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The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers. [ABSTRACT FROM AUTHOR]

This work reports the degradation of cellulosic material, i.e., paper, under different environmental conditions (soil and hydrolytic). Models were developed using central composite design (CCD) within the framework of response surface methodology (RSM) and artificial neural network (ANN) techniques. It is done as part of a solid waste management system to eliminate the waste produced by excessive paper usage and obtain optimized degradation conditions of paper. In view of the real environments where paper-based solid wastes are usually exposed, soil degradation and hydrolytic degradation of paper were investigated. The factors pH (4–10), compost ratio (2–5), CaCl2 (5–15 ppm), and time (7–20 days) were independent factors that varied for the study, and degradation conversion was measured as the dependent variable for soil degradation of paper. The factors pH (4–10), CaCl2 (2–5 ppm), tripotassium phosphate (TPP, 2–5 ppm), and time (6–16 days) were independent variables that varied for the study, and degradation conversion was the dependent variable for hydrolytic degradation of paper. The optimal conditions for soil degradation were determined to be a pH of 4, a compost ratio of 5, a salt (CaCl2) addition of 5 ppm, and a duration of 20 days, resulting in the highest observed conversion rate. The coefficient of regression (R2) for CCD is 87.24%, whereas it is 94.82% for ANN. The optimal conditions for achieving maximum conversion in hydrolytic degradation include a pH of 10, a CaCl2 salt concentration of 5 ppm, a TPP salt concentration of 2 ppm, and a duration of 16 days. The coefficient of regression for CCD is 75.15%, while the coefficient of regression for ANN is 93.91%. In both deterioration scenarios, the data exhibited a higher degree of fit when modeled using ANN compared to CCD (RSM). [ABSTRACT FROM AUTHOR]

This paper is aimed to develop a hybrid model (PCA-ANN) to predict the extractive content in eucalyptus wood clones. The input variables to the virtual analyzer are planting parameters available from the forest inventory, therefore the prediction does not rely on laboratory analysis of the wood samples, affording a quick estimate of the extractive contents. This study further bridges the literature gap on the investigation of the cause of variability of extractive content in eucalyptus wood. The PCA-ANN was identified from experimental data to predict and monitor the extractive content, since laboratory measurements can take several days and become available only after wood processing. The experimental data contained information on ten species of eucalyptus clones from five regions in the extreme south of Bahia, Brazil. Principal Component Analysis (PCA) firstly assessed the impact of planting variables on the extractives content. The variability of the data was represented by eight principal components and the variables that mostly contribute to the extractive content are: potential acidity, iron, saturation of aluminum, magnesium, pH, base saturation, remaining phosphorus, zinc, manganese and copper. The artificial neural network (ANN) with the 8 principal components in the input layer showed that the PCA could effectively reduce the dimensionality of the data. For practical purposes, though, the ANN with 10 input variables and 16 neurons in the hidden layer, presenting an average relative deviation of 1.5%, is recommended. The prediction of the extractive content is essential to allow preventive management practices toward the improvement of yield and quality of the cellulosic pulp. [ABSTRACT FROM AUTHOR]

Measuring the complex permittivity of ultrathin, flexible materials with a high loss tangent poses a substantial challenge with precision using conventional methods, and verifying the accuracy of test results remains difficult. In this study, we introduce a methodology based on a back-propagation artificial neural network (ANN) to extract the complex permittivity of paper-based composites (PBCs). PBCs are ultrathin and flexible materials exhibiting considerable complex permittivity and dielectric loss tangent. Given the absence of mature measurement methods for PBCs and a lack of sufficient data for ANN training, a mapping relationship is initially established between the complex permittivity of honeycomb-structured microwave-absorbing materials (HMAMs, composed of PBCs) and that of PBCs using simulated data. Leveraging the ANN model, the complex permittivity of PBCs can be extracted from that of HMAMs obtained using standard measurement. Subsequently, two published methods are cited to illustrate the accuracy and advancement of the results obtained using the proposed approach. Additionally, specific error analysis is conducted, attributing discrepancies to the conductivity of PBCs, the homogenization of HMAMs, and differences between the simulation model and actual objects. Finally, the proposed method is applied to optimize the cell length parameters of HMAMs for enhanced absorption performance. The conclusion discusses further improvements and areas for extended research. [ABSTRACT FROM AUTHOR]

The color gamut of imaging media is significant for the reproduction of color images because its magnitude directly affects the degree to which colors change during the printing process. Over the last few years, digital impression technology has started to play a substantial role in the printing industry due to the quest for short runs and variable information printing. The color gamut of electrophotographic digital printing depends on various parameters including the printer and toner, but especially the properties (whiteness, roughness, and gloss) of the paper, which influence the final printed color gamut and replication quality. Artificial intelligence approaches are applied herein for the first time to choose and predict the performance of a paper with appropriate properties to achieve the maximum color gamut. A genetic algorithm-based computer code is developed to optimize the architecture of an artificial neural network, thereby yielding an accurate model to predict the color gamut achievable in electrophotographic color printing. The gamut volume was generated using an Eye-One spectrophotometer, ProfileMaker, and ColorThink software. The properties of 11 dissimilar types of paper were assessed by atomic force microscopy, spectrophotometer, and goniophotometer. The results indicate that the reproducibility depended considerably on the features of the paper. Although high whiteness and gloss increased the color gamut volume, and high roughness decreased the reproducibility of the printing machine, the artificial intelligence approach provided the opportunity to achieve a high gamut volume with low gloss and high roughness. [ABSTRACT FROM AUTHOR]

The text discusses the intersection of natural language understanding (NLU) and reasoning in the context of large language models (LLMs) and traditional logic-based approaches. It highlights the strengths and weaknesses of both approaches and explores the potential for hybrid models that combine symbolic and distributional representations. The text also mentions specific applications of hybrid approaches in natural language inference, question-answering, sentiment analysis, and dialog. The document concludes by introducing a special issue that features selected contributions from the NALOMA workshop series, which focuses on hybrid methods in NLU. [Extracted from the article]

One key frontier of artificial intelligence (AI) is the ability to comprehend research articles and validate their findings, posing a magnanimous problem for AI systems to compete with human intelligence and intuition. As a benchmark of research validation, the existing peer-review system still stands strong despite being criticized at times by many. However, the paper vetting system has been severely strained due to an influx of research paper submissions and increased conferences/journals. As a result, problems, including having insufficient reviewers, finding the right experts, and maintaining review quality, are steadily and strongly surfacing. To ease the workload of the stakeholders associated with the peer-review process, we probed into what an AI-powered review system would look like. In this work, we leverage the interaction between the paper's full text and the corresponding peer-review text to predict the overall recommendation score and final decision. We do not envisage AI reviewing papers in the near future. Still, we intend to explore the possibility of a human–AI collaboration in the decision-making process to make the current system FAIR. The idea is to have an assistive decision-making tool for the chairs/editors to help them with an additional layer of confidence, especially with borderline and contrastive reviews. We use a deep attention network between the review text and paper to learn the interactions and predict the overall recommendation score and final decision. We also use sentiment information encoded within peer-review texts to guide the outcome further. Our proposed model outperforms the recent state-of-the-art competitive baselines. We release the code of our implementation here: https://github.com/PrabhatkrBharti/PEERRec.git. [ABSTRACT FROM AUTHOR]

Through call for Papers and the Review Process, the Special Issue has Accepted 22 Articles Covering Transfer Learning Model Designs and Applications. These 22 articles in this special issue provide a general picture of the development and applications of transfer learning method. Transfer Learning is a Machine Learning Paradigm which Reuses Previously Trained Models to help Solve a new Learning task. [Extracted from the article]

Cancer is characterized as a heterogeneous disease of various types. The early detection and prognosis of a cancer type have turned into a major requirement, as it facilitates successive medical treatment of patients. The research team has classified the cancer patients into high or low-risk groups. This makes it a significant task for the medical teams to study the application of deep learning and machine learning models. As a result, such techniques have been employed for modeling the development and treatment of cancer conditions. Additionally, the machine learning tools can have the ability the significant detection features from complex datasets. Numerous techniques like Support Vector Machines (SVM), Bayesian Networks (BN), Decision Trees (DT), Artificial Neural Networks (ANN), Recurrent Neural Network (RNN), and Deep Neural Network (DNN) has been broadly utilized in cancer research. As per the current survey, the detection rate is about 99.89%, which shows the prediction models' efficiency and precise decision making. However, it is proven that deep learning and machine learning approaches can enhance cancer progression. An adequate level of estimation is required for such approaches for considering the daily medical practice. This survey analyzes and learns the diverse contributions of cancer prediction models using intelligent approaches. Further, the paper tries to categorize the different algorithms, the utilized datasets, and utilized environments. Along with this, various performance measures evaluated in each contribution is sorted out. An extensive search is conducted relevant to machine learning and deep learning methods in cancer susceptibility, recurrence, and survivability prediction, and the existing challenges in this area are clearly described. However, ML models are still in the testing as well as the experimentation phase for cancer prognoses. As the datasets are getting larger with higher quality, researchers are building increasingly accurate models. Moreover, ML models have a long way to go, and most of the models still lack sufficient data and suffer from bias. [ABSTRACT FROM AUTHOR]

Due to the large intra-class differences between the same categories and the scale imbalance between different categories in the remote sensing image dataset, the semantic segmentation task presents the problem of small-scale object information loss, the imbalance between foreground and background, and simultaneously the background dominates, which seriously affects the performance of the network model. To solve the above problems, this paper proposes an efficient bilateral branch depth neural network model based on the U-Net depth neural network, named BBU-Net. Firstly, one branch of the network learns the distribution characteristics of the original data, and the other focuses on difficult samples. Then the two branches improve the representation and classification ability of the neural network by accumulating learning strategies. Finally, considering the geometric diversity of remote sensing images, this paper adopts test time augmentation and reflection padding strategies and proposes a balanced weighted loss function named CombineLoss to alleviate the imbalance in the training process. The depth neural network proposed in this paper was first tested on the Inria Aerial Image Labeling Dataset, and 87.53% of mean intersection over union and 97.4% of mean pixel accuracy were obtained, respectively. At the same time, to verify the model's complexity, the model proposed in this paper is compared with the neural network based on integrated learning. The comparison results show that the spatial complexity of the network proposed in this paper is much lower than the neural network obtained by integrated learning, and the parameters are also much smaller than the neural network based on integrated learning. Then use the satellite building dataset I in the WHU Building Dataset and mainstream semantic segmentation methods for multiple groups of comparative experiments. The experimental results show that the method proposed in this paper can effectively extract the semantic information of remote sensing images, significantly improve the imbalance of remote sensing image data, improve the performance of the network model, and achieve a good semantic segmentation effect, which fully proves the effectiveness of this method. [ABSTRACT FROM AUTHOR]

Genomic signal processing is a development field in medicine and agriculture. Numerous research areas are processing the genomics of living organism such as animals and particularly human beings. In this paper, the microarray data set for the biological organism which includes a large number of gene data has taken for the processing. The microarray data are a powerful technology practised in the research field for validating the gene discovery and diagnosis of diseases. The data are processed to a large number with plenty of genes. The proposed Kalman filter-based fuzzy c-means cluster and artificial neural network (KF-FANN) enhance the genomic signal processing to the optimal level. The Kalman filter proposed in this paper to remove the noise and smoothen the data for signal processing. An ideal clustering process is carried out for the classification of the microarray data. The fuzzy c-means clustering was proposed in this paper for grouping the microarray after removing the noise. The artificial neural network is a biologically inspired model proposed in this work for the classification of microarray data to point out the normal and abnormal genes in the microarray data. The proposed work has compared with existing techniques such as c-means, k-means clustering, and multi-SVM, respectively. The proposed method is carried out in the MATLAB platform, and results are evaluated in terms of Calinski–Harabasz index, separation index, Xie and Beni's index, partition index, accuracy, precision, recall, and F-score. The analysed result shows that the proposed KF-FANN is an efficient method for the classification of microarray data than existing approaches in genomic signal processing. [ABSTRACT FROM AUTHOR]

In the academic world, the number of scientists grows every year and so does the number of authors sharing the same names. Consequently, it is challenging to assign newly published papers to their respective authors. Therefore, author name ambiguity is considered a critical open problem in digital libraries. This paper proposes an author name disambiguation approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use data collected from the DBLP repository that contains more than 5 million bibliographic records authored by around 2.6 million co-authors. Our approach first groups authors who share the same last names and same first name initials. The author within each group is identified by capturing the relation with his/her co-authors and area of research, represented by the titles of the validated publications of the corresponding author. To this end, we train a neural network model that learns from the representations of the co-authors and titles. We validated the effectiveness of our approach by conducting extensive experiments on a large dataset. [ABSTRACT FROM AUTHOR]

With the establishment of the "double carbon" goal, various industries are actively exploring ways to reduce carbon emissions. Cloud data centers, represented by cloud computing, often have the problem of mismatch between load requests and resource supply, resulting in excessive carbon emissions. Based on this, this paper proposes a complete method for cloud computing carbon emission prediction. Firstly, the convolutional neural network and bidirectional long-term and short-term memory neural network (CNN-BiLSTM) combined model are used to predict the cloud computing load. The real-time prediction power is obtained by real-time prediction load of cloud computing, and then the carbon emission prediction is obtained by power calculation. Develop a dynamic server carbon emission prediction model, so that the server carbon emission can change with the change of CPU utilization, so as to achieve the purpose of low carbon emission reduction. In this paper, Google cluster data is used to predict the load. The experimental results show that the CNN-BiLSTM combined model has good prediction effect. Compared with the multi-layer feed forward neural network model (BP), long short-term memory network model (LSTM), bidirectional long short-term memory network model (BiLSTM), modal decomposition and convolution long time series neural network model (CEEMDAN-ConvLSTM), the MSE index decreased by 52 % , 50 % , 34 % and 45 % respectively. [ABSTRACT FROM AUTHOR]

The paper introduces a natural language processing system designed to answer natural language questions across knowledge graphs for scientific data applications where no prior training data in the form of question-answer pairs is available. 59 papers, submitted by 243 authors from over 20 countries/regions to SSDBM 2021, have showcased the pervasiveness of AI/ML in the field of scientific and statistical data management, which covered a variety of related topics (among others) including: In-database machine learning. Recent advancement in deep neural networks, combined with the high performance computing power and Big Data, has profoundly brought artificial intelligence (AI) to nearly all fields of scientific disciplines. [Extracted from the article]

In the paper "The Construction of Sports Culture Industry Growth Forecast Model Based on Big Data", the feasibility of sports culture industry growth prediction based on big data theory from the perspective of data mining is analyzed, and a sport culture industry growth prediction model based on genetic neural network is proposed. In the paper "Design of Sports Culture Data Fusion System based on Data Mining Algorithm", K-means spatial clustering, fusion decision tree, Naive Bayes and other data mining algorithms and data warehouse technologies are combined to the sports stationery industry. In the paper "Semantic Change Analysis of Korean Verbs based on Massive Culture Corpus Data", database technology, big data mining and analysis, on-line analytical processing technology and verb feature analysis technology are combined to construct the basic model. [Extracted from the article]

This paper introduces a diagnostic model that effectively diagnoses in fourteen different stages, by fusing functional magnetic resonance imaging (fMRI) and structural MRI (sMRI) information. Georgios Theodoridis and Athanasios Tsadiras from the Aristotle University of Thessaloniki, Greece, have authored the seventh paper which is entitled "Applying machine learning techniques to predict and explain subscriber churn of an online drug information platform." Machine learning (ML) and more specifically deep learning (DL) algorithms are considered among the most paramount technologies of both artificial intelligence (AI) and 4 SP th sp industrial revolution. This paper provides an in-depth comparison of various machine learning (ML) techniques and advanced preprocessing methods, in an effort to successfully perform online subscriber churn prediction. [Extracted from the article]

This paper explores the suitability of lip-based authentication as a behavioural biometric for mobile devices. Lip-based biometric authentication is the process of verifying an individual based on visual information taken from the lips while speaking. It is particularly suited to mobile devices because it contains unique information; its potential for liveness over existing popular biometrics such as face and fingerprint and lip movements can be captured using a device's front-facing camera, requiring no dedicated hardware. Despite its potential, research and progress into lip-based biometric authentication has been significantly slower than other biometrics such as face, fingerprints, or iris.This paper investigates a state-of-the-art approach using a deep Siamese network, trained with the triplet loss for one-shot lip-based biometric authentication with real-world challenges. The proposed system, LipAuth, is rigourously examined with real-world data and challenges that could be expected on lip-based solution deployed on a mobile device. The work in this paper shows for the first time how a lip-based authentication system performs beyond a closed-set protocol, benchmarking a new open-set protocol with an equal error rates of 1.65% on the XM2VTS dataset.New datasets, qFace and FAVLIPS, were collected for the work in this paper, which push the field forward by enabling systematic testing of the content and quantities of data needed for lip-based biometric authentication and highlight problematic areas for future work. The FAVLIPS dataset was designed to mimic some of the hardest challenges that could be expected in a deployment scenario and include varied spoken content, miming and a wide range of challenging lighting conditions. The datasets captured for this work are available to other university research groups on request. [ABSTRACT FROM AUTHOR]

This study attempts to determinate color changes based on time in inks applied on the surface of wood-free uncoated paper with offset printing during drying. This study consists of two main cases: (1) Experimental analysis: By preparing a test page according to the 12647-2 principle with an offset printing system, test prints were applied to 120 g/m wood-free uncoated paper using an ECI 2002 CMYK test chart. Each press was measured being subject to process every 15 min in the first 2 h, then hour by hour between 2 and 12 h, then 4-4 h between 12 and 24 h, and then 6-6 h between 24 and 48 h. CIELAB and reflectance values between 380 and 720 nm of the target, 1,485 colors of the test chart were obtained. To see the drying and color changes of the ink on paper, changes were determined by printing on the paper and applying artificial neural network (ANN) to spectrophotometer data at the stated time intervals. (2) Empirical analysis: The use of the ANN has been proposed as numerical approach to get of empirical equations of color changes in inks applied on the surface of wood-free uncoated paper with offset printing during drying. Based on the outputs of the study, ANN model can be used to estimate the effects of digital proofing systems used in color management on print quality with high confidence with the use of the acquired equations without experimental study. In the study, as colors are defined in terms of wave length, in case, all wave lengths are taken into consideration, certain wave length changes have been taken into consideration. [ABSTRACT FROM AUTHOR]

I Mathematical Geosciences i is fortunate to publish many outstanding research papers each year, which makes the selection of the Best Paper Award recipient a challenging undertaking. This year's awarded paper introduces a convolutional neural network reconstruction method to reconstruct high-resolution porous structures based on low-resolution µ-CT images and high-resolution scanning electron microscope (SEM) images. Because the SEM images are segmented and have a higher resolution than the µ-CT image, this algorithm integrates the super-resolution and segmentation processes. [Extracted from the article]

In the third paper, I Deep transfer learning: a novel glucose prediction framework for new subjects with Type 2 diabetes i , the authors designed a novel cross-subject glucose prediction framework by integrating instance-based and network-based deep transfer learning via segmented continuous glucose monitoring time series. Control and optimization Six papers have devoted to the computational intelligence-based decision-making and analysis of complex systems. Complex systems, which are composed of many interconnected and interactive functional parts, widely exist in the nature and human society. [Extracted from the article]

To verify the behavior of the system, the authors have used several publicly available datasets, obtaining satisfactory results. In this paper, the authors have presented a new CNN architecture based on the Ordinal Binary Decomposition (OBD) technique using Error Corrected Output Codes (ECOC) and have shown how it can improve performance over previously proposed methods. We are proud to present the set of final accepted papers for the Neural Processing Letters with contributions presented at the IWANNN conference - the International Work-Conference on Artificial Neural Networks- held online during June 16-18, 2021 (http://iwann.uma.es/). [Extracted from the article]

The model integrates artificial intelligence (AI) and big data analytics, utilizing IoMT devices for data acquisition and Hadoop ecosystem for managing big data. The field of medical diagnosis is currently undergoing a remarkable transformation with the emergence of artificial intelligence (AI) techniques, particularly deep learning and big data analytics. By harnessing the power of deep learning and big data analytics, AI-based e-diagnosis has the potential to revolutionize healthcare delivery. [Extracted from the article]

The Brazilian energy spot price is obtained through a chain of dynamic stochastic optimization models that works with the uncertainties related to its continental hydrogeneration-based power system. In that sense, this paper presents an information theoretic learning neural forecasting model for daily streamflow prediction of Brazilian hydroelectric power plants. More precisely, the maximum correntropy criterion was used as the error function of a multilayer perceptron. After the prediction stage, the generated outputs were used as one of the inputs of the model chain that is used to compute the hourly energy spot price in Brazil. To the best of the authors' knowledge, it is the first paper that aims to analyze the impact of the streamflow prediction on the Brazilian hourly energy spot price formation. In terms of streamflow forecast, results indicated that the predictions originated from the proposed forecasting model were equivalent to the ones from the official models, especially in the first predicted day. In the spot price graph analysis, the main result pointed that the curves provided from the modeled structure were closer to the values obtained using the actual flows than the official prices, which shows that the proposed work could produce prices more aligned to the real hydrological system conditions. From that, the study's relevance is due to the conclusion that the official process of streamflow forecasting can be improved to generated outputs more consistent with the actual system conditions, to avoid further expenses in the system operation due to potential unscheduled hydrothermal dispatches. [ABSTRACT FROM AUTHOR]

This paper presents an indoor positioning strategy based on Visible Light Communication that relies on LED luminaires as transmitters, whose luminous flux is modulated at different frequencies, and a light sensor as a receiver. Then, a previously trained Artificial Neural Network (ANN) uses the illuminance signal gathered by the receiver as input to estimate the sensor's position. The main contribution of the technique is that the ANN is trained by using an illuminance estimator based on the lighting distribution of the luminaires, which is obtained through the IES file provided by the luminaire's manufacturer, without the need to collect data from the environment. In this work, the designed illuminance estimator is validated by comparing it to the well-known commercial software DIALux and Relux. The algorithm's setting and the performance evaluation of the ANN are explained. Then, the impact of the lighting uniformity and the environment's number of divisions on the accuracy of the results is analyzed. Error analyses are also made by adding uncertainty to the illuminance measurements obtained by the sensor. Finally, the work is compared to several papers in the field. [ABSTRACT FROM AUTHOR]

Diclofenac sodium is an emerging contaminant that can be harmful for ecology and human health. This substance can be degraded by a heterogeneous Photo-Fenton process, CoFe2O4 as catalyst, H2O2 as oxidant and UV radiation. The aims of the work are the comparison of different artificial neural networks to characterize the relationship between diclofenac degradation and H2O2 consumption, with the Total Organic Carbon achieved in the mineralization of the drug and the testing of the selected model capacity to predict the Total Organic Carbon concentration, by employing the reused catalyst. The best performing backpropagation neural network was constituted with a ten neurons hidden layer with sigmoid transfer function and one linear neuron, as output. It was determined that the model can approximate the trend between the input data (Absorbance and H2O2 concentration) and output ones (Total Organic Carbon concentration) when it was validated with data from reactions employing CoFe2O4 for second and third time. The development of these models is of interest due to the consequent reduction of time and costs in experimental work. It represents a study of the evolution of chemical indicators in the treatment of emerging contaminants. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is the leading cause of death among women worldwide. It affects in general women older than 40 years old. Medical images analysis is one of the most promising research areas since it provides facilities for diagnosis and decision-making of several diseases such as BC. This paper conducts a Structured Literature Review (SLR) of the use of Machine Learning (ML) and Image Processing (IP) techniques to deal with BC imaging. A set of 530 papers published between 2000 and August 2019 were selected and analyzed according to ten criteria: year and publication channel, empirical type, research type, medical task, machine learning techniques, datasets used, validation methods, performance measures and image processing techniques which include image pre-processing, segmentation, feature extraction and feature selection. Results showed that diagnosis was the most used medical task and that Deep Learning techniques (DL) were largely used to perform classification. Furthermore, we found out that classification was the most ML objective investigated followed by prediction and clustering. Most of the selected studies used Mammograms as imaging modalities rather than Ultrasound or Magnetic Resonance Imaging with the use of public or private datasets with MIAS as the most frequently investigated public dataset. As for image processing techniques, the majority of the selected studies pre-process their input images by reducing the noise and normalizing the colors, and some of them use segmentation to extract the region of interest with the thresholding method. For feature extraction, we note that researchers extracted the relevant features using classical feature extraction techniques (e.g. Texture features, Shape features, etc.) or DL techniques (e. g. VGG16, VGG19, ResNet, etc.), and finally few papers used feature selection techniques in particular the filter methods. [ABSTRACT FROM AUTHOR]

This document is a preface to a special issue of the journal Optical Review, featuring review articles by the winners of the Optics Awards for Excellent Papers, given by the Optical Society of Japan (OSJ). The awards have been given since 1960, with 117 young researchers receiving them over 62 years. The two winning papers featured in this issue are "Three-dimensional deeply generated holography" by Dr. Ryoichi Horisaki and "Full-color-sorting metalenses for high-sensitivity image sensors" by Masashi Miyata and others. Dr. Horisaki's paper discusses a high-speed generation method of a computer-generated hologram using deep neural networks, while Dr. Miyata's paper focuses on a color image sensor integrated with metasurface lenses. The preface concludes by expressing hope that readers will enjoy the reviews and find inspiration for their own research. [Extracted from the article]

Overpressure is always considered as a severe problem in the oil industry. Besides creating life risks through serious accidents while drilling, failure to correctly identify high-pressure intervals causes a significant increase in costs and prolongs the drilling process. Kazhdumi Formation in the Abadan Plain is considered as a high-pressure formation in several wells. Various reasons can cause overpressure problem. Hydrocarbon generation is one cause of abnormal pressure in source rocks. Understanding hydrocarbon generation potential can be a helpful approach since the Kazhdumi Formation is considered as a probable source rock in this area. In this paper, in order to better understand the problem of abnormal pressure in Kazhdumi Formation, geochemical concepts and tools have been applied. To that way, 1D petroleum system modeling of five wells was done, and the thermal maturity level of Kazhdumi Formation was determined and then, compared with drilling records. The results indicate that in wells where this formation has sufficient organic matter and has reached an early mature level, there is an abnormal pressure problem. Otherwise, this formation does not show abnormal pressure. Also, geochemical data are not available in all drilled wells, which makes impossible the assessing of hydrocarbon generation role. Therefore, petrophysical well logs (sonic (DT), neutron (NPHI), density (RHOB), spectral gamma ray (SGR), and resistivity (RES)) as well as 137 sets of geochemical data belonging to 13 wells from 7 oilfields in the Abadan Plain were used to predict geochemical indicators. Using artificial neural networks, geochemical data of a well in Abadan Plain were predicted. This selected well has a high-pressure problem in Kazhdumi Formation, but no geochemical data are available in this well. The results of predicted geochemical data show that the high-pressure phenomenon in this well may also be due to hydrocarbon generation. The precise understanding of the abnormal pressure, resulting from hydrocarbons generation, requires comprehensive studies and a full investigation of the studied area. However, the results of this paper help to predict approximately the behavior of the source rocks before drilling. Acquiring this overview will aid in reducing drilling hazards and costs. [ABSTRACT FROM AUTHOR]

Purpose: In the winding process of chemical fiber production, the winding machine was responsible for winding the post-treated primary fiber. The winding machine clamp head was used as the direct winding part, and its running state would directly affect the quality and production efficiency of the chemical fiber cake. With the increasing complexity and precision of chemical fiber equipment, the traditional method of fault diagnosis relying on manual experience has been unable to meet the needs of intelligent production. The winding equipment also has some problems, such as few fault samples, strong background noise interference and large speed fluctuation. Therefore, the study of a better intelligent fault diagnosis method is of great significance to improve the winding reliability of the clamp head and ensure the quality and production efficiency of the chemical fiber cake. Methods: In this paper, a fault diagnosis method based on Empirical Mode Decomposition Order Spectrum of vibration signal and Dual-Channel Convolutional Neural Network (EMDOS-DCCNN) was proposed and the corresponding fault diagnosis model was designed. The trigger interval sampling scheme was designed according to the characteristics of the changing working conditions of the winder. The vibration data at the base of the winder's clamp head was collected by data acquisition equipment from Zhongli Winder in a chemical fiber production plant in Zhejiang Province as the real measured data for rolling bearing diagnosis. The strong background noise and the influence of the variable speed of the winder were removed by Empirical Mode Decomposition (EMD) reconstruction of the original signal and order spectrum analysis. The dual-channel convolutional neural network model was used for fault diagnosis, and the dual-channel data was fused to further improve the fault diagnosis accuracy. Order analysis and validation set confusion matrix were used to evaluate the diagnostic model proposed in this paper. Results and Conclusion: The model was trained with vibration data collected at three rotational speeds and verified on data sets collected at other rotational speeds. The results showed that the fault diagnosis model of rolling bearings based on EMDOS-DCCNN could effectively remove noise, and a single diagnosis model could be used to diagnose rolling bearings at different rotational speeds. In the model evaluation experiment, the accuracy of the dual-channel CNN model was stable at 98.6%, which was 3.1% higher than that of the single-channel model. It had high fault identification accuracy and adaptability at different speeds, and the model was suitable for variable working conditions and had better robustness. [ABSTRACT FROM AUTHOR]

De-mining operations are of critical importance for humanitarian efforts and safety in conflict-affected regions. In this paper, we address the challenge of enhancing the accuracy and efficiency of mine detection systems. We present an innovative Deep Learning architecture tailored for pulse induction-based Metallic Mine Detectors (MMD), so called DL-MMD. Our methodology leverages deep neural networks to distinguish amongst nine distinct materials with an exceptional validation accuracy of 93.5%. This high level of precision enables us not only to differentiate between anti-personnel mines, without metal plates but also to detect minuscule 0.2-g vertical paper pins in both mineralized soil and non-mineralized environments. Moreover, through comparative analysis, we demonstrate a substantial 3% and 7% improvement (approx.) in accuracy performance compared to the traditional K-Nearest Neighbors and Support Vector Machine classifiers, respectively. The fusion of deep neural networks with the pulse induction-based MMD not only presents a cost-effective solution but also significantly expedites decision-making processes in de-mining operations, ultimately contributing to improved safety and effectiveness in these critical endeavors. [ABSTRACT FROM AUTHOR]

The concept of tripartite synapses has revolutionized the world of neuroscience and the way we understand how information is transmitted in the brain. Since its discovery, some research groups have incorporated into connectionist systems classically focused on the development of Artificial Neuron Networks (ANNs) as a single element, artificial astrocytes that try to optimize performance in problem solving.In this systematic review, we searched the ISI Web of Science for papers that focused on the development of such novel models and their comparison with classical ANNs. A total of 22 papers that satisfied the inclusion criteria were analyzed, showing three different ways of applying the neuromodulatory influence of artificial astrocytes on neural networks. Using Multilayer Perceptron Networks, Artificial Neuro-Glial Newtworks and Multilayer Perceptron with Self-Organizing Maps approaches, a detailed analysis of the incorporation of artificial astrocytic networks has been carried out, and the main differences between the different methods have been weighed up. Regardless of the type of inclusion performed, the greater the complexity of the problem to be solved, it has been observed that the influence of artificial astrocytes has improved the performance of classical ANNs, as occurs in the biological brain. [ABSTRACT FROM AUTHOR]

This cannot be achieved only through conventional Machine Learning (ML) frameworks and technologies. The technological advancement in recent years has seen a great shift leading to a greater magnitude of data storage, communication and subsequent processing with reduced size and cost of the systems. The thirteen paper "A Novel Emergency Situation Awareness Machine Learning Approach to Assess Flood Disaster Risk based on Chinese Weibo" is authored by Bai Hua, Yu Hualong, Yu Guang and Huang Xing. [Extracted from the article]

The first issue of Artificial Intelligence and Law journal was published in 1992. This paper provides commentaries on landmark papers from the first decade of that journal. The topics discussed include reasoning with cases, argumentation, normative reasoning, dialogue, representing legal knowledge and neural networks. [ABSTRACT FROM AUTHOR]

This study investigates the impact of air pollution on health outcomes in Middle Eastern countries, a region facing severe environmental challenges. As such, these are important in an effort to add up to policy-level as well as interventional changes that can be put in practice in the area of public health. Numeration analysis and association with health parameters was carried out by using Analytical tools such as, AIR Data, ARIMA,ANN, SVM and Exponential smoothing. Amongst the models, Support Vector Machine came again on top, with high accuracy yielding Mean Absolute Percentage Error of approximately 1%. Mortality of Air pollution in Qat from the case of Mortality of Air Pollution in Qatar is 959 while Auto regressive Integrated Moving average is 11.096, Exponential Smoothing 9.892 and Artificial Neural Networks are the source of inspiration for the development of this paper 4.61. The above perceptions indicate that there is need to adapt modeling strategies depending on the context and establish that it is possible to implement ML models in public health planning basket. This paper publishes the methodological frameworks for the purpose of modeling and analysis of the EHDs and serves as policy prescription for the policy makers to intending to reduce the effects of air borne pollution on health. [ABSTRACT FROM AUTHOR]

This paper introduces a novel design for a universal DC-DC and DC-AC converter tailored for DC/AC microgrid applications using Approximate Dynamic Programming and Artificial Neural Networks (ADP-ANN). The proposed converter is engineered to operate efficiently with both low-power battery and single-phase AC supply, utilizing identical side terminals and switches for both chopper and inverter configurations. This innovation reduces component redundancy and enhances operational versatility. The converter's design emphasizes minimal switch usage while ensuring efficient conversion to meet diverse load requirements from battery or AC sources. A conceptual example illustrates the design's principles, and comprehensive analyses compare the converter's performance across various operational modes. A test bench model, rated at 3000W, demonstrates the converter's efficacy in all five operational modes with AC/DC inputs. Experimental results confirm the system's robustness and adaptability, leveraging ADP-ANN for optimal performance. The paper concludes by outlining potential applications, including microgrids, electric vehicles, and renewable energy systems, highlighting the converter's key advantages such as reduced complexity, increased efficiency, and broad applicability. [ABSTRACT FROM AUTHOR]

Growing popularity of self-service in retail stores and increasing associated shrinkage presents an urgent need for computer-vision-based product recognition in the area of self-checkouts. The article focuses on individual product recognition using automated workflow in images collected from retail store self-checkouts. The interest of this research lies exclusively in the recognition of barcodeless products—ones that present a challenge of being identified quickly and qualitatively at self-checkouts. Image sets representative of retail store product distribution do not exist as of the time of writing to the authors' knowledge. Images collected by exploiting self-checkout events often contain products partially covered by customer body parts, inside semi-transparent plastic bags, or not present in the area of interest. Due to the huge assortment of products that varies between stores and changes frequently, manual image labeling, filtering and long training time are unpractical. The proposed method investigates the need for automated steps to eliminate empty images and eliminate images where product visibility is unsatisfactory. Authors achieved 80.5±1.2% classification accuracy on a real-world dataset of 194 products using automatic workflow. The ablation studies proved the need for image filtering in both training and inference workflows. The neural network architecture tuned to the self-checkout dataset proved to outperform well-known networks: the suggested architecture's training time is a fraction of ImageNet's best EfficientNet and accuracy is slightly better. The suggested method generalization is proved on comparable products dataset Fruits 360, where 99.6% accuracy was achieved—comparable or better than other authors. [ABSTRACT FROM AUTHOR]

Scene classification and recognition have always been one of the most challenging tasks of scene understanding due to the inherent ambiguity in visual scenes. The core of scene classification and recognition tasks is scene representation. Deep learning advances in computer vision, especially deep CNNs, have significantly improved scene representation in the last decade. Deep convolutional features extracted from deep CNNs provide discriminative representations of the images and are widely used in various computer vision tasks, such as scene classification. Deep convolutional features capture the appearance characteristics of the image and the spatial information about different image regions. Meanwhile, the semantic and context information obtained from high-level concepts about scene images, such as objects and their relationships, can significantly contribute to identifying scene images. Therefore, in this paper, we divide visual scenes into two categories, object-based and layout-based. Object-based scenes are scenes that have scene-specific objects and, based on those objects, can be described and identified. In contrast, the layout-based scenes do not have scene-specific objects and are described and identified based on the appearance and layout of the image. This paper proposes a new neural network model for representing and classifying visual scenes, which we call G-CNN (GNN-CNN). The proposed model includes two modules, feature extraction and feature fusion, and the feature extraction module composes of visual and semantic branches. The visual branch is responsible for extracting deep CNN features from the image, and the semantic branch is responsible for extracting semantic GNN features from the scene graph corresponding to the image. The feature fusion module is a novel two-stream neural network that fuses the CNN and GNN feature vectors to produce a comprehensive representation of the scene image. Finally, a fully-connected classifier classified the obtained comprehensive feature vector into one of the pre-defined categories. The proposed model has been evaluated on three benchmark scene datasets, UIUC Sports, MIT67, and SUN397, and obtained classification accuracy of 99.91%, 96.01%, and 85.32%, respectively. In addition, a new dataset named Scene40, which has been introduced in our previous paper, is also used for further evaluation of the proposed method. The comparison results based on classification accuracy criteria show that the proposed model can outperform the best previous methods on three benchmark scene datasets. [ABSTRACT FROM AUTHOR]

In recent years, there has been a significant increase in the design of neural network models for solving math word problems (MWPs). These neural solvers have been designed with various architectures and evaluated on diverse datasets, posing challenges in fair and effective performance evaluation. This paper presents a comparative study of representative neural solvers, aiming to elucidate their technical features and performance variations in solving different types of MWPs. Firstly, an in-depth technical analysis is conducted from the initial deep neural solver DNS to the state-of-the-art GPT-4. To enhance the technical analysis, a unified framework is introduced, which comprises highly reusable modules decoupled from existing MWP solvers. Subsequently, a testbed is established to conveniently reproduce existing solvers and develop new solvers by combing these reusable modules, and finely regrouped datasets are provided to facilitate the comparative evaluation of the designed solvers. Then, comprehensive testing is conducted and detailed results for eight representative MWP solvers on five finely regrouped datasets are reported. The comparative analysis yields several key findings: (1) Pre-trained language model-based solvers demonstrate significant accuracy advantages across nearly all datasets, although they suffer from limitations in math equation calculation. (2) Models integrated with tree decoders exhibit strong performance in generating complex math equations. (3) Identifying and appropriately representing implicit knowledge hidden in problem texts is crucial for improving the accuracy of math equation generation. Finally, the paper also discusses the major technical challenges and potential research directions in this field. The insights gained from this analysis offer valuable guidance for future research, model development, and performance optimization in the field of math word problem solving. [ABSTRACT FROM AUTHOR]

This special issue of the circuits, systems and signal processing journal focuses on recent advances and improvements in leading-edge integrated machine learning algorithms and signal processing systems. Although signal processing has been studied over several decades, the computer industry is only beginning to understand how signal processing techniques can be well integrated for the development of human-machine interfaces with the advancement of machine learning algorithms. As advances in signal processing tools and machine learning algorithms are becoming more powerful in terms of functionality and communicative capabilities, their contribution to the journal of circuits, system and signal processing is becoming more significant. [Extracted from the article]

This special issue of the International Journal of Computer Vision (IJCV) presents the best papers of the 28th British Machine Vision Conference (BMVC) held in Imperial College London, UK, on 4th-7th September 2017. The papers presented in this issue offer a snapshot of some of the best work in the field, on the topics of (1) learning quantised representations by deep neural networks, (2) 3D shape estimation, (3) novel image representations, (4) image descriptors and matching, and (5) data generation. I Image descriptors and matching i by local descriptors or deep neural networks remains an active research topic. [Extracted from the article]

This document is an editorial discussing the development of 6G networks and the challenges and requirements they will face. The author emphasizes the need for research in areas such as autonomous systems, latency, vertical services, machine learning, trust and security, and quantum communications. The editorial also includes summaries of six research papers related to 6G networks, covering topics such as resource management, indoor positioning, resource allocation for connected cars, side-channel attacks, secure UAV communications, and reconfigurable intelligent surfaces. The authors of the editorial and the research papers come from various institutions and have expertise in communication and information systems, electronics and telecommunications engineering, electrical engineering, and computer science. [Extracted from the article]

This paper presents a new approach to survival analysis using topological data analysis (TDA) within Bayesian statistics combined with machine learning algorithms suitable to time-to-event data. The paper brings into the analysis aspects of topological invariance through what is known as persistence homology. TDA demonstrates the existence and statistical significance of a kind of unmeasured heterogeneity originating from the topology of the data as a whole. Combined with machine learning tools persistence homology provides us with new tools to construct a rich set of ways to analyze data and build predictive models that are optimized using inherent topological invariants such as one-dimensional loops as regularization. Specifically, this paper incorporates persistent homology effects in different ways in the analysis of survival data through the technique of functional principal component analysis (FPCA): first, by using topological invariants converted into FPCA factors that shape Bayesian statistical analysis of time-to-event data; second, by using FPCA measures of topological invariants in regularizing the process of optimizing the data and the posterior distributions of the Bayesian estimation; three, by using FPCA factors of measures of topological invariants in machine learning algorithms and deep neural networks suitable for analyzing survival data as a way of going beyond usual parametric and semi-parametric models of survival analysis. The approach is illustrated through a running example of multi-frailty survival analysis of democracies in the period of 1950–2010. [ABSTRACT FROM AUTHOR]

Accurate prediction of sea level height is critically important for the government in assessing sea level risk in coastal areas. However, due to the nonlinear, time-varying and highly uncertain characteristics of sea level change data, sea level prediction is challenging. To improve the accuracy of sea level prediction, this paper uses a new swarm intelligence algorithm named the sparrow search algorithm (SSA), which can imitate the foraging behavior and antipredation behavior of sparrows, to determine optimal solutions. To avoid the algorithm falling into a local optimal situation, this paper integrates the sine–cosine algorithm and the Cauchy variation strategy into the SSA to obtain an algorithm named the SCSSA. The SCSSA is used to optimize the parameter values of the CNN-BiLSTM (convolutional neural network combined with bidirectional long short-term memory neural network) model; finally, a combined neural network model (named SCSSA-CNN-BiLSTM) is proposed. In this paper, the time series data of seven tidal stations located in coastal China are used for experimental analysis. First, the SCSSA-CNN-BiLSTM model is compared with the CNN-BiLSTM model to predict the time series data of SHANWEI Station. With respect to the training and test sets of data, the SCSSA-CNN-BiLSTM model outperforms the other models on all the evaluation metrics. In addition, the remaining six tide station datasets and five neural network models, including the SCSSA-CNN-BiLSTM model, are used to further study the performance of the proposed prediction model. Four evaluation indices including the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R2) are adopted. For six stations, the RMSE, MAE, MAPE and R2 of SCSSA-CNN-BiLSTM model are ranged from 20.9217 ~ 27.8427 mm, 9.4770 ~ 17.8603 mm, 0.1322% ~ 0.2482% and 0.9119 ~ 0.9759, respectively. The experimental analysis results show that the SCSSA-CNN-BiLSTM model makes effective predictions at all stations, and the prediction performance is better than that of the other models. Even though the combination of SCSSA algorithm may increase the complexity of the model, indeed the proposed model is a new prediction method with good accuracy and robustness for predicting sea level change. [ABSTRACT FROM AUTHOR]

With the ongoing integration of information technology and industrialization, strategic emerging industries are becoming an increasingly important force in guiding future economic and social development. As one of the strategic emerging industries' development priorities and a replacement for scarce labor resources, industrial robots will be widely used in labor-intensive industries. It is a contentious topic how to evaluate and improve robot knowledge education. There are modular features introduced in this paper, which builds a modular network for the evaluation of robot knowledge education quality and enhances the cognitive computing ability of artificial neural networks and their ability to process complex information. A modular neural network-based model of feature combination robot knowledge education quality evaluation is developed based on the three aspects of module division method, subnet structure selection, and feature combination output. The following are some of the paper's most important contributions: K-OD algorithm of density clustering optimized by K-means is proposed. Because of its high level of modularized partition simulating, this method has an excellent clustering effect, and it identifies the core points, boundary points, as well as outliers. Using K-OD algorithm, the calculation of density radius and threshold is optimized by using density clustering, which reduces the overall computational complexity. Find out how SOM neural networks learn from competition. SOM network's competition layer neuron weights are prone to falling into local optimal solutions, so an SASOM neural network with weight adjustment simulated by an annealing algorithm is proposed to address this issue. It is more accurate in terms of prediction and error, and it is better at identifying sample attribute features. This work builds a modular neural network for evaluating robot knowledge education quality using K-OD clustering algorithm and SASOM neural network, which introduces the simulated annealing mechanism. [ABSTRACT FROM AUTHOR]

Knowledge graph embedding converts knowledge graphs based on symbolic representations into low-dimensional vectors. Effective knowledge graph embedding methods are key to ensuring downstream tasks. Some studies have shown significant performance differences among various knowledge graph embedding models on different datasets. They attribute this issue to the insufficient representation ability of the models. However, what representation ability knowledge graph embedding models possess is still unknown. Therefore, this paper first selects three representative models for analysis: translation and rotation models in distance models, and the Bert model in neural network models. Based on the analysis results, it can be concluded that the translation model focuses on clustering features, the rotation model focuses on hierarchy features, and the Bert model focuses on word co-occurrence features. This paper categorize clustering and hierarchy as structure features, and word co-occurrence as semantic features. Furthermore, a model that solely focuses on a single feature will lead to a lack of accuracy and generality, making it challenging for the model to be applicable to modern large-scale knowledge graphs. Therefore, this paper proposes an ensemble model with structure and semantic features for knowledge graph embedding. Specifically, the ensemble model includes a structure part and a semantic part. The structure part consists of three models: translation, rotation and cross. Translation and rotation models serve as basic feature extraction, while the cross model enhances the interaction between them. The semantic part is built based on Bert and integrated with the structure part after fine-tuning. In addition, this paper also introduces a frequency model to mitigate the training imbalance caused by differences in entity frequencies. Finally, we verify the effectiveness of the model through link prediction. Experiments show that the ensemble model has achieved improvement on FB15k-237 and YAGO3-10, and also has good performance on WN18RR, proving the effectiveness of the model. [ABSTRACT FROM AUTHOR]

We are pleased to present a special issue of Data Science and Engineering (DSE), which contains a collection of eight extended papers from the ICDM 2019 conference. The eight extended papers in this special issue cover a variety of topics related to data science and engineering. The seventh paper "Contextual Sentiment Neural Network for Document Sentiment Analysis" proposes a novel neural network model that can explain the process of its sentiment analysis prediction in a way that humans find natural and agreeable and can catch-up the summary of the contents. [Extracted from the article]

Artificial intelligence (AI) is being employed not only in businesses, entertainment, and academia but also in sports training to raise the level of competition among athletes. Artificial neural networks (ANNs) are the brains of modern AI, and this paper examines their application in sports training's load prediction with the hope of improving athletic performance, creating scientifically efficient training programs, and increasing the accuracy of training load forecasts. First, a sports training analysis system based on Browser Server (B/S) is constructed, and the overall structure, specific function modules, and database of the system are designed and introduced. Then, the artificial fish swarm algorithm (AFSA)-based prediction and modeling approach of sports training's load is applied. To put it more specifically, we obtained a comprehensive index of joint acceleration in sports training by using data acquisition devices such as sensors. The independent variable in the load prediction is defined by the computational results, which are combined with other relevant features to obtain the load characteristics of sports training. The initial weights and thresholds of the ANN are optimized with the help of AFSA. To produce the final prediction result, the optimized ANN and extracted load characteristics are coupled with the prediction equation. The prediction model is applied to the sports training analysis system for realizing the application of training load prediction. Experiments show that the methods proposed in this paper can effectively improve the accuracy and efficiency of sports training and load prediction, improve the effect of athletes' training, and thus enhances the level of sports competitiveness. [ABSTRACT FROM AUTHOR]

The construction industry occupies a high proportion of the global economy. However, with the energy consumption of construction enterprises, it still brings a series of serious environmental problems. Construction enterprises should take active green development behavior to respond. Based on enterprises' green development behavior, this paper explores the influencing factors of green development behavior adopted by construction enterprises in China. Through literature analysis, this paper identifies that construction enterprises' adoption of green development behaviors is influenced by technological, organizational and environmental factors. Then this paper constructs an index system of the influencing factors of green development behaviors adopted by enterprises. According to the data of construction enterprises from 2000 to 2020 of National Bureau of Statistics, an artificial neural network is used to construct the prediction model of influencing factors of green development behavior adopted by construction enterprises. The conclusions are as follows. (1) Construction enterprises' adoption of green development behavior shows an upward trend over time. (2) Market share of construction enterprises is the most important factor to promote construction enterprises' adoption of green development behavior. (3) The prediction model of influencing factors constructed in this paper is verified to be effective by the technology-organization-environment framework. This paper provides a reference for construction enterprises and the government to promote enterprises to adopt green development behavior, which is beneficial for construction enterprises to achieve green development faster. [ABSTRACT FROM AUTHOR]