Previous Related Models Research Articles

Curriculum in conflict: influences of Australian teacher’s decision-making for students with complex disabilities

ABSTRACT This paper is an exploratory study of the personal and systematic influences over the curriculum decision-making process of teachers of students with severe or profound and multiple learning difficulties in Australia. The way in which these influences impact teacher perceptions of the mandated curriculum and its day-to-day operationalization are discussed. The paper draws from both a national survey (n = 46) and in-depth case studies (n = 5) of teachers who work in government-funded specialist educational settings. Findings show that teachers have prominent levels of emotionality, a drive for creativity and dedication to inclusive practice, but ultimately work within a context perceived through conflict and dilemma. Previous related decision-making models are used to compare results, with some distinct differences emerging. Findings indicate that rather than enabling inclusion, standardized curriculums create an environment of professional (self)exclusion and reduce access to professional support. Increases in responsibility burden and negative emotional effects are explored. Although teachers view access to standardized curriculum programs as vital to educational inclusion and the enhancement of their professional expertise, the utilization of the current offering in Australia is makeshift at best and fraught with internal conflict. The implications of these findings are discussed, and future recommendations for research are prioritized.

Identifying Spatial Determinants of Rice Yields in Main Producing Areas of China Using Geospatial Machine Learning

Rice yield is essential to global food security under increasingly frequent and severe climate change events. Spatial analysis of rice yields becomes more critical for regional action to ensure yields and reduce climate impacts. However, the understanding of the spatially varied geographical, climate, soil, and environmental factors of rice yields needs to be improved, leading to potentially biased local rice yield prediction and responses to climate change. This study develops a spatial machine learning-based approach that integrates machine learning and spatial stratified heterogeneity models to identify the determinants and spatial interactions of rice yields in the main rice-producing areas of China, the world’s largest rice-producing nation. A series of satellite remote sensing-derived variables are collected to characterize varied geographical, climate, soil, and environmental conditions and explain the spatial disparities of rice yields. The first step is to explore the spatial clustering patterns of the rice yield distributions using spatially global and local autocorrelation models. Next, a Geographically Optimal Zones-based Heterogeneity (GOZH) model, which integrates spatial stratified heterogeneity models and machine learning, is employed to explore the power of determinants (PD) of individual spatial variables in influencing the spatial disparities of rice yields. Third, geographically optimal zones are identified with the machine learning-derived optimal spatial overlay of multiple geographical variables. Finally, the overall PD of various variables affecting rice yield distributions is calculated using the multiple variables-determined geographically optimal zones and the GOZH model. The comparison between the developed spatial machine learning-based approach and previous related models demonstrates that the GOZH model is an effective and robust approach for identifying the spatial determinants and their spatial interactions with rice yields. The identified spatial determinants and their interactions are essential for enhancing regional agricultural management practices and optimizing resource allocation within diverse main rice-producing regions. The comprehensive understanding of the spatial determinants and heterogeneity of rice yields of this study has a broad impact on agricultural strategies and food security.

Prediction of MicroRNA-Disease Potential Association Based on Sparse Learning and Multilayer Random Walks.

More and more studies have shown that microRNAs (miRNAs) play an indispensable role in the study of complex diseases in humans. Traditional biological experiments to detect miRNA-disease associations are expensive and time-consuming. Therefore, it is necessary to propose efficient and meaningful computational models to predict miRNA-disease associations. In this study, we aim to propose a miRNA-disease association prediction model based on sparse learning and multilayer random walks (SLMRWMDA). The miRNA-disease association matrix is decomposed and reconstructed by the sparse learning method to obtain richer association information, and at the same time, the initial probability matrix for the random walk with restart algorithm is obtained. The disease similarity network, miRNA similarity network, and miRNA-disease association network are used to construct heterogeneous networks, and the stable probability is obtained based on the topological structure features of diseases and miRNAs through a multilayer random walk algorithm to predict miRNA-disease potential association. The experimental results show that the prediction accuracy of this model is significantly improved compared with the previous related models. We evaluated the model using global leave-one-out cross-validation (global LOOCV) and fivefold cross-validation (5-fold CV). The area under the curve (AUC) value for the LOOCV is 0.9368. The mean AUC value for 5-fold CV is 0.9335 and the variance is 0.0004. In the case study, the results show that SLMRWMDA is effective in inferring the potential association of miRNA-disease.

Complex Mountain Road Extraction in High-Resolution Remote Sensing Images via a Light Roadformer and a New Benchmark

Mountain roads are of great significance to traffic navigation and military road planning. Extracting mountain roads based on high-resolution remote sensing images (HRSIs) is a hot spot in current road extraction research. However, massive terrain objects, blurred road edges, and sand coverage in complex environments make it challenging to extract mountain roads from HRSIs. Complex environments result in weak research results on targeted extraction models and a lack of corresponding datasets. To solve the above problems, first, we propose a new dataset: Road Datasets in Complex Mountain Environments (RDCME). RDCME comes from the QuickBird satellite, which is at an elevation between 1264 m and 1502 m with a resolution of 0.61 m; it contains 775 image samples, including red, green, and blue channels. Then, we propose the Light Roadformer model, which uses a transformer module and self-attention module to focus on extracting more accurate road edge information. A post-process module is further used to remove incorrectly predicted road segments. Compared with previous related models, the Light Roadformer proposed in this study has higher accuracy. Light Roadformer achieved the highest IoU of 89.5% for roads on the validation set and 88.8% for roads on the test set. The test on RDCME using Light Roadformer shows that the results of this study have broad application prospects in the extraction of mountain roads with similar backgrounds.

Melanoma Detection Using XGB Classifier Combined with Feature Extraction and K-Means SMOTE Techniques.

Melanoma, a very severe form of skin cancer, spreads quickly and has a high mortality rate if not treated early. Recently, machine learning, deep learning, and other related technologies have been successfully applied to computer-aided diagnostic tasks of skin lesions. However, some issues in terms of image feature extraction and imbalanced data need to be addressed. Based on a method for manually annotating image features by dermatologists, we developed a melanoma detection model with four improvement strategies, including applying the transfer learning technique to automatically extract image features, adding gender and age metadata, using an oversampling technique for imbalanced data, and comparing machine learning algorithms. According to the experimental results, the improved strategies proposed in this study have statistically significant performance improvement effects. In particular, our proposed ensemble model can outperform previous related models.

Ensemble Feed-Forward Neural Network and Support Vector Machine for Prediction of Multiclass Malaria Infection

Globally, recent research are focused on developing appropriate and robust algorithms to provide a robust healthcare system that is versatile and accurate. Existing malaria models are plagued with low rate of convergence, overfitting, limited generalization due to restriction to binary cases prediction, and proneness to local minimum errors in finding reliable testing output due to complexity of features in the feature space, which is a black box in nature. This study adopted a stacking method of heterogeneous ensemble learning of ArtificialNeural Network (ANN) and Support Vector Machine (SVM) algorithms to predict multiclass, symptomatic, and climatic malaria infection. ANN produced 48.33 percent accuracy, 60.61 percent sensitivity, and 45.58 percent specificity. SVM with Gaussian kernel function gave better performance results of 85.60 percent accuracy, 84.06 percent sensitivity, and 86.09 percent specificity. Consequently, to improve prediction performance, a stacking method was introduced to ensemble SVM with ANN. The proposed ensemble malaria model was tuned on different thresholds at a threshold value of 0.60, the ensemble model gave an optimum accuracy of 99.86 percent, sensitivity 100 percent, specificity 98.68 percent, and mean square error 0.14. The ensemble model experimental results indicated that stacked multiple classifiers produced better results than a single model. This research demonstrated the efficiency of heterogeneous stacking ensemble model on effects of climatic variations on multiclass malaria infection classification. Furthermore, the model reduced complexity, overfitting, low rate of convergence, and proneness to local minimum error problems of multiclass malaria infection in comparison to previous related models.

Disease and information spreading at different speeds in multiplex networks.

Nowadays, one of the challenges we face when carrying out modeling of epidemic spreading is to develop methods to control disease transmission. In this article we study how the spreading of knowledge of a disease affects the propagation of that disease in a population of interacting individuals. For that, we analyze the interaction between two different processes on multiplex networks: the propagation of an epidemic using the susceptible-infected-susceptible dynamics and the dissemination of information about the disease-and its prevention methods-using the unaware-aware-unaware dynamics, so that informed individuals are less likely to be infected. Unlike previous related models where disease and information spread at the same time scale, we introduce here a parameter that controls the relative speed between the propagation of the two processes. We study the behavior of this model using a mean-field approach that gives results in good agreement with Monte Carlo simulations on homogeneous complex networks. We find that increasing the rate of information dissemination reduces the disease prevalence, as one may expect. However, increasing the speed of the information process as compared to that of the epidemic process has the counterintuitive effect of increasing the disease prevalence. This result opens an interesting discussion about the effects of information spreading on disease propagation.

Discriminative low-rank projection for robust subspace learning

The robustness to outliers, noises, and corruptions has been paid more attention recently to increase the performance in linear feature extraction and image classification. As one of the most effective subspace learning methods, low-rank representation (LRR) can improve the robustness of an algorithm by exploring the global representative structure information among the samples. However, the traditional LRR cannot project the training samples into low-dimensional subspace with supervised information. Thus, in this paper, we integrate the properties of LRR with supervised dimensionality reduction techniques to obtain optimal low-rank subspace and discriminative projection at the same time. To achieve this goal, we proposed a novel model named Discriminative Low-Rank Projection (DLRP). Furthermore, DLRP can break the limitation of the small class problem which means the number of projections is bound by the number of classes. Our model can be solved by alternatively linearized alternating direction method with adaptive penalty and the singular value decomposition. Besides, the analyses of differences between DLRP and previous related models are shown. Extensive experiments conducted on various contaminated databases have confirmed the superiority of the proposed method.

Volatility and Variance Swap Using Superposition of the Barndorff-Nielsen and Shephard type Lévy Processes

The main goal of this paper is to model variance and volatility swap using superposition of Barndorff-Nielsen and Shephard (BN-S) type models. In particular, in this paper we propose superposition of Levy process driven by Γ(ν,α) and Inverse Gaussian distributions. Model performance is assessed on data not used to build the model (i.e., test data). It is shown that the prediction error rate for the models considered in this paper are much lower compared to those from previous related models. Moreover, it is shown that unlike previous related models which are restricted to stable markets, the present approach can be applied to both stable and unstable markets.

A strain-consistent elastic plate model with surface elasticity

A strain-consistent elastic plate model is formulated in which both initial surface tension and the induced residual stress are treated as finite values, and the exactly same strain expressions are consistently employed for both the surface and the bulk plate. Different than most of previous related models which follow the original Gurtin–Murdoch model and include some non-strain displacement gradient terms (which cannot be expressed in terms of the surface infinitesimal strains or the von Karman-type strains) in the surface stress–strain relations, the present model does not include any non-strain displacement gradient terms in the surface stress–strain relations. For a free elastic plate with in-plane movable edges, the present model predicts that initial surface tension exactly cancels out the induced residual compressive stress. On the other hand, if all edges are in-plane immovable, residual stress cannot develop in the plate and the initial surface tension causes a tensile net membrane force. In addition, the present model predicts that initial surface tension reduces the effective bending rigidity of the plate, while this reduction does not depend on Poisson ratio. In particular, self-buckling of a free elastic plate under tensile surface tension cannot occur unless the effective bending rigidity of plate vanishes or becomes negative.

Credit Rating Using Type-2 Fuzzy Neural Networks

Nowadays various new technologies such as artificial neural networks, genetic algorithms, and decision trees are used for modelling of credit rating. This paper presents design of credit rating model using a type-2 fuzzy neural networks (FNN). In the paper, the structure of the type-2 FNN is designed and its learning algorithm is derived. The proposed network is constructed on the base of a set of fuzzy rules that includes type-2 fuzzy sets in the antecedent part and a linear function in the consequent part of the rules. A fuzzy clustering algorithm and gradient learning algorithm are implemented for generation of the rules and identification of parameters. Effectiveness of the proposed system is evaluated with the results obtained from the simulation of type-2 FNN based systems and with the comparative simulation results of previous related models.

New Model for the Optimization of Runway Orientation

One of the most significant factors that affects the location of airport elements is the optimum runway orientation. In this study, a new model called Calculation of Optimum Runway Orientation (CORO) is developed with an approach different from the previous related models. All models previously developed to solve the problem of optimum runway orientation are based on the Federal Aviation Administration (FAA) wind rose methodology. Alternatively, in the new model, the number of wind observations is used directly in the calculations without grouping wind velocities; the use of wind percentages is also excluded. The new model is written in CSharp (C#) programming language, which can be operated within a Windows environment. The proposed model is tested and compared to existing methods by using two different numerical examples to ascertain its accuracy and reliability. Since the errors arising from partial coverage problem and the FAA’s assumption defined in wind rose methodology are eliminated, it can be argued easily that it is possible to obtain more accurate results from the new model.

Fuzzy wavelet neural network based on fuzzy clustering and gradient techniques for time series prediction

This paper presents the development of fuzzy wavelet neural network system for time series prediction that combines the advantages of fuzzy systems and wavelet neural network. The structure of fuzzy wavelet neural network (FWNN) is proposed, and its learning algorithm is derived. The proposed network is constructed on the base of a set of TSK fuzzy rules that includes a wavelet function in the consequent part of each rule. A fuzzy c-means clustering algorithm is implemented to generate the rules, that is the structure of FWNN prediction model, automatically, and the gradient-learning algorithm is used for parameter identification. The use of fuzzy c-means clustering algorithm with the gradient algorithm allows to improve convergence of learning algorithm. FWNN is used for modeling and prediction of complex time series and prediction of foreign-exchange rates. Exchange rates are dynamic process that changes every day and have high-order nonlinearity. The statistical data for the last 2 years are used for the development of FWNN prediction model. Effectiveness of the proposed system is evaluated with the results obtained from the simulation of FWNN-based systems and with the comparative simulation results of previous related models.

A New Model for Estimating Real-time Travel Time on a Signalized Arterial

A new model for estimating the real-time travel time on a signalized arterial is developed in this paper.The basic idea behind the proposed model is that the time horizon analyzed is first divided into multiple time windows,and these time windows are then further divided into shorter time intervals.In the paper,the arterial system states are defined as whether the vehicles at the stopping line of an intersection queue or not.After the determination of the arterial system states at each time interval within each time window,a stochastic process with memoryless property is then established. Consequently,the travel time on a signalized arterial within a time window can be estimated by using the discrete-time Markovian decision process(DTMDP).This process is applied repeatedly to each time window,and the real-time travel time on the signalized arterial over the time horizon is then obtained.Compared to the previous related models,the model's inputs only include the data of traffic flow and signal setting,the proposed model's parameters are less and easier to calibrate,the cost of model application is low,and the model has a good transplant property as well.Finally,the model proposed in this paper is validated on an actual signalized arterial in Guangzhou.

Fuzzy wavelet neural network for prediction of electricity consumption

AbstractThe development of a fuzzy wavelet neural network (FWNN) for the prediction of electricity consumption is presented. The fuzzy rules that contain wavelets are constructed. Based on these rules, the structure of FWNN-based system is described. The FWNN system is applied for modeling and prediction of complex time series. The gradient algorithm and genetic algorithm are used for learning of FWNN parameters. The developed FWNN is applied for prediction of electricity consumption. This process has high-order nonlinearity. The statistical data for the last 10 years are used for the development of FWNN prediction model. The effectiveness of the proposed system is evaluated with the results obtained from the simulation of FWNN-based prediction system and with the comparative simulation results of previous related models.

Towards self-authenticable smart cards

Traditionally, the smart cards have been seen as security devices, but as soon as they could be integrated into distributed and networked environments their vulnerabilities could be attempted and countermeasures against new security threats in an open-access internet were required. In this work, our target could be represented by an end-to-end mutual authentication scenario where the smart card could authenticate by itself to a Network Access Server by means of link layer protocols and therefore in absence of IP connectivity. Some previous related models based on the Extensible Authentication Protocol are presented. However, in these works the smart card and terminal implement jointly the supplicant functionality (split supplicant). We consider the native EAP multiplexing model specified by the IETF to propose a new approach in order to avoid this split and to achieve an autonomous and highly independent smart card in the authentication scheme: a self-authenticable smart card.

Semantic Analyzer of Schemata Organization (SASO)

A menu-driven software system was developed to implement schemata organization in a constraint-satisfaction neural network. The current neural-net model present several advantages and modifications over previous related models. On the one hand, the schemata behavior shown by the present model is based on conceptual definitions and ratings obtained empirically from human subjects rather than on the idiosyncratic knowledge database of a single programmer. On. the other hand, this property is most valuable to experimental research in which the current knowledge status of the subjects is critical for the experiments (e.g., word-recognition experiments).

A topological model for two-dimensional gravity coupled to matter

Starting from a topological gauge theory in two dimensions with symmetry groups ISO(2, 1), SO(2, 1) and SO(1, 2) we construct a model for gravity with non-trivial coupling to matter. We discuss the equations of motion which are connected to those of previous related models but incorporate matter content. We also discuss the resulting quantum theory and finally present explicit formulæ for topological invariants.

Modeling the behavioral substrates of associate learning and memory: adaptive neural models

Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive/learning systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Furthermore, each model can find the most nonredundant and earliest predictor of reinforcement. The behavior of the models accounts for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well the models fit empirical data from various animal learning paradigms. >