Model Advantage Research Articles

CrackTinyNet: A novel deep learning model specifically designed for superior performance in tiny road surface crack detection

AbstractWith the rapid advancement of highway construction, the maintenance of highway infrastructure has become particularly vital. During highway maintenance, the effective detection of tiny road surface cracks helps to extend the lifespan of roads and enhance traffic efficiency and safety. To elevate the performance of existing road detection models, the CrackTinyNet (CrTNet) algorithm is specifically proposed for detecting tiny road surface cracks. This algorithm utilizes the novel BiFormer general visual transformer, designed expressly for tiny objects, and optimizes the loss function to a normalized Wasserstein distance loss function. It replaces traditional downsampling with Space‐to‐Depth Conv to prevent the excessive loss of tiny object information in the network structure. To highlight the model's advantage in detecting tiny road cracks, ablation experiments and comparison trials were conducted with mainstream deep learning models for crack detection. The results of the ablation experiments show that, compared to the baseline, CrTNet improved the Mean Average Precision (MAP) by 0.22. When compared to other network models suitable for road detection, these results exhibited an improvement of over 8.9%. In conclusion, the CrTNet proposed in this study enables a more accurate detection of tiny road cracks, playing a significant role in the advancement of intelligent traffic management.

Predicting Drug-Disease Associations Through Similarity Network Fusion and Multi-View Feature Projection Representation.

Predicting drug-disease associations (DDAs) through computational methods has become a prevalent trend in drug development because of their high efficiency and low cost. Existing methods usually focus on constructing heterogeneous networks by collecting multiple data resources to improve prediction ability. However, potential association possibilities of numerous unconfirmed drug-related or disease-related pairs are not sufficiently considered. In this article, we propose a novel computational model to predict new DDAs. First, a heterogeneous network is constructed, including four types of nodes (drugs, targets, cell lines, diseases) and three types of edges (associations, association scores, similarities). Second, an updating and merging-based similarity network fusion method, termed UM-SF, is presented to fuse various similarity networks with diverse weights. Finally, an intermediate layer-mediated multi-view feature projection representation method, termed IM-FP, is proposed to calculate the predicted DDA scores. This method uses multiple association scores to construct multi-view drug features, then projects them into disease space through the intermediate layer, where an intermediate layer similarity constraint is designed to learn the projection matrices. Results of comparative experiments reveal the effectiveness of our innovations. Comparisons with other state-of-the-art models by the 10-fold cross-validation experiment indicate our model's advantage on AUROC and AUPR metrics. Moreover, our proposed model successfully predicted 107 novel high-ranked DDAs.

Design of optimal Elman Recurrent Neural Network based prediction approach for biofuel production

Renewable sources like biofuels have gained significant attention to meet the rising demands of energy supply. Biofuels find useful in several domains of energy generation such as electricity, power, or transportation. Due to the environmental benefits of biofuel, it has gained significant attention in the automotive fuel market. Since the handiness of biofuels become essential, effective models are required to handle and predict the biofuel production in realtime. Deep learning techniques have become a significant technique to model and optimize bioprocesses. In this view, this study designs a new optimal Elman Recurrent Neural Network (OERNN) based prediction model for biofuel prediction, called OERNN-BPP. The OERNN-BPP technique pre-processes the raw data by the use of empirical mode decomposition and fine to coarse reconstruction model. In addition, ERNN model is applied to predict the productivity of biofuel. In order to improve the predictive performance of the ERNN model, a hyperparameter optimization process takes place using political optimizer (PO). The PO is used to optimally select the hyper parameters of the ERNN such as learning rate, batch size, momentum, and weight decay. On the benchmark dataset, a sizable number of simulations are run, and the outcomes are examined from several angles. The simulation results demonstrated the suggested model's advantage over more current methods for estimating the output of biofuels.

Discrimination between Precancerous Gastric Lesions and Gastritis Using a Gastric Cancer Risk Stratification Model.

Seropositivity to certain Helicobacter pylori proteins may affect development of gastric lesions that could become cancerous. Previously, we developed a model of gastric cancer risk including gender, age, HP0305 sero-positivity, HP1564 sero-positivity, UreA antibody titer and serologically defined chronic atrophic gastritis (termed: "Lasso model"). We evaluated the Lasso model's ability to discriminate individuals with precancerous gastric lesions (n=320) from individuals with superficial or mild atrophic gastritis (n=226) in Linqu County, China, a population at high risk for gastric cancer. We also compared its performance to the ABC Method, a gastric cancer risk stratification tool currently used in East Asia. For distinguishing precancerous lesions from those with gastritis, the receiver operating characteristic curve had an area under the curve (AUC) of 73.41% (95% CI: 69.10%, 77.71%) and, at Youden's Index, a sensitivity of 78.44% (59.38%, 82.50%) and specificity of 64.72% (95% CI: 58.85%, 81.42%). Positive predictive value (PPV) was 75.38% (72.78%, 82.51%). Specificity, AUC and PPV were significantly greater (p < 0.05) than those of the ABC Method. When specificity was held constant, the Lasso model had greater sensitivity, PPV and negative predictive value (NPV) than the ABC Method. However, adjusting the ABC Method for age and gender negated the Lasso model's significant improvement in AUC. The Lasso model for gastric cancer risk prediction can classify precancerous lesions with significantly greater AUC than the ABC Method and, at constant specificity, with greater sensitivity, PPV and NPV. However, adding age and gender to the ABC Method, as included in the Lasso model, substantially improved its performance and negated the Lasso model's advantage.

Short-Term Origin-Destination Forecasting in Urban Rail Transit Based on Attraction Degree

Origin-destination (OD) forecasting is a difficult task in urban rail transit because many random factors can influence outcomes such as numerous OD pairs with few or even no flows. Therefore, treating all OD pairs equally, which is the method adopted by all existing studies, may not only increase model complexity and computation, but also negatively influence forecasting results. Therefore, in this study, we propose an indicator called OD attraction degree (ODAD) to address this problem in the field of OD forecasting. First, we introduce the ODAD indicator and five ODAD levels to describe the attraction between OD pairs. Based on the ODAD, an OD matrix pre-processing method is presented to prepare data for the LSTM model. Second, we use the mature long short-term memory (LSTM) network model to examine the effects of the introduction of ODAD. The LSTM model's advantage of dealing with variable-length sequences by leveraging the masking layer is creatively utilized. Finally, nine cases under different time granularities and different ODAD levels are thoroughly studied to explore their optimal combination. Based on this analysis, we recommend a time granularity of 30 min and an ODAD level of “Low” for actual subway operation. In this case, the root mean squared error, mean absolute error, and weighted mean absolute percentage error are 2.31%, 0.66%, and 27.28%, respectively, for a network of nearly 300 subway stations. The introduction of ODAD can provide critical insights for subway operators to conduct short-term OD forecasting.

Fast Model-based Protein Homology Discovery without Alignment

The need for quick gene categorization tools is growing as more genomes are sequenced. To evaluate a newly sequenced genome, the genes must first be identified and translated into amino acid sequences, which are then categorized into structural or functional classes. Protein homology detection using sequence alignment algorithms is the most effective way for protein categorization. Discriminative approaches such as support vector machines (SVMs) and position-specific scoring matrices (PSSM) derived from PSI-BLAST have recently been used to improve alignment algorithms. However, if a fresh sequence is being aligned, alignment algorithms take time. must be compared to a large number of previously published sequences — the same is true for SVMs. Building a PSSM for the PSSM is even more time-consuming than a fresh order It would take roughly 25 hours to implement the best-performing approaches to classify the sequences on today's computers. Describing a novel genome (20, 000 genes) as belonging to one single organism. There are hundreds of classes to choose from, though. Another flaw with alignment algorithms is that they do not construct a model of the positive class, instead of measuring the mutual distance between sequences or profiles. Only multiple alignments and hidden Markov models are common classification approaches for creating a positive class model, but they have poor classification performance. A model's advantage is that it may be evaluated for chemical features that are shared by all members of the class to get fresh insights into protein function and structure. We used LSTM to solve a well-known remote protein homology detection benchmark, in which a protein must be categorized as a member of the SCOP superfamily. LSTM achieves state-of-the-art classification performance while being significantly faster than other algorithms with similar classification performance. LSTM is five orders of magnitude quicker than the quickest SVM-based approaches and two orders of magnitude faster than methods that perform somewhat better in classification (which, however, have lower classification performance than LSTM). We applied LSTM to PROSITE classes and analyzed the derived patterns to test the modeling capabilities of the algorithm. Because it does not require established similarity metrics like BLOSUM or PAM matrices, LSTM is complementary to alignment-based techniques. The PROSITE motif was retrieved by LSTM in 8 out of 15 classes. In the remaining seven examples, alternative motifs are developed that, on average, outperform the PROSITE motifs in categorization.

Friction model and foreign exchange market intervention

The friction model is consistent with the hypothesis that a central bank intervenes in a foreign exchange market only if the necessity grows beyond certain thresholds. For this feature, the model is adopted in some recent studies as an attractive central bank reaction function. However, with official data on Federal Reserve and Bundesbank intervention, this paper shows that the friction model's advantage relative to a linear model may be negligible in terms of RMSE and MAE of in-sample fitting and out-of-sample forecasts. The implication is that intervention decisions are at the monetary authorities' discretion rather than dictated by a rule.

SOME FURTHER ANALYTICAL PROPERTIES OF THE CONSTANT CORRELATION MODEL FOR PORTFOLIO SELECTION

The constant correlation model is a mean-variance portfolio selection model where, for a given set of risky securities, the correlation of returns between any pair of different securities is considered to be the same. Support for the model is from previous empirical evidence that sample averages of correlations outperform various more sophisticated models in forecasting the correlation matrix, an important input component for portfolio analysis. To enable a better understanding of the constant correlation model, this study identifies some additional analytical properties of the model and relates them to familiar portfolio concepts. By comparing computational times for portfolio construction with and without simplifying the correlation matrix in a simulation study, this study also confirms the model's computational advantage. This study is intended to provide further analytical support for the model as a viable, simple alternative to those portfolio selection models where input requirements and the attendant computations are more burdensome.

Restoration of wavelet-compressed images

We investigate the characteristics of compression noise in images compressed by scalar quantization of the data's wavelet transform coefficients. Such quantization noise is both experimentally and theoretically shown to be spatially varying in the pixel domain, with statistical correlations between the errors at the pixel locations. A quantization covariance matrix is presented that can find use in general restoration scenarios where the observed image or images have been compressed by scalar quantization of image wavelet coefficients. Deblurring is presented as an example use of the quantization model, which demonstrates the model's advantage over the common assumption of independent and identically distributed noise.

空間-時間系モデルを用いた歩行者空間の混雑評価

The purpose of this study is to evaluate the pedestrian space with time series interfering area model. The model illustrates the interference of pedestrian's area in the several directions. And the coordinates of pedestrians are sensed and used for evaluation of the space. Pedestrian movement was recorded by the VCR camera and coded individual frames at sampling intervals of 0.5 seconds. The records were projected and corrected for foreshortened perspective. Then the coordinates of pedestrians in each frame were calculated. The pedestrian area is defined as the circle 0.9 meters in diameter in which pedestrian can avoid and pass others. When someone approaches another in pedestrian area, the conflict between two pedestrians occurs. The term "interfering load" can be defined as a load which pedestrians take in such a situation. There are two types of interfering load. One is a load between pedestrians in the same direction and the other is a load between pedestrians in the opposite direction. We only concerned with the Interfering load between bidirectional pedestrians, because monodirectional pedestrians reduce their walking load by forming groups in the same direction. The "time series interfering area model" describes the load between pedestrians in the several directions. This model's advantage is as follows; (1) The characteristics of pedestrian loads in a time series are grasped and comprehended with it. (2) As the congestion in the observation area in a time series is observed, and the area is evaluated particularly. (3) The situations of intersecting pedestrians in the several directions are grasped.

An evaluation of the QSP and the QPP: two methods for measuring patient satisfaction

Patient satisfaction is a function of several variables addressing reasons why it is important to use methods in which these different factors can be isolated and their importance analysed. In this project, two methods using this approach were used: the 'Quality from the Patient's Perspective' and the 'Quality, Satisfaction, Performance' models. The aim of the present study is to evaluate these two different methods with respect to application, strengths and weaknesses. In the Quality from the Patient's Perspective model, the patient judges the different domains in two dimensions: perceived reality and subjective importance. The Quality, Satisfaction, Performance model uses a multivariate analysis to capture the patient's priorities. Four hundred and sixty forms for each model were distributed to a random sample of patients at the Department of Obstetrics and Gynecology at Karolinska Hospital. The quality factors 'treatment by the nurse', 'participation', 'information', 'environment' and 'accessibility' were measured. On both forms, 'medical care', 'treatment by the doctor' and 'access to nursing treatment' received high scores in perceived reality' while 'accessibility' and 'participation' received low scores. 'Subjective importance' measured directly and indirectly, respectively, in the two models showed high values for 'medical care' and 'treatment by the doctor'. The advantages of the Quality from the Patient's Perspective model are that it has a comprehensive and solid question bank. The Quality, Satisfaction, Performance model's advantage is its immediate usefulness and its clear graphic presentation. An integration and further development of these two approaches may prove useful.

A202歩行者領域モデルを用いた群集流動効率の可視化

The purpose of this study is to evaluate the pedestrian space with time series interfering area model. The model illustrates the interference of pedestrian's area in the several directions. And the coordinates of pedestrians are sensed and used for evaluation of the space. The “time series interfering area model” describes the load between pedestrians in the several directions. This model's advantage is as follows; (1) The characteristics of pedestrian loads in a time series are grasped and comprehended with it. (2) As the congestion in the observation area in a time series is observed, and the area is evaluated particularly. (3) The situations of intersecting pedestrians in the several directions are grasped.

Estimating Development Stages of Corn from Spectral Data — An Initial Model1

AbstractWhen applying remote sensing technology to crop area estimation or crop condition assessment, a knowledge of the crop development stage at the time the remotely sensed data is acquired is important. In addition to historical statistics and weather driven development stage models, remotely sensed spectral data provides a potential tool for estimation of crop development stage. A model based upon spectral data should also provide advantages in terms of simplicity, timeliness, and applicability to areas as small as individual fields.In this investigation, a model for determining development stage of corn (Zea mays L.) from spectral measurements in the four Landsat bands of 0.5 to 0.6, 0.6 to 0.7, 0.7, to 0.8, and 0.8 to 0.11 &lt;m is proposed.Estimated corn development stages using the model were determined from Landsat data acquired over sample areas 01 the U.S. Corn Belt as well as agricultural research plots at the Purdue University Agronomy Farm, West Lafayette, Indiana. The estimated corn development stage using the model was compared to ground observations made with the Hanway scale (1966).Corn development stage estimates made from Landsat data had a correlation of R2 = 0.964 with the ground observed stages, being most accurate at stages earlier than Hanway Stage 6.0 (blister). Analysis of spectral data obtained from corn research plots at Purdue Agronomy Farm revealed that the model worked well with variation in plant population and soil background differences as well as arbitrary division into grain yield levels due to various experimental conditions. For all treatment variations of soil type and plant population and yield groupings, the R2 of calculated vs. observed development stage exceeded 0.95. This model's advantage for remote sensing is that it uses only spectral data and does not require meteorological data such as temperature, precipitation, and daylength. The model can be applied to individual corn fields rather than a general application to a region as large as crop reporting districts (or a state) and was found to work well for fields sampled over a large geographic area.