Dynamic Ensemble Associative Learning

$$K$$ -nearest neighbor (K-NN) graphs are an essential component of many established methods for content-based image retrieval and automated image annotation. The performance of such methods relies heavily on the semantic quality of the graphs, which can be measured as the proportion of neighbors sharing the same class label as their query images. Due to the noise in image features, the K-NN graphs produced by existing methods may suffer from low semantic quality. In this article, we propose NNF-Descent for the efficient construction of K-NN graphs based on nearest-neighbor and feature descent, in which selective sparsification of feature vectors is interleaved with neighborhood refinement operations in an effort to improve the semantic quality of the result. A variant of the Laplacian Score is proposed for the identification of noisy features local to individual images, whose values are then set to $$0$$ (the global mean value after standardization). We show through extensive experiments on several datasets that NNF-Descent is able to increase the proportion of semantically-related images over unrelated images within the neighbor sets, and that the proposed method generalizes well for other types of data which are represented by high-dimensional feature vectors.

Hyperspectral inversion of soil organic matter based on improved ensemble learning method.

In this paper, several ensemble cancer survivability predictive models are presented and tested based on three variants of AdaBoost algorithm. In the models we used Random Forest, Radial Basis Function Network and Neural Network algorithms as base learners while AdaBoostM1, Real AdaBoost and MultiBoostAB were used as ensemble techniques and ten other classifiers as standalone models. There has been major research in ensemble modelling in statistics, medicine, technology and artificial intelligence in the last three decades. This might be because of the effectiveness and reliability of the technique in medical diagnosis and incident predictions compare with the standalone classifiers. We used Wisconsin breast cancer dataset in training and testing the models. The performances of the ensemble and standalone models were evaluated using Accuracy, RMSE and confusion matrix predictive parameters. The result shows that despite the complexity of the ensemble models and the required training time, the models did not outperform most of the standalone classifiers.

Agriculture plays a predominant role in the economic growth and development of the country. The major and serious setback in the crop productivity is that the farmers do not choose the right crop for cultivation. In order to improve the crop productivity, a crop recommendation system is to be developed that uses the ensembling technique of machine learning. The ensembling technique is used to build a model that combines the predictions of multiple machine learning models together to recommend the right crop based on the soil specific type and characteristics with high accuracy. The independent base learners used in the ensemble model are Random Forest, Naive Bayes, and Linear SVM. Each classifier provides its own set of class labels with an acceptable accuracy. The class labels of individual base learners are combined using the majority voting technique. The crop recommendation system classifies the input soil dataset into the recommendable crop type, Kharif and Rabi. The dataset comprises of the soil specific physical and chemical characteristics in addition to the climatic conditions such as average rainfall and the surface temperature samples. The average classification accuracy obtained by combining the independent base learners is 99.91%.

Remote sensing using unmanned aerial vehicles (UAV) combined with machine learning (ML) has significantly advanced field-scale prediction of aboveground biomass. Although ensemble learning frameworks (ELFs) typically outperform individual ML algorithms in accuracy, systematic evaluations of meta learner selection and the effects of base learner quantity and diversity remain limited. Leveraging vegetation indices and texture features extracted from multi-temporal UAV imagery, ELFs were constructed incorporating nine ML algorithms with three meta learners, Linear model, Random forest and Bayesian model averaging (BMA), to systematically evaluate how base learner configuration affects prediction accuracy. Using the fused feature set of sensitive vegetation indices and texture features, Gaussian process regression (GPR) achieved the highest accuracy among all base learners, with R2 = 0.769 and RMSE = 1.83 t·ha–1. Also, the three meta learners outperformed the best base learner, with the BMA meta learner yielding the superior accuracy (R2 = 0.795, RMSE = 1.73 t·ha–1). However, meta learner performance depended strongly on the composition of the base learners pool, stability was optimal with five base learners, and maximum accuracy was achieved by hybrid ensembles that combined linear-kernel models with GPR. This study highlights the importance of both meta learner selection and base learner composition in ELFs for aboveground biomass prediction in rice. These findings offer methodological guidance for UAV-based high-precision monitoring of crop AGB, with practical implications for precision agriculture and crop management.

Background: Retrieval of facial images based on its contents is one of the main areas of research. However, images contain high dimensional feature vectors and it is a challenging task to select the relevant features due to the variations available in the images of similar objects. Therefore, the selection of relevant features is an important step to make the facial retrieval system computationally efficient and more accurate. Objective: The main aim of this paper is to design and develop an efficient feature selection method for obtaining relevant and non-redundant features from the face images so that the accuracy and computational cost of a face retrieval system can be improved. Methods: The proposed feature selection method uses a new enhanced grasshopper optimization algorithm to obtain the significant features from the high dimensional features vector of face images. The proposed algorithm modifies the target vector by considering more than one best solution which maintain the elitism property and save the search from local optimum. Furthermore, it has been utilized to select the prominent features from the high dimensional facial features vector. Results: The performance of the proposed feature selection method has been tested on Oracle Research Laboratory face database. The proposed method eliminates 89% features which are minimum among the other methods and increases the accuracy of face retrieval system to 96.5%. Conclusion: The enhanced grasshopper optimization algorithm-based feature selection method for face retrieval system outperforms the existing methods in terms of accuracy and computational cost.

Dimensionality reduction of image features using the canonical contextual correlation projection

Position detection of elements, especially with high-precision and high-efficiency, continue to present challenges in the off-axis three-mirror space optical system to ensure the imaging quality. To test the position of elements in an off-axis three-mirror system, a method based on ResNet50 and Long short-term memory (LSTM) is proposed in this paper. In the proposed method, point spread function (PSF) with different fields of view is extracted as a high dimensional feature vector by the ResNet50 network to achieve high efficiency. The same position information in the high dimensional feature vector from different image plane is obtained by the LSTM module to achieve high precision. To evaluate the performance of the proposed method, three examples of position detection were conducted. The results indicated that the probabilities of achieving detection accuracy better than 10 µm for eccentricity and 10'' for tilt, across single-dimensional variation of components, multi-dimensional random variation, and detector variation, are 100%, 94%, and 100%, respectively. Unlike the other detection method, the positions of elements are obtained with only one calculation and are close to the final results, which saves both time and resources for adjustment. The findings indicate that the method is an accurate and effective method for detecting position of elements in off-axis three-mirror space optical system.

Robotic end-effectors in automated production lines are specially designed and built for a unique task and for a particular part. An end-effector capable of grasping different parts with different geometries will expand its benefit and reduce costs. This work focuses on the grasp of sheet metal parts for the automotive industry. In order to maximize the use of a single end-effector, this paper proposes a search algorithm for a simple common grasp configuration. Such configuration imply for an end-effector design capable of grasping a set of sheet metal parts. The algorithm maps possible grasps which are candidate to be common. We define a novel quality measure estimating the distribution of the contacts across the sheet metal part. Candidate grasps which have a sufficient quality are mapped into high-dimensional feature vectors. These feature vectors parameterize the geometry of the polyhedron formed by the contacts locations and the direction of the surface normals relative to the polyhedron. Thereby, they describe the design of the end-effector compatible to the grasp. A database is generated for all possible grasps for each part in the feature vector space. A similarity join based on nearest-neighbor search and classification algorithm are used for intersecting all possible feature vectors over all sheet metal parts and finding common ones. Simulations of a 3-finger grasp on four meshed sheet metal parts resulted in a common grasp. Results of the simulations validate the feasibility of the proposed algorithm.

The goal of the study is to increase the computation efficiency of the face recognition that uses feature vectors to describe facial images on photos and videos. These high-dimensional feature vectors are nowadays produced by convolutional neural networks. The methods to aggregate the features generated for each video frame are used to process the video sequences. A novel hierarchical recognition algorithm is proposed. In contrast to known approaches our algorithm seeks the nearest neighbors only among reference images of most reliable classes selected at the preceding stage to carry out the sequential analysis of a more detailed description level (with a greater dimensionality of the feature vector). At each stage principal components are compared, the number of the components being chosen according to a given portion of explained variations. Datasets like Labeled Faces in the Wild, YouTubeFaces, IARPA Janus Benchmark C and different neural-net face descriptors are used to compare the algorithm with other methods. In contrast with the conventional nearest-neighbor method, the proposed approach is shown to allow a 2- to 16-times reduction of the classifier running time.

Ear based identity recognition subject to uncontrolled conditions such as illumination changes, pose variation, low contrast, partial occlusion and noise, is an active research area in the field of biometrics. Meanwhile, multimodal biometrics is becoming increasingly popular and offers improved performance due to the use of multiple sources of information. In this paper, a multimodal biometrics system is proposed based on the ear and profile face that not only alleviates the short-comings of ear biometrics but also improves the overall recognition rate. The ear and profile face modalities are first represented individually using the combination of two efficient local feature descriptors namely, local phase quantization (LPQ) and local directional patterns (LDP). These histogram-based local descriptors are then combined into a high-dimensional feature vector that preserves complementary information in both frequency and spatial domains. The PCA along with the z-score normalization technique is independently applied on each feature vector and the resultant reduced feature vectors are combined at the feature level. The kernel discriminative common vector (KDCV) approach is finally exploited over the combined feature set to derive more discriminative and non-linear features for the identification of individuals using kNN classifier. The effectiveness of the proposed model has been verified with the deep features derived from three popular pre-trained CNN models such as AlexNet, VGG16 and GoogleNet. Experimental results on two benchmark databases clearly show that the proposed approach achieves better performance than individual modality and other state-of-the-art methods.

In existing ensemble learning algorithms (e.g., random forest), each base learner’s model needs the entire dataset for sampling and training. However, this may not be practical in many real-world applications, and it incurs additional computational costs. To achieve better efficiency, we propose a decentralized framework:Multi-Agent Ensemble.The framework leverages edge computing to facilitate ensemble learning techniques by focusing on the balancing of access restrictions (small sub-dataset) and accuracy enhancement. Specifically, network edge nodes (learners) are utilized to model classifications and predictions in our framework. Data is then distributed to multiple base learners who exchange data via an interaction mechanism to achieve improved prediction. The proposed approach relies on a training model rather than conventional centralized learning. Findings from the experimental evaluations using 20 real-world datasets suggest that Multi-Agent Ensemble outperforms other ensemble approaches in terms of accuracy even though the base learners require fewer samples (i.e., significant reduction in computation costs).

Urban traffic congestion is a rapidly rising challenge in today’s cities, which result in longer travel times, increased pollution levels, and economic drawbacks. Traditional traffic control methods, which often rely on fixed rules, struggle to handle the dynamic and complex nature of modern traffic. In this study, we explore how machine learning can be used to classify traffic congestion levels more effectively using real-time data. We experimented with several models—like Support Vector Machines (SVM), Random Forests, Gradient Boosting, Deep Neural Networks, and various ensemble techniques. Our findings show that ensemble models, which combine the strengths of multiple classifiers, perform better in terms of accuracy and reliability compared to individual models. We also look at recent advancements in deep learning, including artificial neural networks (ANNs), gated recurrent units (GRUs), and hybrid models, to see how they enhance congestion prediction. We also evaluate how well these deep learning solutions perform in scenarios set in real world by looking at key factors like computational efficiency, feasibility for real-time use, and how well they scale. This provides a more complete picture of their potential in smart traffic management. Putting aside academic interest, applying machine learning to real time traffic classification has significant real world value, offering practical solutions to everyday urban mobility challenges.. With cities slowly becoming complex systems, traffic management needs to be better,more agile, responsive, and data-driven. This paper bridges that gap by combining the usual machine learning methods with deep learning and ensemble approaches. Besides classification, these models open up the path towards smarter decision-making in urban planning. It also addresses how developments in sensor technologies, IoT, and federated learning paradigms are reshaping the future of traffic analytics, while preserving data privacy without sacrificing real-time model flexibility. By identifying gridlocks, predicting congestion trends, and optimizing route management, these models can actively reduce emissions and travel delays, contributing to a more sustainable urban future. The experimental evaluation revealed that among all tested algorithms, the Artificial Neural Network (ANN) with the Swush activation function achieved the highest classification accuracy of 99.79% outperforming ensemble and traditional machine learning models. The findings highlight the feasibility of deploying such models in real-time traffic management systems to optimize routing and minimize congestion-related emissions.

In statistical pattern recognition, it is important to estimate true distribution of patterns precisely to obtain high recognition accuracy. Normal mixtures are sometimes used for representing distributions. However, precise estimation of the parameters of normal mixtures requires a great number of sample patterns, especially for high dimensional vectors. For some pattern recognition problems, such as face recognition, very high dimensional feature vectors are necessary and there are always not enough training samples compared with the dimensionality. We present a method to estimate the distributions based on normal mixtures with small number of samples. The proposed algorithm is applied to face recognition problem which requires high dimensional feature vectors. Experimental results show the effectiveness of the proposed algorithm.

Smart homes integrate several sensors to facilitate information exchange and the execution of tasks. In addition, with the development of the Internet of Things (IoT) platforms, the control of appliances and remote devices has become possible. This sensor collects data in real time to closely monitor the devices of a user’s household. The present study employs a machine learning methodology to perform a global analysis of energy consumption and efficiency in smart homes. In This work we propose two advanced ensemble models to improve the performance of energy consumption in smart homes, the first one is a voting ensemble model based on a ranking weight averaging that combines following basic machine learning techniques: decision tree (DT), random forest (RF), and eXtreme Gradient Boosting (XGB). The second one is the stacking ensemble model in which the basic models (DT-RF-XGB) are combined through stacked generalization, then uses a secondary layer model or meta-learner (RF) to provide output prediction. The findings obtained show that the proposed ensemble model based on DT-RF-XGB using stacking technique surpasses all other basic algorithms with R2 around 0.9825.