Accuracy Of Model Research Articles

Decision model of public opinion risk in campus social network based on hybrid dynamic deletion and shortest path algorithm

Aiming at the problem that traditional network public opinion monitoring and searching are inefficient and can easily cause resource waste, the study firstly, through the dynamic deletion-shortest path algorithm to classify network text, and on this basis, innovatively constructs a text sentiment classification model based on the variant of convolutional neural network and recurrent neural network, and secondly, uses attention mechanism to classify the model. improvement of the classification model by using the attention mechanism. The research results show that the average precision rate, recall rate, and F-value of the dynamic deletion-shortest path algorithm are 97.30%, 79.55%, and 87.53%, and the classification speed is 397 KB/s, which is better than the traditional shortest path algorithm. In the classification effect measurement of long text, the accuracy and F-value of the recurrent neural network variant model are above 84%, and the accuracy of the text sentiment classification model with the introduction of the attention mechanism is improved by 3.89% compared to the pre-improvement period. In summary, the dynamic deletion-shortest path algorithm proposed in the study and the sentiment classification model with the introduction of the attention mechanism have superior performance and can provide certain application value for campus social network opinion risk decision-making.

The Impact of Storm Sewer Network Simplification and Rainfall Runoff Methods on Urban Flood Analysis

Due to the impact of climate change, the importance of urban flood analysis is increasing. One of the biggest challenges in urban flood simulations is the complexity of storm sewer networks, which significantly affects both computational time and accuracy. This study aimed to analyze and evaluate the impact of sewer network simplification on the accuracy and computational performance of urban flood prediction by comparing different rainfall runoff methods. Using the hyper-connected solution for urban flood (HC-SURF) model, two rainfall runoff methods, the SWMM Runoff method and the Surface Runoff method, were compared. The sewer network simplification was applied based on manhole catchment areas ranging from 10 m2 to 10,000 m2. The analysis showed that the computation time could be reduced by up to 54.5% through simplification, though some accuracy loss may occur depending on the chosen runoff method. Overall, both methods produced excellent results in terms of mass balance, but the SWMM Runoff method minimized the reduction in analytical performance due to simplification. This study provides important insights into balancing computational efficiency and model accuracy in urban flood analysis.

An Intelligent Kick Detection Model for Large-Hole Ultra-Deep Wells in the Sichuan Basin

The Sichuan Basin has abundant deep and ultra-deep natural gas resources, making it a primary target for exploration and the development of China’s oil and gas industry. However, during the drilling of ultra-deep wells in the Sichuan Basin, complex geological conditions frequently lead to gas kicks, posing significant challenges to well control and safety. Compared to traditional kick detection methods, artificial intelligence technology can improve the accuracy and timeliness of kick detection. However, there are limited real-world kick data available from drilling operations, and the datasets are extremely imbalanced, making it difficult to train intelligent models with sufficient accuracy and generalization capabilities. To address this issue, this paper proposes a kick data augmentation method based on a time-series generative adversarial network (TimeGAN). This method generates synthetic kick samples from real datasets and then employs a long short-term memory (LSTM) neural network to extract multivariate time-series features of surface drilling parameters. A multilayer perceptron (MLP) network is used for data classification tasks, constructing an intelligent kick detection model. Using real drilling data from ultra-deep wells in the SY block of the Sichuan Basin, the effects of k-fold cross-validation, data dimensionality, various imbalanced data handling techniques, and the sample imbalance ratio on the model’s kick detection performance are analyzed. Ablation experiments are also conducted to assess the contribution of each module in identifying kick. The results show that TimeGAN outperforms other imbalanced data handling techniques. The accuracy, recall, precision, and F1-score of the kick identification model are highest when the sample imbalance ratio is at 1 but decrease as the imbalance ratio increases. This indicates that maintaining a balance between positive and negative samples is essential for training a reliable intelligent kick detection model. The trained model is applied during the drilling of seven ultra-deep wells in Sichuan, demonstrating its effectiveness and accuracy in real-world kick detection.

Fabric defect detection method based on multi-scale fusion attention mechanisms

Abstract Fabric defect detection is extremely important for the development of the textile industry, but the existing traditional image processing algorithms are not good enough to detect fabric defects, and the detection efficiency and accuracy of the classical deep learning model is not satisfactory, so this paper proposes an improved fabric defect detection method based on multi-scale fusion of attention mechanism YOLOv7-PCBS. Based on the YOLOv7 network structure, some of the standard convolutions of the backbone network are replaced with Partial Convolution modules, which reduces the amount of network computation and improves the network detection speed; add coordinate attention to enhance the ability of extracting the positional features of tiny defects in fabrics; reconfiguration of the SPPCSPC module to improve small target detection; optimization of Bidirectional Feature Pyramid Network (BiFPN) and design of Tiny- BiFPN for simple and fast multi-scale feature fusion; finally, a novel loss function SIoU with angular loss is introduced to facilitate the fitting of the true and predicted frames and enhance the accuracy of defect prediction. The results show that the algorithm achieves a mAP value of 94.4% on the detection of defects in solid-colored fabrics of six denim materials, which is an improvement of 15.1% compared to the original YOLOv7 algorithm, while the model achieves a frame rate of 59.5 per second. Compared with other traditional deep learning algorithms SSD and Faster-RCNN, the detection accuracies are improved by 21.6% and 15.2%, and the FPS values are improved by 78.1% and 101.0%, respectively. Therefore, the YOLOv7-PCBS fabric defect detection algorithm proposed in this paper makes the fabric defect detection results more accurate while realizing lightweight, which provides an important technical reference for the subsequent improvement of textile quality.

Level-Set Method Based Predictive Modeling to Track the Evolution of Surface During Pulsed Laser Surface Melting

Abstract The objective of this study is to investigate the evolution of surface geometry during pulsed laser surface melting (pLSM) via level-set method-based interface tracking numerical framework. Existing models to track surface geometry are inaccurate and computationally expensive. Therefore, they have limited use in gaining understanding of the surface evolution during pLSM. A numerical model, integrating the level-set approach, fluid flow, and heat transfer dynamics, is detailed in this paper. The multi-phase numerical model achieves accurate tracking of interface for a single pulse by implementing the volumetric laser heat source on the moving interface by modifying Beer–Lambert's law. The accuracy of the single pulse model is confirmed by comparing its peak-to-valley height (PVH) to the experimental data. The deviation in PVH is limited to about 15%, with a maximum root mean square error of ∼0.24 µm, highlighting the model's reliability. Additionally, the evolved surface of a single pulse from the model is replicated over an area with dedicated overlaps to generate the predicted textured surface with reasonable accuracy. Some inaccuracies in the predicted surface roughness values were observed because the textures were generated based on a single pulse geometry computed on an initially flat surface. Nonetheless, the results highlight a significant development in numerical frameworks for pLSM and can be used as a tool to gain deeper insights into the process and for process optimization.

Predictive accuracy of machine learning models for conservative treatment failure in thoracolumbar burst fractures

Predictive accuracy of machine learning models for conservative treatment failure in thoracolumbar burst fractures

Effects of Red Vinasse on Physicochemical Qualities of Blue Round Scad (Decapterus maruadsi) During Storage, and Shelf Life Prediction

A fish processed with red vinasse is a type of Fujian cuisine with regional characteristics. In order to monitor the effect of red vinasse on storage quality and shelf life of blue round scad (Decapterus maruadsi) during storage, the changes in fat content, thiobarbituric acid reactive substances (TBARS), composition of polyunsaturated fatty acids (PUFAs), pH value, texture, and sensory quality were studied at different storage temperatures (4 °C, 25 °C, and 37 °C). By analyzing the correlation between changes in sensory qualities and physical and chemical indexes, a first-order kinetic model and the Arrhenius equation were used to build a shelf-life prediction model for blue round scad during storage. The results showed that processing with red vinasse can significantly reduce the malondialdehyde (MDA) production and the decrease in PUFAs, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) (p < 0.05) during storage. Based on partial least squares regression (PLSR), the storage temperature and time have a significant impact on the PUFA composition in blue round scad, which changed less when the samples were stored at 4 °C and 25 °C, and they had better nutritional composition of fatty acids at lower temperatures. Among the PUFAs, DHA (C22:6n-3) and EPA (C20:5n-3) had higher relative contents and significantly decreased during storage (p < 0.05). Additionally, the processing with red vinasse can slow down the increase in total volatile basic nitrogen (TVB-N) value and pH of blue round scad, maintain the appropriate hardness, elasticity, cohesion and chewability, and improve the overall sensory quality of the fish. In addition, according to the results of model prediction based on TBARS value, the storage shelf life of blue round scad with red vinasse added was 55 d, 2.7 d and 28 h at 4 °C, 25 °C and 37 °C, respectively. The accuracy of the forecast model was high, and the relative errors of the measured values and predicted values were less than 10%. Thus, it not only provided a theoretical basis for the processing and application of red vinasse to Chinese traditional food, but also provided innovative ideas for the safe storage and high-value utilization of blue round scad.

Privacy preserving large language models: ChatGPT case study based vision and framework

AbstractThe generative Artificial Intelligence (AI) tools based on Large Language Models (LLMs) use billions of parameters to extensively analyse large datasets and extract critical information such as context, specific details, identifying information, use this information in the training process, and generate responses for the requested queries. The extracted data also contain sensitive information, seriously threatening user privacy and reluctance to use such tools. This article proposes the conceptual model called PrivChatGPT, a privacy‐preserving model for LLMs consisting of two main components, that is, preserving user privacy during the data curation/pre‐processing and preserving private context and the private training process for large‐scale data. To demonstrate the applicability of PrivChatGPT, it is shown how a private mechanism could be integrated into the existing model for training LLMs to protect user privacy; specifically, differential privacy and private training using Reinforcement Learning (RL) were employed. The privacy level probabilities are associated with the document contents, including the private contextual information, and with metadata, which is used to evaluate the disclosure probability loss for an individual's private information. The privacy loss is measured and the measure of uncertainty or randomness is evaluated using entropy once differential privacy is applied. It recursively evaluates the level of privacy guarantees and the uncertainty of public databases and resources during each update when new information is added for training purposes. To critically evaluate the use of differential privacy for private LLMs, other mechanisms were hypothetically compared such as Blockchain, private information retrieval, randomisation, obfuscation, anonymisation, and the use of Tor for various performance measures such as the model performance and accuracy, computational complexity, privacy vs. utility, training latency, vulnerability to attacks, and resource consumption. It is concluded that differential privacy, randomisation, and obfuscation can impact the training models' utility and performance; conversely, using Tor, Blockchain, and Private Information Retrieval (PIR) may introduce additional computational complexity and high training latency. It is believed that the proposed model could be used as a benchmark for privacy‐preserving LLMs for generative AI tools.

Research on multi-scale fusion image enhancement and improved YOLOv5s lightweight ROV underwater target detection method.

Underwater target detection technology is very important for Marine resources detection and underwater environment perception. The problems of low-quality underwater images, a large amount of calculation, and low accuracy of target detection models still need to be solved. Firstly, a new underwater image enhancement algorithm based on multi-scale fusion is proposed. The algorithm combines the improved white balance algorithm and the improved CLAHE (Contrast Limited Adaptive Histogram Equalization) and realizes the multi-scale fusion strategy by introducing different weight maps, aiming to improve the visual effect and quality of underwater images. Secondly, to further improve the accuracy and efficiency of underwater target detection, an improved YOLOv5s lightweight underwater target detection algorithm is proposed. The algorithm combines the Ghost convolution module, EMA(Efficient Multi-Scale Attention) mechanism, and CARAFE (Content-Aware ReAssembly of FEatures) up-sampling mode. The comparison on the URPC(Underwater Robot Perception Challenge) public dataset shows that the proposed algorithm has achieved significant improvement in terms of model size, parameter amount, calculation amount, and mAP@0.5. Thirdly, to verify the effectiveness of the image enhancement algorithm and underwater target detection algorithm proposed in this paper, the ROV(Remote Operated Vehicle) is used to conduct experiments in the experimental pool. By designing underwater target detection experiments before and after image enhancement, the results show that the enhanced image significantly improves the detection ability of underwater targets. Finally, underwater target detection software is developed to provide strong support for the application of underwater-related fields.

SmartStudy-Learning Path Recommender system using DS and ML

Learning Path Recommender Systems (LPRS) utilize machine learning and data-driven approaches to personalize educational experiences, adapting content sequences based on individual learners’ needs and progress. With the rising demand for tailored learning, LPRS have emerged as vital tools to guide students through educational content, enhancing engagement and achievement. This review covers state-of-the-art algorithms, including collaborative filtering and reinforcement learning, used in LPRS to optimize learning paths. We discuss model accuracy, user interaction data, and adaptive feedback mechanisms, providing insights into the potential of LPRS to improve learning outcomes

Machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS.

The objective was to establish a machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS. 80% of sepsis patients selected randomly from the MIMIC-IV database, without prior pulmonary conditions and with nonpulmonary infection sites, were used to construct prediction models through machine learning techniques (including K-nearest neighbour, extreme gradient boosting, support vector machine, deep neural network, and decision tree methods). The remaining 20% of patients were utilized to validate the model's accuracy. Additionally, local data were employed for further model validation. A total of 11,409 patients were included, with the most common type of infection being bloodstream infection. A total of 7,632 (66.9%) patients developed nonpulmonary sepsis-associated ARDS (NPS-ARDS). Patients with NPS-ARDS had significantly longer ICU stays (6.2 ± 5.2 days vs. 4.4 ± 3.7 days, p < 0.01) and higher 28-day mortality rates (19.5% vs. 14.9%, p < 0.01). Both internal and external validation demonstrated that the model constructed with the extreme gradient boosting method had high accuracy. In the internal validation, the model predicted NPS-ARDS and mortality in such patients with accuracies of 77.5% and 71.8%, respectively. In the external validation, the model predicted NPS-ARDS and mortality in these patients with accuracies of 78.0% and 81.4%, respectively. The model established via the extreme gradient boosting method can predict the occurrence and mortality of nonpulmonary sepsis-associated ARDS to a certain extent.

Network architecture search methods for constructing efficient fault diagnosis models in rotating machinery

Abstract The development of high-performance fault diagnosis models for specific tasks requires substantial expertise. Neural Architecture Search (NAS) offers a promising solution, but most NAS methodologies are hampered by lengthy search durations and low efficiency, and few researchers have applied these methods within the fault diagnosis domain. This paper introduces a novel differentiable architecture search (DARTS) method tailored for constructing efficient fault diagnosis models for rotating machinery, designed to rapidly and effectively search for network models suitable for specific datasets. Specifically, this study constructs a completely new and advanced search space, incorporating various efficient, lightweight convolutional operations to reduce computational complexity. To enhance the stability of the differentiable network architecture search process and reduce fluctuations in model accuracy, this study proposes a novel Multi-scale Pyramid Squeeze Attention (MPSA) module. This module aids in the learning of richer multi-scale feature representations and adaptively recalibrates the weights of multi-dimensional channel attention. The proposed method was validated on two rotating machinery fault datasets, demonstrating superior performance compared to manually designed networks and general network search methods, with notably improved diagnostic effectiveness.

Linearly Scaling Molecular Dynamic Modeling To Simulate Picosecond Laser Ablation of a Silicon Carbide Crystal.

A molecular dynamics model for picosecond laser ablation of nanoscale silicon carbide crystals was established by linearly scaling the laser focal diameter, and the correlation between the molecular dynamic simulation of the nanoscale and the experimental reproduction of the microscale was achieved. The calculation accuracy of the molecular dynamic model was verified by ablating the surface of silicon carbide wafers with a laser pulse width of 37 ps. On this basis, this paper further investigated the influence of the laser pulse width and fluence on the surface ablation damage and modification width, threshold, and lattice temperature. The results showed that, when the laser pulse width is higher than 10 ps, the silicon carbide damage threshold increases with increasing the pulse width, while the modification threshold is almost unaffected by the pulse width. In addition, the influence of crystal orientation has been studied, and laser irradiation along the [1-100] crystal orientation induces a higher peak temperature, larger damage, and modification width and threshold, followed by irradiation along the [0001] crystal orientation and lowest along the [11-20] crystal orientation. Finally, with the linear scaling value increasing, the spatial distribution of the laser energy field deviates more from the actual situation, resulting in the calculated results being more consistent with the experimental results. Through this paper, it is demonstrated that this linearly scaled molecular dynamics model can be used to study laser ablation results over tens of micrometers.

Exploiting neural networks bit-level redundancy to mitigate the impact of faults at inference

AbstractNeural networks are widely used in critical environments such as healthcare, autonomous vehicles, or video surveillance. To ensure the safety of the systems that rely on their functionality, it is essential to validate their correct behaviour in the presence of faults. This paper studies the behaviour of state-of-the-art neural network models with fault injection in their weights. For this purpose, we analyse the sensitivity of these models and identify the impact of bit flips on their accuracy. To mitigate the effects of faults, we introduce two mechanisms that leverage bit-level redundancy for protection. The first mechanism, Fixed Protection, safeguards consecutive sets of bits, while the second, Variable Protection, targets non-consecutive bits. Our findings demonstrate that, on average, random bit flip faults cause the accuracy of the original models to drop by 1.3% to over 3%. However, with our protection mechanisms in place, accuracy reductions are significantly minimised, ranging from only 0.0001% to 0.4%.

Hyperspectral Imaging Combined with Deep Learning for the Early Detection of Strawberry Leaf Gray Mold Disease

The presence of gray mold can seriously affect the yield and quality of strawberries. Due to their susceptibility and the rapid spread of this disease, it is important to develop early, accurate, rapid, and non-destructive disease identification strategies. In this study, the early detection of strawberry leaf diseases was performed using hyperspectral imaging combining multi-dimensional features like spectral fingerprints and vegetation indices. Firstly, hyperspectral images of healthy and early affected leaves (24 h) were acquired using a hyperspectral imaging system. Then, spectral reflectance (616) and vegetation index (40) were extracted. Next, the CARS algorithm was used to extract spectral fingerprint features (17). Pearson correlation analysis combined with the SPA method was used to select five significant vegetation indices. Finally, we used five deep learning methods (LSTMs, CNNs, BPFs, and KNNs) to build disease detection models for strawberries based on individual and fusion characteristics. The results showed that the accuracy of the recognition model based on fused features ranged from 88.9% to 96.6%. The CNN recognition model based on fused features performed best, with a recognition accuracy of 96.6%. Overall, the fused feature-based model can reduce the dimensionality of the classification data and effectively improve the predicting accuracy and precision of the classification algorithm.

Enhancing Multilayer Glass Transmittance through Particle Swarm Optimization: An Experimental Approach

Abstract. This study focuses on enhancing the solar transmittance of multilayer glass in buildings located in cold northern regions to improve natural lighting efficiency and maintain indoor warmth in harsh climates. The Particle Swarm Optimization (PSO) algorithm is utilized to optimize the glass layer thickness, and an optical model is constructed to calculate the light transmittance of the multilayer glass. Through simulations and experimental analysis, the impact of different glass thickness combinations on overall transmittance is investigated. The study examines the optical properties of gradient glass and calculates changes in light transmittance under varying thickness combinations using MATLAB software. The PSO algorithm is applied for global optimization to identify the glass thickness combination that maximizes light transmittance within the specific visible light range (400nm-800nm). The experimental results, including spectral energy distribution before and after sunlight transmission, validate the accuracy of the theoretical model. The findings demonstrate that the optimized glass thickness combination significantly improves solar transmittance, especially within the visible light band, contributing to more energy-efficient and comfortable building environments in cold regions. Additionally, the potential of the PSO algorithm in global search and local convergence is analyzed, suggesting future research directions to further enhance optimization through dynamic adjustments or combining with other algorithms.

Comparative evaluation of machine learning models for assessment of seabed liquefaction using finite element data

Predicting wave-induced liquefaction around submarine pipelines is crucial for marine engineering safety. However, the complex of interactions between ocean dynamics and seabed sediments makes rapid and accurate assessments challenging with traditional numerical methods. Although machine learning approaches are increasingly applied to wave-induced liquefaction problems, the comparative accuracy of different models remains under-explored. We evaluate the predictive accuracy of four classical machine learning models: Gradient Boosting (GB), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF). The results indicate that the GB model exhibits high stability and accuracy in predicting wave-induced liquefaction, due to its strong ability to handle complex nonlinear geological data. Prediction accuracy varies across output parameters, with higher accuracy for seabed predictions than for pipeline surroundings. The combination of different input parameters significantly influences model predictive accuracy. Compared to traditional finite element numerical methods, employing machine learning models significantly reduces computation time, offering an effective tool for rapid disaster assessment and early warning in marine engineering. This research contributes to the safety of marine pipeline protections and provides new insights into the intersection of marine geological engineering and artificial intelligence.

Equivalent Heat Source Model of Thermal Relay Contact Based on Surface Roughness of Silver–Magnesium–Nickel Contact

In a sealed electromagnetic relay, the change in the surface roughness mainly depends on the collision wear between the contact and the moving reed and the ablation effect of the arc on the contact surface based on the strong correlation between the contact resistance and the surface roughness of the Ag-Mg-Ni contact. With a change in contact resistance, the contact temperature increase in a hermetically sealed electromagnetic relay (HSER) is greatly affected. Under extreme overload conditions, the contact surface is severely ablated by the arc, and the roughness increases rapidly with the number of cycles, which greatly affects the contact resistance of the contact surface and the reliability of the relay. A thermal model of a relay contact system based on the surface roughness of Ag-Mg-Ni contacts was established in this paper by analyzing the effect of an arc on the surface roughness of Ag-Mg-Ni contacts under heavy overload conditions. The arc image of the Ag-Mg-Ni contact was recorded using a double-axis arc photographing platform, and the moving track of the arc center under overload conditions was drawn. This paper explored the patterns of arc center movement on the contact surface and the effects of the arc on the surface roughness of the contacts by analyzing the probabilities of the arc center appearing in various locations. A mathematical model correlating the number of contact cycles with contact resistance was established. Subsequently, a finite element simulation model for the equivalent heat source of the contact was developed. The theoretical model error was less than 10%. The accuracy of the equivalent heat source model was verified by comparing the measured data with the simulation results.

Reliability Analysis of High-Pressure Tunnel System Under Multiple Failure Modes Based on Improved Sparrow Search Algorithm–Kriging–Monte Carlo Simulation Method

It is often difficult for a structural safety design method based on deterministic analysis to fully and reasonably reflect the randomness of mechanical parameters, while the traditional reliability analysis method has a large calculation cost and low accuracy. In this paper, based on the seepage–stress coupling numerical model, the random variables affecting the reliability of the collaborative bearing of surrounding rock and lining structures are successfully identified. Then, the improved sparrow search algorithm (ISSA) is used to optimize the hyper-parameters of the Kriging surrogate model, in order to improve the computational efficiency and accuracy of the reliability analysis model. Finally, the ISSA-Kriging-MCS model is used to quantitatively evaluate the reliability of the surrounding rock-reinforced concrete lining structure under multiple failure modes, and the sensitivity of each random variable is discussed in depth. The results show that the high-pressure tunnel structure has high safety and reliability. The reliability indexes of each failure mode decrease with the increase in the coefficient of variation (COV) of random variables. In addition, the same random variable also exhibits varying degrees of influence in different failure modes.

Research on Urban Traffic Flows Prediction Model Based on Deep Learning

Abstract. Vehicle accumulation in urban regions is a significant complication, giving rise to monetary and sustainability-related concerns. The most important procedure for effective traffic management is the precise forecasting of traffic flow. This study leverages Long Short-Term Memory (LSTM) networks to predict traffic volume on Interstate-94 (I-94) in the US, using hourly traffic and weather data from 2012 to 2018, which was normalized using Min-Max scaling, and the LSTM model was trained on 80% of the data, with the remaining 20% used for testing. Evaluation of the model was conducted using performance indicators such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Showing its strong forecasting ability, evidenced by its low MAE and RMSE, which highlight the model's high accuracy, and forecast the traffic flow under varying conditions. This study highlights the effectiveness of LSTM networks for traffic prediction, offering a significant tool for the management of the metropolitan areas. Upcoming studies might prioritize on real-time implementation and integrating additional data sources to further enhance prediction accuracy.