accident emergency, fire brigade for fire emergency and affected individuals. We have also developed a MySQL-PHP-Laravel based live dashboard and emergency response system where both the user and relevant stakeholder will have the access which could mitigate the emergency situation in the quickest amount of time without human intervention. The findings of our study reveals that incorporation of IoT and smart infrastructure offers significant improvement of server response time 3 ms, accuracy > 99% and overall effectiveness of emergency management. This integrated approach not only enhances individual safety but also contributes to broader public safety goals, showcasing the potential of IoT technologies in creating smarter, safer environments.

Kinetic analysis of the self-discharge of the NiOOH OER active phase in KOH electrolyte: insights from in-situ Raman and UV–Vis reflectance spectroscopies

NiOOH has been established as the active phase of NiO-based electrocatalysts in the alkaline Oxygen Evolution Reaction (OER). Here, we investigate the self-discharge behavior of NiOOH electrodes under open circuit potential (OCP) conditions in 1 M KOH electrolyte by monitoring phase changes via in-situ Raman and UV–Vis reflectance spectroscopies and performing kinetic analyses on the OCP and spectroscopic data. Our findings reveal a linear phase change from NiOOH to Ni(OH)2 over time, indicative of a 0th-order reduction reaction. Contrarily, the OCP evolution associated with this phase reduction displayed a combination of linear and exponential decay patterns as a result of various kinetics, including Faradaic processes and diffusion-controlled mechanisms, influencing the self-discharge potential over 1.25 V (vs RHE). An additional linear region at lower potentials (<1.25 V (vs RHE)) suggests that charge redistribution due to the phase change from α-Ni(OH)2 to β-Ni(OH)2 dominates the self-discharge, a behavior confirmed by in-situ UV–Vis reflectance spectroscopy. These findings highlight the effectiveness of combining in-situ Raman and UV–Vis spectroscopy with electrochemical data for real-time monitoring of electrochemical processes, here potential-dependent electrocatalyst phase changes, leading to a more detailed and accurate understanding of the dynamic behavior, phase change kinetics, and self-discharge behaviors of solid electrocatalysts that can guide the design of more efficient and durable energy storage and conversion materials.

Building personalized machine learning models using real-time monitoring data to predict idiographic suicidal thoughts

Building personalized machine learning models using real-time monitoring data to predict idiographic suicidal thoughts

IoT and Edge Computing for Smart Manufacturing: Architecture and Future Trends

The integration of the Internet of Things (IoT) and Edge Computing is revolutionizing the manufacturing industry, ushering in the era of smart manufacturing as part of Industry 4.0. This paper explores the synergy between IoT and Edge Computing, focusing on their combined architecture and the future trends driving innovation in smart factories. IoT enables the connection and communication of machines, sensors, and systems, allowing for real-time data collection and monitoring. However, traditional cloud-based approaches face challenges such as latency, bandwidth limitations, and security risks, which can hinder real-time decision-making in fast-paced manufacturing environments. Edge Computing addresses these issues by processing data closer to the source, minimizing latency and reducing dependence on cloud infrastructures. By combining IoT and edge solutions, smart manufacturing systems can make faster, data-driven decisions, leading to improved efficiency, reliability, and operational flexibility. This paper delves into the architectural design of IoT and edge computing in manufacturing, outlining how data flows from IoT devices to edge nodes and cloud services. Several real-world use cases and industry examples are analyzed to highlight the practical benefits of these technologies. Additionally, this research identifies key challenges such as security vulnerabilities, the need for robust network infrastructures (e.g., 5G), and issues related to data standardization. The future of smart manufacturing is also explored, emphasizing trends like the adoption of artificial intelligence (AI) and machine learning (ML) at the edge, digital twins for real-time monitoring, and the role of IoT and edge computing in fostering sustainability through energy-efficient production processes.This study provides a comprehensive overview of IoT and edge computing architectures in smart manufacturing and offers insights into future technological trends that will shape the industry.

Leveraging IoT Technology for Transformative Impact in the Maritime Sector

The maritime sector is undergoing a transformative shift driven by the adoption of the Internet of Things (IoT) era. IoT permits real-time data collection, monitoring, and evaluation of maritime assets, leading to improved operational efficiency, stepped forward protection, and value financial savings. By integrating IoT sensors into ships, ports, and cargo, stakeholders can optimize gas intake, control vessel maintenance, tune shipment movements, and ensure compliance with environmental regulations. Furthermore, IoT enables predictive protection, decreasing downtime and extending gadget lifespan. The usage of IoT in maritime logistics also promotes transparency within the supply chain, assisting in more specific direction-making plans and reducing delays. This summary explores the key packages of IoT inside the maritime region, addressing its ability to revolutionize transport operations, enhance sustainability, and power innovation across the enterprise. Via manner of its very nature, the delivery and logistics organization desires an in-depth and powerful statistical alternative for fulfillment management and choice-making. A specific technique is needed for the marine enterprise due to the complicated and various environments. The naval enterprise's short adoption of IoT generation will make it an awful lot less tough to administer essential services like vessel monitoring, emissions control, preventative protection, protection, and welfare. The IoT answers defined in this article are tailor-made to the business enterprise's present-day needs. The hints are normal with the venture, which intends to offer an interactive remark route on maritime and survey or IoT systems to fill the market need due to the speedy growth of the smart Maritime &amp; Surveying business enterprise. By its very nature, the delivery and logistics company wishes for an in-depth and powerful facts alternative for fulfillment control and selection-making. A specific method is wanted for the marine company due to the complex and diverse environment. The naval enterprise's short adoption of IoT generation will make it much less tough to administer essential offerings like vessel tracking, emissions control, preventative protection, protection, and welfare. The IoT solutions defined in this newsletter are tailored to the organization's current wishes. The tips are regular with the undertaking, which intends to provide an interactive observation course on maritime and survey or IoT structures to fill the market want because of the speedy boom of the smart Maritime &amp; Surveying commercial corporation

A robust adaptive error state Kalman filter for MEMS IMU attitude estimation under dynamic acceleration

Accurate estimation of attitude (pitch, roll) is a prerequisite for ensuring safe navigation during vehicle control execution. In dynamic environments, the presence of acceleration often degrades the accuracy and robustness of attitude estimation using traditional algorithms with Micro-Electro-Mechanical Systems (MEMS) Inertial Measurement Unit (IMU). This paper proposes a robust adaptive error state Kalman filter (RAESKF) algorithm for attitude estimation of MEMS IMU under dynamic acceleration conditions. The RAESKF algorithm decomposes the true attitude Direction Cosine Matrix (DCM) into nominal and error components. An error state Kalman filter is utilized to fuse real-time measurements from MEMS gyroscope and accelerometer, estimating the error component, which is then used to correct the nominal attitude DCM. Furthermore, within the error state Kalman filtering framework, a dynamic acceleration robust adaptive adjustment strategy is implemented. This strategy relies on real-time data monitoring, threshold-based decision-making, and switching mechanisms. It adaptively selects between dynamic acceleration model compensation and online adjustment of the measurement noise covariance matrix. The primary objective of this strategy is to mitigate the impact of disturbance induced by dynamic acceleration on attitude estimation, thereby enhancing the accuracy and robustness. Laboratory rotation experiments have demonstrated that the algorithm improved pitch and roll measurement accuracy by at least 8.81% and 11.69%, respectively, under rotational conditions. In dynamic acceleration conditions, pitch and roll measurement accuracy improved by at least 42.76% and 67.21%, respectively.

A method for measuring carbon emissions from power plants using a CNN-LSTM-Attention model with Bayesian optimization

Accurate measurement of CO2 emissions from coal-fired power plants is crucial for achieving China's “carbon neutrality” goal; however, traditional CO2 emission measurement methods have several limitations, and emerging prediction models rarely utilize real-time monitoring data from power plants at the micro level. To address these challenges, this study presents a novel hybrid convolutional neural network with long short-term memory (CNN-LSTM)-Attention model, leveraging real-time monitoring data to enhance the accuracy of CO2 emission predictions. The proposed model employs a hybrid architecture that leverages the strength of CNN, LSTM, and Attention mechanism. To address the challenges of multiple hyperparameters and difficult selection, the Bayesian optimization algorithm is used to optimize the hyperparameters ensuring optimal performance. Using historical operational data from a 660 MW generator set in Hubei province, the proposed model was evaluated and validated horizontally by using three evaluation metrics. Additionally, the vertical evaluation and validation of the proposed model were done using the operational data from other months. The results indicate that the proposed model achieved the highest performance across every evaluation index for predicting CO2 emissions from coalfired power plants, and the prediction accuracy of the CO2 emission in the future months is also guaranteed.

An IoT Based Real-Time Environmental Monitoring System for Developing Areas

Rapid urbanization and industrialization have raised concerns about environmental quality and sustainability in recent years. The Internet of Things (IoT) has played an important role in monitoring physical phenomena by generating data that can be sent and preserved in the cloud. This work explores an IoT-based environmental monitoring system's potential, using an Arduino-based device for real-time tracking of environmental parameters including sound levels, humidity, dust concentration, total volatile organic compounds (TVOC), carbon dioxide (CO2), and carbon monoxide (CO). Real-time data are collected from various semi-residential and marketplace locations named in Pach raster More, Tomaltola, DowamoyiMore, Fojdarimore, and station road in Jamalpur district of Mymensingh Division, Bangladesh on non-holiday days, providing a representative snapshot of typical environmental conditions. The collected data is stored in a cloud server named firebase database. The implemented monitoring system offers several key features including accuracy and reliability, real-time monitoring data analytics alerts and notifications historical data as well as it can lead to various benefits and impacts of Improved Air and Water Quality Healthier Urban Environment (IAWUE) to enable local authorities and individuals to make educated decisions for a healthier and more sustainable urban environment. Graphical representations of the data revealed distinct patterns and trends, offering valuable insights into air quality variations across different areas. Interestingly, the results showed sound levels slightly below the standard range, indicating a relative control of noise pollution in the sampled areas. The findings of the work will serve as a vital resource for further research and guide policy-making for environmental improvement and sustainable practices in urban settings.

Acquisition of Flight Data from Mini Unmanned Aerial Systems

Swift progress in unmanned aerial vehicle (UAV) system technology mandates effective real-time monitoring and accurate flight data capture for building an optimum target-based cutting-edge UAVs. In the current epoch of modern-UAV era, small size and mission-oriented UAV prototypes are increasingly pervasive. This study highlights the design of a data acquisition target-based mini-UAV and introduces a novel telemetry software solution to safe and reliable communication between mini-UAV and ground control station (GCS). The primary aim is to demystify the proposed wireless communication software solution on mini-UAV capability in obtaining and managing comprehensive flight data. While creating the appropriate target-based mini-UAV prototype architecture, the features that we aim to realize for the UAV components are addressed with a system approach and the component selection is presented in detail. The integration point of the selected components is conveyed in general terms. Thus, a guiding resource for future UAV studies will be presented. Then, the processes of obtaining the flight data of mini-UAVs will be addressed. At this point, the design logic of the required commands and background software will be conveyed. Obtaining the flight data of mini-UAVs will provide important scientific contributions to the literature in terms of aviation research and safety analysis. Obtaining the flight data of mini-UAVs will meet the data set needs for flight safety improvements and air traffic management optimization studies. A general analysis of the flight parameters obtained as a result of obtaining the flight data will be performed, the predictability of the parameters will be evaluated, and the analysis of the flight phases will be performed by presenting the relationships of the parameters according to the flight scenarios and the sections taken from the parameter set in the study. This study can be considered as a concrete example of situational monitoring of UAVs.

Integrated Iot Approaches for Crop Recommendation and Yield-Prediction Using Machine-Learning

In this study, we present an integrated approach utilizing IoT data and machine learning models to enhance precision agriculture. We collected an extensive IoT secondary dataset from an online data repository, including environmental parameters such as temperature, humidity, and soil nutrient levels, from various sensors deployed in agricultural fields. This dataset, consisting of over 1 million data points, provided comprehensive insights into the environmental conditions affecting crop yield. The data were preprocessed and used to develop predictive models for crop yield and recommendations. Our evaluation shows that the LightGBM, Decision Tree, and Random Forest classifiers achieved high accuracy scores of 98.90%, 98.48%, and 99.31%, respectively. The IoT data collection enabled real-time monitoring and accurate data input, significantly improving the models’ performance. These findings demonstrate the potential of combining IoT and machine learning to optimize resource use and improve crop management in smart farming. Future work will focus on expanding the dataset to include more diverse environmental factors and exploring the integration of advanced deep learning techniques for even more accurate predictions.

Advanced Risk Assessment for Deep Excavation in Karst Regions Using Improved Dempster–Shafer and Dynamic Bayesian Networks

This study presents a novel risk-assessment methodology for deep foundation pit projects in karst regions, aimed at enhancing project safety and decision-making processes. This approach amalgamates fuzzy dynamic Bayesian networks with a refined Dempster–Shafer (DS) evidence theory to tackle the intricate uncertainties present in such contexts. A comprehensive risk index system, derived from historical accident cases, relevant standards, and the literature, encompasses environmental, design, construction, and management factors. Initial probabilities for each risk factor are determined through the integration of expert knowledge and fuzzy theory. The enhanced Dempster–Shafer theory is utilized to fuse diverse information sources, culminating in a robust and dynamic risk evaluation model. This model leverages real-time monitoring data to dynamically assess and adjust risk levels throughout the construction process. The validation of the proposed method is demonstrated through a detailed case study of the Guangzhou Tangxi Section 1 deep foundation pit project, which effectively identified critical risk factors and facilitated proactive construction strategy adjustments. To further evaluate the reliability of the methodology, comparisons were made with three alternative methods, and applications were conducted on three additional deep foundation pit projects. These comparative analyses confirm the superior reliability and applicability of the proposed methodology across varied scenarios.