Efficient Information Fusion from Environmental Sensors for Near Real-Time IoT Analytics

Machine learning for the intelligent analysis of 3D printing conditions using environmental sensor data to support quality assurance

Objective To preliminarily analyze the prediction efficiency of multimodal MRI-based radiomics model for preoperative glioma IDH1 gene expression type. Methods The MRI data of 108 surgery-proven glioma patients from May 2015 to January 2019 were retrospectively analyzed, and the MRI data included axial T1WI,T2WI,fluid attenuated inversion recovery (FLAIR),DWI imaging and enhanced T1WI sequence.Forty-seven cases were IDH1 mutant type, and 61 cases were IDH1 wild type. All patients were divided into training and validation groups according to the 7∶3 ratio of the random forest model. Seventy-three cases were in training group, and 35 cases were in validation group. Independent predictors of IDH1 mutation were screened by univariate analysis combined with multivariate logistic regression analysis (P<0.05) in order to construct a random forest diagnosis model of general clinical information and conventional MRI morphological features.General clinical information and conventional MRI morphological features included gender, age, umbers of cases of left and right hemispheres, location of tumors, maximum diameter of tumors, peritumoral edema, intratumoral cystic degeneration, enhancement and ADC value of tumors. The ROI was manually outlined by MaZda software in the most obvious level of 5 sequences of tumor mass and the radiomics features were extracted, including the gray-level co-occurrence matrix(GLCM), the run-length matrix(RUN), the absolute gradient(GRA),the auto-regressive model(ARM) and wavelets transform(WAV). The least absolute shrinkage and selection operator (LASSO)regression were used to select image radiomics features with a method of 10 fold cross -validation and to reduce the dimensions. The screened image radiomics labels were combined with the conventional morphological feature independent predictors to construct a multimodal MRI-based random forest model, and the validation data set was used to evaluate the accuracy and diagnostic efficiency of each model. The sensitivity and specificity of conventional MRI morphological feature model and multimodal MRI-based radiomics prediction model were evaluated dynamically by drawing ROC curves, and the prediction efficiency of the two models was quantified by using AUC statistical indicators. The model classification error rate under different outcomes and the classification error rate of out of bag(OOB)were used to evaluate the stability of the multimodal MRI-based random forest model. The contribution rate of each variable to the model was reflected by the characteristic variables importance assessment map. Results Univariate regression analysis of the conventional MRI morphological characteristics showed that peritumoral edema, cystic degeneration and enhancement were the three independent predictors of IDH1 gene expression (P<0.01). LASSO algorithm and 10-fold cross-validation identified six robust radiomic features including high frequency coefficients of wavelet transform (WavEnHH_s-4) of T2WI, S(4,4) inverse difference of gray uniformity measurement (InvDfMom),S(5,0) Entropy (entropy),WavEnHH_s-4 of T1WI enhancement, S(1,1) InvDfMom,S(1, -1) Entropy Difference (DifEntrp)of Flair.The error rate of classification for different outcomes and classification error rate of random forest OOB data of multimodal MRI radiomics diagnosis model finally stabilized at 10%. The results of Characteristic Variable Importance Assessment Map: Mean Decrease Accuracy and Mean Decrease Gini index were consistent, which showed that besides three conventional MRI morphological predictors peritumoral edema, enhancement and cystic degeneration, the radiomics labels also played a key role in the model. The results of ROC curve showed that the accuracy, specificity, sensitivity and AUC of conventional MRI morphological feature model were 82.7%, 68.4%, 90.9% and 0.835, respectively, and those of multimodal MRI-based radiomics model were 88.5%, 89.5%, 87.8% and 0.956 respectively. Conclusion Multimodal MRI-based radiomics random forest model can improve the predictive efficiency of preoperative glioma IDH1 gene expression type more quantitatively. Key words: Glioma; Texture; Radiomics; Magnetic resonance imaging

A fusion framework for occupancy estimation in office buildings based on environmental sensor data

We designed and evaluated an assumption-free, deep learning-based methodology for animal health monitoring, specifically for the early detection of respiratory disease in growing pigs based on environmental sensor data. Two recurrent neural networks (RNNs), each comprising gated recurrent units (GRUs), were used to create an autoencoder (GRU-AE) into which environmental data, collected from a variety of sensors, was processed to detect anomalies. An autoencoder is a type of network trained to reconstruct the patterns it is fed as input. By training the GRU-AE using environmental data that did not lead to an occurrence of respiratory disease, data that did not fit the pattern of “healthy environmental data” had a greater reconstruction error. All reconstruction errors were labelled as either normal or anomalous using threshold-based anomaly detection optimised with particle swarm optimisation (PSO), from which alerts are raised. The results from the GRU-AE method outperformed state-of-the-art techniques, raising alerts when such predictions deviated from the actual observations. The results show that a change in the environment can result in occurrences of pigs showing symptoms of respiratory disease within 1–7 days, meaning that there is a period of time during which their keepers can act to mitigate the negative effect of respiratory diseases, such as porcine reproductive and respiratory syndrome (PRRS), a common and destructive disease endemic in pigs.

This study proposes a new paradigm for real-time, predictive, and multidimensional risk assessment in industrial environments by leveraging multimodal data (video, audio, environmental sensors). Existing risk assessment systems typically rely on single data sources or subjective judgment, making it difficult to adapt swiftly to complex workplace changes and achieve real-time responsiveness. To address these limitations, we constructed a large-scale multimodal dataset of approximately 10TB, employing real-time streaming-based preprocessing and data synchronization. This approach integrates various data sources—high-resolution cameras, high-sensitivity microphones, and environmental sensors (temperature, humidity, vibration)—and applies data augmentation techniques such as AutoAugment and MixUp to build robust models capable of handling diverse environmental conditions. We adopted a hybrid analytical algorithm combining Vision Transformer (ViT) and YOLOv8, achieving high accuracy (over 95%) and real-time processing (average response time under one second). Additionally, we utilized machine learning algorithms such as SVM, Random Forest, and K-Means to detect anomalies in audio and environmental sensor data, thus identifying latent risk factors. Experimental results demonstrate multifaceted performance improvements compared to conventional approaches, including over a 15% increase in accuracy, approximately 30% reduction in response time, about 20% reduction in power consumption, and user satisfaction exceeding 90%. These achievements were verified across various industrial settings—chemical, manufacturing, logistics—highlighting the system’s capacity to detect complex risk factors and respond proactively. By seamlessly integrating multimodal data analysis, state-of-the-art deep learning models (ViT, YOLOv8), and reinforcement learning-based response strategies, we have demonstrated a transition from traditional, static, and retrospective risk assessment to an intelligent, real-time, and predictive safety management framework.

Testing a Landsat-based approach for mapping disturbance causality in U.S. forests

Room occupancy detection with reasonable accuracy is indispensable for developing innovative building systems that provide energy-efficient management, increased security, and greater comfort. The existing occupancy detection solutions based on traditional sensors suffer from high installation costs, a lack of scalability, and the inability to adapt to dynamic environments. This study proposes an optimized machine learning (ML) approach using a Neural Network (NN) model tailored with a Puma Optimizer Sine Cosine Optimizer (POSC) metaheuristic optimization technique to address these challenges. Based on environmental sensor data, such as temperature, humidity, light intensity, and hbox {CO}_2 levels, the proposed model achieves high accuracy in predicting room occupancy. The optimization process helps reinforce the training of the NN model through a dynamic equilibrium between exploration and exploitation, achieving faster convergence speed and better classification. The model is evaluated and compared on a publicly available dataset with other optimization techniques such as the Genetic Algorithm (GA) and Grey Wolf Optimization (GWO). Experimental results prove that the POSC-optimized NN model achieves superior classification and significantly outperforms conventional ML methods in terms of accuracy, precision, recall, and F1-score. These findings suggest that the combined use of metaheuristic optimization and deep learning can be a practical approach for real-world applications in intelligent building automation. The solutions proposed in this research may contribute to the growing field of intelligent occupancy detection and energy-efficient systems for future smart environments.

A design of novel wireless environment parameter measuring instrument based on solar energy is introduced in this paper.The measurement instrument uses solar self-powered technology,can achieve the rapid detection and display of particulate matter, light intensity, temperature, humidity,noise and other environmental parameters,and uses wireless networking and PC terminal to form a network.The measuring instrument also has a control output function,the control output can be set according to the parameters, to meet the needs of different applications. The hardware and software development of the instrument were realized.And through the experiments showed that the designed measuring instrument can quickly and accurately detect a variety of environmental parameters, full-featured, cost-effective, wide, and has broad application prospects.

The development of the Internet of Things (IoT) creates a multitude of new Real Time Analytics (RTA) application possibilities, also in the area of industry. However, already the topic of big data forced the use of analytical solutions which were processed in real time. In order to consider the possible applications of RTA, it is necessary to gather the current state of them. Thereby, the term RTA is not separable with regard to the other concepts, rather it may be viewed as the application of those analytics concepts, based on current data. The application of analytics in real time demands a clarification about analytics itself to gain an understanding of possible application scenarios. This paper introduces the term RTA and depicts related concepts.

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v2]Hypnos Board: A Low-Cost All-In-One Solution for Environment Sensor Power Management, Data Storage, and Task SchedulingAuthorsBrysonGotoiDBaoNguyeniDJohnSelkeriDChetUdelliDSee all authors Bryson GotoiDCorresponding Author• Submitting AuthorOregon State UniversityiDhttps://orcid.org/0000-0002-9224-3290view email addressThe email was not providedcopy email addressBao NguyeniDOregon State UniversityiDhttps://orcid.org/0000-0002-5692-3176view email addressThe email was not providedcopy email addressJohn SelkeriDOregon State UniversityiDhttps://orcid.org/0000-0001-9751-6094view email addressThe email was not providedcopy email addressChet UdelliDOregon State UniversityiDhttps://orcid.org/0000-0001-5815-8319view email addressThe email was not providedcopy email address

Data fusion of laser induced breakdown spectroscopy (LIBS) and infrared spectroscopy (IR) coupled with random forest (RF) for the classification and discrimination of compound salvia miltiorrhiza

This paper describes the design and implementation of a rail-guided mobile robot-based monitoring system that can be used for autonomous monitoring of the facilities and rapid response to the on-site event. It is necessary to improve the safety and efficiency of the monitoring system when applying a robot system, and the implementation of intelligent functions can increase the level of automation to facilitate operation. In order to increase driving safety and lower the level of control, a two-track rail structure of circular pipes, U-shaped wheels, and a differential gear are used to develop a driving system that enables rotational path driving even with forward and backward movement. For the proper monitoring of facility conditions, high-temperature occurrence, and environmental quality, cameras (RGB and thermal imaging) and air quality sensors are chosen by considering the range of the monitoring site and target subjects. Data communication between the robot and the remote control room(a remote server) is built up to patrol in a planned schedule and deliver acquired video streams and environmental sensor data. Emergency alarms about high temperatures and the abnormality of air conditions can be generated by processing the transferred data. Additionally, deep network-based detection services on video data are implemented on the server for the detection of emergency situations; fall downs. Integrating the developed driving system, image/sensor data communication, and alarm function through data analysis in an operating framework reduces management work through automatic patrol and enables efficient on-site response when an alarm occurs. The validity of the proposed system is demonstrated by building and operating the system in the existing multi-use facility. The automatic patrol function and the manual operation function are performed through a test operation similar to the facility monitoring task of a human operator. The operator can control the robot to move to a desired position along the path, and by manipulating the camera module, the operator can check the on-site situation via a video stream The environmental sensor data is also transferred to the server and stored during operation, and the robot normally completes the automatic patrol task and returns to the charging station. In a simulation experiment to verify the detection alarm service, the test shows that a human falling down is detected even in the field.

Existing tomato datasets often focus on short-term experiments or lack integrated environmental and agronomic data. We present Horti-M3-Tomato, a comprehensive three-year dataset collected in Northeast China’s greenhouse, including high-resolution RGB images, environmental sensor data (recorded every 30 minutes), soil conditions, and detailed agronomic records such as yield data and management practices. Spanning three growing seasons (2023–2025), the dataset integrates temporal imaging, environmental monitoring, soil data, and manual phenotypic and yield records. Horti-M3-Tomato supports research on growth dynamics, genotype-environment interactions, and provides a benchmark for AI-based phenotyping and precision horticulture. The dataset is openly available for further research in controlled-environment agriculture.

Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection

The molecular mapping of atom-level properties (MOLMAP) descriptor was generated on the basis of chemical bond descriptors of a molecule by Kohonen self-organizing map with a specific algorithm. The bond descriptors were composed of the physiochemical properties of the chemical bond, such as the difference of the charges between the two atoms and topological properties, such as the number of hetero-atoms connected to the two atoms. In this paper, the MOLMAP descriptors were used to predict the mutagenicity of 4075 organic substances (2305 mutagens and 1770 nonmutagens in Ames test). Random forests were used to construct mathematical models with three kinds of descriptors: (1) MOLMAP descriptors of different size; (2) global molecular descriptors; (3) the combination of MOLMAP descriptors and global molecular descriptors. The correct prediction percentage of out of bag (OOB) cross-validation of the whole data set reached 85.4%. To test the stability of the prediction model, it was used to predict the properties of a test set that was composed of 472 compounds collected from another database. The percentage of correct prediction of the test set was 86.7%. The prediction results were improved compared with the results of previous work.