F2 Values Research Articles

AMFNet: Attention-Guided Multi-Scale Fusion Network for Bi-Temporal Change Detection in Remote Sensing Images

Change detection is crucial for evaluating land use, land cover changes, and sustainable development, constituting a significant component of Earth observation tasks. The difficulty in extracting features from high-resolution images, coupled with the complexity of image content, poses challenges for traditional change detection algorithms in terms of accuracy and applicability. The recent emergence of deep learning methods has led to substantial progress in the field of change detection. However, existing frameworks often involve the simplistic integration of bi-temporal features in specific areas, lacking the fusion of temporal information and semantic details in the images. In this paper, we propose an attention-guided multi-scale fusion network (AMFNet), which effectively integrates bi-temporal image features and diverse semantics at both the encoding and decoding stages. AMFNet utilizes a unique attention-guided mechanism to dynamically adjust feature fusion, enhancing adaptability and accuracy in change detection tasks. Our method intelligently incorporates temporal information into the deep learning model, considering the temporal dependency inherent in these tasks. We decode based on an interactive feature map, which improves the model’s understanding of evolving patterns over time. Additionally, we introduce multi-level supervised training to facilitate the learning of fused features across multiple scales. In comparison with different algorithms, our proposed method achieves F1 values of 0.9079, 0.8225, and 0.8809 in the LEVIR-CD, GZ-CD, and SYSU-CD datasets, respectively. Our model outperforms the SOTA model, SAGNet, by 0.69% in terms of F1 and 1.15% in terms of IoU on the LEVIR-CD dataset, by 2.8% in terms of F1 and 1.79% in terms of IoU on the GZ-CD dataset, and by 0.54% in terms of F1 and 0.38% in terms of IoU on the SYSU-CD dataset. The method proposed in this study can be applied to various complex scenarios, establishing a change detection method with strong model generalization capabilities.

Application of non parametric Bayesian methods in high dimensional data

With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to combine non parametric methods with Bayesian methods for application research in high-dimensional data analysis. In this paper, the nonparametric Bayesian method was applied to the analysis of high-dimensional data, and the Dirichlet process Mixture model was used to cluster high-dimensional data. The regression analysis of high-dimensional data was carried out through the prediction model of nonparametric Bayesian regression. In this paper, the nonparametric Bayesian method based on Bayesian sparse linear model was used for feature selection of high-dimensional data. In order to determine the superiority of nonparametric Bayesian methods in high-dimensional data analysis, this paper conducted experiments on nonparametric Bayesian methods and traditional parametric methods in high-dimensional data analysis from five aspects of cluster analysis, classification analysis, regression analysis, feature selection and anomaly detection, and evaluated them through multiple indicators. This article explored the application of non parametric Bayesian methods in high-dimensional data analysis from these aspects through simulation experiments. The experimental results show that the clustering accuracy of the non parametric Bayesian clustering algorithm was 0.93, and the accuracy of the non parametric Bayesian classification algorithm was between 0.93 and 0.99; the coefficient of determination of nonparametric Bayesian regression algorithm was 0.98; the F1 values of non parametric Bayesian methods in anomaly detection ranged from 0.86 to 0.91, which was superior to traditional methods. Non parametric Bayesian methods have broad application prospects in high-dimensional data analysis, and can be applied in multiple fields such as clustering, classification, regression, etc.

Multi-scene application of intelligent inspection robot based on computer vision in power plant

As industries develop, the automation and intelligence level of power plants is constantly improving, and the application of patrol robots is also increasingly widespread. This research combines computer vision technology and particle swarm optimization algorithm to build an obstacle recognition model and obstacle avoidance model of an intelligent patrol robot in a power plant respectively. Firstly, the traditional convolutional recurrent neural network is optimized, and the obstacle recognition model of an intelligent patrol robot is built by combining the connection timing classification algorithm. Then, the artificial potential field method optimizes the traditional particle swarm optimization algorithm, and an obstacle avoidance model of an intelligent patrol robot is built. The performance of the two models was tested, and it was found that the highest precision, recall, and F1 values of the identification model were 0.978, 0.974, and 0.975. The highest precision, recall, and F1 values of the obstacle avoidance model were 0.97, 0.96, and 0.96 respectively. The two optimization models designed in this research have better performance. In conclusion, the two models in this study are superior to the traditional methods in recognition effect and obstacle avoidance efficiency, providing an effective technical scheme for intelligent patrol inspection of power plants.

Exploring forest fire susceptibility and management strategies in Western Himalaya: Integrating ensemble machine learning and explainable AI for accurate prediction and comprehensive analysis

Forest fires pose a significant threat to ecosystems and socio-economic activities, necessitating the development of accurate predictive models for effective management and mitigation. In this study, we present a novel machine learning approach combined with Explainable Artificial Intelligence (XAI) techniques to predict forest fire susceptibility in Nainital district. Our innovative methodology integrates several robust models — AdaBoost, Gradient Boosting Machine (GBM), XGBoost and Random Forest — with a Deep Neural Network (DNN) as a meta-model in a stacking framework. This approach not only utilises the individual strengths of these models, but also improves the overall prediction performance and reliability. By using XAI techniques, in particular SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), we improve the interpretability of the models and provide insights into the decision-making processes. Our results show the effectiveness of the ensemble model in categorising forest fire susceptibility into different zones: very low, low, moderate, high and very high. In particular, the stacking ensemble and XGBoost models identified extensive areas of high and very high susceptibility, with precision, recall and F1 values underpinning their effectiveness. These models achieved ROC AUC values above 0.90, with XGBoost performing exceptionally well with an AUC of 0.94. The precision, recall and F1 values for these models are remarkably high. The inclusion of confidence intervals for the most important metrics in all models emphasises their robustness and reliability and supports their practical use in forest fire management. Through SHAP summary plots, we analyze the global variable importance, revealing annual rainfall and Evapotranspiration (ET) as key factors influencing susceptibility. Local analysis using LIME consistently highlights the importance of annual rainfall, ET, and distance from roads across all models. This study fills an important research gap by providing a comprehensive and interpretable modelling approach that improves our ability to predict and effectively manage forest fire risk and is consistent with environmental protection and sustainable development goals.

URL Detection Based on Quantum Long Short-Term Memory Neural Network

In today's era, with the continuous development of the internet, surfing the internet has become an inseparable part of our lives. For those who frequently roam the internet, URLs are information that we are constantly exposed to. And YRL is also divided into malicious and normal. Clicking on malicious URLs can cause certain losses to our internet tools, and in severe cases, it can even lead to money theft. Malicious URL detection is an important task in the field of network security, aimed at identifying and preventing potential network attacks and malicious activities. Traditional malicious URL detection methods, such as Blacklist based detection and Machine learning based on feature extraction, have achieved certain results, but face challenges in real-time, accuracy, and data requirements. Therefore, this article proposes the use of Quantum Long Short-Term Memory Neural Network (QLSTM) to solve this problem. By observing the accuracy, recall, and F1 values obtained from training a large amount of data on the QLSTM network, it is shown that this method is feasible.

Real time electronic-waste classification algorithms using the computer vision based on Convolutional Neural Network (CNN): Enhanced environmental incentives

An innovative approach is needed to boost the economic value of e-waste by improving metal recovery and facilitating the separation of plastics and valuable metal components. Leveraging deep learning and computer vision technology offers a promising solution for automatically categorizing and sorting e-waste components like copper, printed circuit boards (PCB), steel, glass, and aluminum, presenting significant financial and environmental incentives for increased recycling efforts. In this instance, the real-time object identification algorithms YOLO 7 and 5 were used along with TensorFlow version 2.8.0. The e-waste dataset works incredibly well with TensorFlow and YOLO. For the detection of copper, PCBs, and plastic, the F1 values (F1 score is the harmonic mean of precision and recall; precision is the fraction of relevant instances among the retrieved instances whereas recall or sensitivity is the fraction of relevant instances that were retrieved) were as good as 1.0, whereas the score for steel and aluminum was 0.8. The mean average precision (mAP; for a set of queries, it is the mean of the average precision scores for each query) was 0.96 for all classes, with the highest precision for copper (0.99) followed by PCB (0.981). 240 e-waste objects were independently evaluated using the YOLO v7 model, achieving a remarkable ∼94 % prediction accuracy with a batch size of 16, ensuring robust performance. The real-time e-waste component detection was also done using video clips as well as webcam streaming. Deploying real-time e-waste object detection and sorting can significantly narrow the gap between e-waste accumulation and recycling rates, leading to reduced environmental strain and impact.

Clear speech effects in production of sentence-medial Mandarin lexical tonesa).

Adding to limited research on clear speech in tone languages, productions of Mandarin lexical tones were examined in pentasyllabic sentences. Fourteen participants read sentences imagining a hard-of-hearing addressee or a friend in a casual social setting. Tones produced in clear speech had longer duration, higher intensity, and larger F0 values. This style effect was rarely modulated by tone, preceding tonal context, or syllable position, consistent with an overall signal enhancement strategy. Possible evidence for tone enhancement was observed only in one set of analysis for F0 minimum and F0 range, contrasting tones with low targets and tones with high targets.

Marathi Speech Emotion recognition using Deep Learning techniques.

Marathi Speech Emotion recognition using Deep Learning techniques.

AIseed Simulation: A seed simulation sorting software for rapidly determining seed processing procedures and parameters

Seed processing is a crucial measure for improving seed quality after harvest. However, traditional seed processing procedures and parameter settings have largely depended on technicians' operational experience. To enhance the digitization of the seed processing procedures, AIseed Simulation software was developed and incorporates ''Simulated sorting'' and ''One-click sorting'' based on RGB images of seeds and impurities. Based on the sorting principles of current main seed sorting equipment, length, width, thickness, specific gravity, and color features (R, G, B, R/G, R/B, G/B) are selected. Through Spearman correlation analysis and probability density distribution plots, the sorting features and parameters are determined. By repeatedly comparing precision, recall, and F1 values of different sorting combinations, simulation sorting on a computer is achieved. The ''One-click sorting'' function has been integrated into the above procedures, facilitating automation and making the determination of optimal seed processing procedures and parameters easier and more rapid. The AIseed Simulation was individually validated on five crop seeds—Astragalus membranaceus, Perilla frutescens, Scutellaria baicalensis, Bupleurum chinense, and Platycodon grandiflorus—and the processing procedures and parameters established for each seed type were validated with actual processing equipment. The results validated that AIseed Simulation is a stand-alone, automated RGB image processing platform suitable for seed processing and parameter setting. Furthermore, the AIseed Simulation software exhibits versatility in a range of seed processing scenarios, suitable for both large-scale seed processing facilities and the demands of scientific research and education. These findings contribute significantly to enhancing seed quality and facilitate the intelligent development of the seed processing industry.

Acoustic Analyses of Tone Productions in Sequencing Contexts Among Cantonese-Speaking Preschool Children With and Without Childhood Apraxia of Speech.

Pitch variations (tone productions) have been reported as a measure to differentiate Cantonese-speaking children with and without childhood apraxia of speech (CAS). This study aims to examine fundamental frequency (F0) changes within syllables and the effects of syllable structure, lexical status, and syllable positions on F0 in Cantonese-speaking preschool children with and without CAS. Six children with CAS, six children with non-CAS speech sound disorder plus language disorder (S&LD), 22 children with speech sound disorder only (SSD), and 63 children with typical speech-language development (TD) performed the tone sequencing task (TST). Growth curve analysis was employed to analyze and compare the F0 values within syllables with three Cantonese tones (high level, high rising, and low falling). The analysis considered the effects of syllable structure (vowel and consonant-vowel), lexical status (word and nonword), and syllable position (initial, medial, and final) on F0, as well as comparisons within and between groups. Within each group, the effects of syllable structure and position on F0 values were found with different patterns. Between-group comparisons showed that the CAS group had reduced F0 contrasts. The CAS group could be differentiated from the control groups based on interactions of F0 with syllable structure and position, but not lexical status. The dissimilarity of F0 values detected between the CAS and SSD/TD groups was more prominent than that observed between the CAS and S&LD groups. This study demonstrated that Cantonese-speaking children with CAS had difficulty in varying F0 within syllables as compared to those without CAS, suggesting pitch variation difficulty and language-specific impairment profiles in CAS. Future investigations of objective measures for identifying Cantonese speakers with CAS and cross-linguistic investigations using growth curve analysis and the TST are suggested.

Remote supervised relationship extraction method of clustering for knowledge graph in aviation field

In the process of building domain knowledge graph, the result of relationship extraction between entities is an important guarantee of the quality of the graph. Therefore, we propose a clustering method based on reinforcement learning for remote supervised relation extraction. For the relationship extraction of accident information in the aviation domain mapping, a clustering method combining local dense and global dissimilarity is proposed in combination with remote supervision, which can obtain a large amount of low-noise labeled data and reduce part of the wrong labeling and missing labeling due to the strong specialization in the aviation domain; meanwhile, reinforcement learning is introduced to denoise the negative instance noise in the positive sample data; Finally, we propose a two-attention segmentation (DAPCNN) relationship extraction model to mine deep semantic sentences. The experimental results show that in the civil aviation relationship extraction text constructed in this paper, the Micro_R, Micro_P and Micro_F1 values of the proposed relationship extraction method reach 83.41 %, 84.16 % and 83.96 %. In the open relationship extraction dataset DuIE, The Micro_R, Micro_P and Micro_F1 of the proposed method are up to 83.41 %, 93.58 % and 94.02 % respectively. Compared with the current advanced multi-instance and multi-label model, the proposed method can more accurately extract the relationship between aviation accident entities. At the same time, the performance of the open data set is also good, and has a certain universality.

QoS prediction using EMD-BiLSTM for II-IoT-secure communication systems

Abstract To address the challenges of secure and reliable communication and system quality of service (QoS) prediction in intelligent production lines (IPL) in the Industrial Intelligent Internet of Things (II-IOT) environment, a redundant collaborative security model-based communication architecture is designed. First, the redundant collaborative security communication model is introduced to construct the network communication architecture of IPL, including the industrial-site mechanical floor, data awareness layer, and gateway and application layer. Then, to leverage the advantages of the empirical-mode decomposition (EMD) method and the bidirectional long short-term memory (BiLSTM) model in time-series data analysis and processing, an EMD-BiLSTM-based QoS prediction model is proposed that can synchronously achieve one-step and multi-step prediction of QoS attributes. The proposed model exhibits a prediction accuracy of up to 94.01% on the SourceForge dataset, with prediction, recall, and F1 values as high as 91.37, 90.60, and 90.99%, respectively. The proposed EMD-BiLSTM model can achieve better performance than the state-of-the-art QoS prediction models, indicating that the proposed model can be more effectively used to improve the reliable communication level of II-IoT.

Voice aspects in children with precocious puberty

PurposeIn this prospective study, we aimed to investigate the difference in voice acoustic parameters between girls with idiopathic central precocious puberty (ICPP) and those who developed normally during prepuberty. Materials and methodsOur study recruited 54 girls diagnosed with ICPP and randomly sampled 51 healthy prepubertal girls as the control. Tanner stages, circulating hormone levels and bone ages of the girls with ICPP and the age and body mass index (BMI) of all participants were recorded. Acoustic analyses were performed using PRAAT computer-based voice analysis software and the mean pitch (F0), jitter, shimmer, noise-to harmonic-ratio (NHR) and harmonic-to-noise ratio (HNR) values were compared in the patient and control groups. ResultsThe two groups did not significantly differ in age or BMI. In the evaluation of the F0 and jitter values, we were found to be lower in the control group than in the patient group. However, we did not find a statistical significance. The mean shimmer values of the patient group were significantly higher than those of the control group. In addition, a statistically significant difference was noted for the mean HNR and NHR values (P < 0.001). A moderate negative correlation was found between shimmer and hormone levels in the patient group. ConclusionsVoice acoustic parameters one of the defining features of girls with ICPP. Voice changes in acoustic parameters could reflect hormonal changes during puberty. Clinicians should suspect ICPP when there is a change in the voice.

Impact of prominence type on the coarticulation of vowels following palatalized consonants

The quantitative impact of prominence (neutral vs emphatic stress) on the coarticulation of the Russian low [æ], mid [e] and high [i] front vowels is evaluated in CV sequences with palatalized consonants of various places of obstruction. The research is conditioned by the aims of speech synthesis and recognition of the Russian language, the current data in the field being insufficient. First order linear equations (locus equations) are used to evaluate the degree of CV coarticulation. The experiment is performed on CjVCjV nonsense words imbedded in the middle position of a carrier phrase «Vyros CVCV sjyljnym» («CjVCjV has grown up strong»). The first syllable of the target words was uttered both with neutral and emphatic stress. The Praat software was used to measure F2 formant values at vowel onsets and central parts. It was established that linear regression slopes are similar both in the Y-intercepts and slope coefficients for the palatalized consonants of various places of obstruction uttered with the same prominence. The degree of coarticulation was different depending upon the type of prominence (neutral or emphatic): the coefficients were lower in case of the emphatic stress compared to those of the neutral one. ANOVA analysis and Student’s t-test were performed to reveal the difference in F2 values of vowel onsets and middle parts as a function of the vowel openness and type of prominence. The results obtained were interpreted based on the degree of the openness of the vowels under study: an open vowel under emphatic stress tends to expand its vowel space by a backward displacement while close vowels tend to move forward in the mouth cavity when pronounced with emphatic stress.

Real-time tunnel lining leakage image semantic segmentation via multiple attention mechanisms

One of the key objectives in tunnel illness detection is identifying tunnel lining leakage, and deep learning-based image semantic segmentation approaches can automatically locate tunnel lining leakage. However, in order to meet the real-time processing needs of professional mobile inspection equipment, existing leakage image segmentation approaches have difficulties in identifying real-time, dealing with voids, and dealing with edge discontinuities in the leaking zone. To address the aforementioned issues, this study introduces the PP-LiteSeg-Attn model, which takes the real-time semantic segmentation model PP-LiteSeg-B as baseline model, and combines the multi-layer CBAM attention mechanism and the CoT attention mechanism. Using the publically available dataset Water-Leakage, we trained and validated the PP-LiteSeg-Attn model, and attained IoU and F1 values of 88.18% and 93.72%, respectively, outperforming similar models in both measures. Extensive experiments show that the segmentation speed of the PP-LiteSeg-Attn model reaches 112.28 FPS, which meets real-time requirements, and that the model can effectively solve problems such as the appearance of voids in the seepage area, discontinuity, and fuzzy segmentation of seepage edges. The PP-LiteSeg-Attn model is better applicable to complicated tunnel settings, offering technical references for real-time diagnosis of tunnel illnesses.

New ensemble learning algorithm based on classification certainty and semantic correlation

A new bootstrap-aggregating (bagging) ensemble learning algorithm is proposed based on classification certainty and semantic correlation to improve the classification accuracy of ensemble learning. First, two predetermined thresholds are introduced to construct the long and short-text sample subsets, and different deep learning methods are compared to construct the optimal base classifier groups for each sample subsets. Then, the random sampling method employed in traditional bagging classification algorithms is improved, and a threshold group based random sampling method is proposed to obtain long and short training sample subsets of each iteration. Finally, the sample classification certainty of the base classifiers for different categories is defined, and the semantic correlation information is integrated with the traditional weighted voting classifier ensemble method to avoid the loss of important information during the sampling process. The experimental results on multiple datasets demonstrate that the algorithm significantly improves text classification accuracy and outperforms typical deep learning algorithms. The proposed algorithm achieves the improvements of approximately 0.082, 0.061 and 0.019 on CNews dataset when the F1 measurement is used over the traditional ensemble learning algorithms such as random forest, M_ADA_A_SMV and CNN_SVM_LR. Moreover, it achieves the best F1 values of 0.995, 0.985, and 0.989 on the datasets of Spam, CNews, and SogouCS datasets, respectively, when compared with the ensemble learning algorithms using different base classifiers.

Language model based on deep learning network for biomedical named entity recognition

Biomedical Named Entity Recognition (BioNER) is one of the most basic tasks in biomedical text mining, which aims to automatically identify and classify biomedical entities in text. Recently, deep learning-based methods have been applied to Biomedical Named Entity Recognition and have shown encouraging results. However, many biological entities are polysemous and ambiguous, which is one of the main obstacles to the task of biomedical named entity recognition. Deep learning methods require large amounts of training data, so the lack of data also affect the performance of model recognition. To solve the problem of polysemous words and insufficient data, for the task of biomedical named entity recognition, we propose a multi-task learning framework fused with language model based on the BiLSTM-CRF architecture. Our model uses a language model to design a differential encoding of the context, which could obtain dynamic word vectors to distinguish words in different datasets. Moreover, we use a multi-task learning method to collectively share the dynamic word vector of different types of entities to improve the recognition performance of each type of entity. Experimental results show that our model reduces the false positives caused by polysemous words through differentiated coding, and improves the performance of each subtask by sharing information between different entity data. Compared with other state-of-the art methods, our model achieved superior results in four typical training sets, and achieved the best results in F1 values.

AI-driven design of customized 3D-printed multi-layer capsules with controlled drug release profiles for personalized medicine

Personalized medicine aims to effectively and efficiently provide customized drugs that cater to diverse populations, which is a significant yet challenging task. Recently, the integration of artificial intelligence (AI) and three-dimensional (3D) printing technology has transformed the medical field, and was expected to facilitate the efficient design and development of customized drugs through the synergy of their respective advantages. In this study, we present an innovative method that combines AI and 3D printing technology to design and fabricate customized capsules. Initially, we discretized and encoded the geometry of the capsule, simulated the dissolution process of the capsule with classical drug dissolution model, and verified it by experiments. Subsequently, we employed a genetic algorithm to explore the capsule geometric structure space and generate a complex multi-layer structure that satisfies the target drug release profiles, including stepwise release and zero-order release. Finally, Two model drugs, isoniazid and acetaminophen, were selected and fused deposition modeling (FDM) 3D printing technology was utilized to precisely print the AI-designed capsule. The reliability of the method was verified by comparing the in vitro release curve of the printed capsules with the target curve, and the f2 value was more than 50. Notably, accurate and autonomous design of the drug release curve was achieved mainly by changing the geometry of the capsule. This approach is expected to be applied to different drug needs and facilitate the development of customized oral dosage forms.

Blockchain Enhanced Student Physical Performance Analysis using Machine Learning-IoT and Apriori Algorithm in Physical Education Network Teaching

In the digital era, particularly with the rise of online teaching, traditional approaches to college physical education face challenges in adequately monitoring and enhancing students’ physical fitness. This study introduces a novel approach that integrates blockchain technology with a Machine Learning-IoT framework to evaluate and improve students' physical performance. Utilizing the Apriori algorithm, enhanced with particle swarm optimization and an improved K-means methodology, this system offers a robust tool for correlating student behavior with sports performance in a secure and decentralized manner. The proposed system uses blockchain for safe data management and IoT for real-time data collection, ensuring privacy as well as efficiency. The algorithm's accuracy, recall, and F1 values on the Iris dataset are 0.947, 0.931, and 0.928, respectively, with a considerable Calinski Harabasz score of more than 240. When applied to university student behavior data, the blockchain-enhanced system successfully mined association rules with a maximum confidence level of 0.923.

Chinese medical short text classification model based on DPECNN

Medical short text classification is of great significance to medical information extraction and medical auxiliary diagnosis. However, medical short texts face challenges such as sparse features, semantic ambiguity, and the specialized nature of the medical field, resulting in relatively low accuracy in short text classification. Taking into consideration the characteristics of medical short texts, this paper proposes a Chinese medical short text classification model based on DPECNN. First, ERNIE is utilized to learn text knowledge and information in order to enhance the model’s semantic representation capabilities. Then, the DPECNN model is employed to extract rich feature information, and the classification results are generated through a fully connected layer. In the case of DPCNN, it only considers deep-level contextual semantic information, overlooking the correlation of adjacent semantic information between channels. To address this, ECA channel attention is introduced to account for adjacent semantic information. The use of a self-normalizing activation function helps avoid the problem of vanishing gradients. To enhance the model’s robustness and generalization ability, the FGM adversarial training algorithm is employed to perturb the data. The F1 values achieved on the THUCNews, KUAKE-QIC, and CHIP-CTC datasets are 95.00%, 79.45%, and 82.81%, respectively.