Attributed grammars in hypnogram analysis

Capturing images in thousands of contiguous spectral bands has been made simpler with the emergence of technology in the field of hyperspectral remote sensing. Despite of these huge data available for analysis, Hyperspectral images (HSI) face many challenges due to high dimensionality, noise, spectral mixing and computational complexity. Several preprocessing methods can be used to overcome the above mentioned issues. In this paper, an enhancement technique using 2D-Empirical Wavelet Transform (EWT) is used as a preprocessing step for the HSI reconstruction prior to sparsity based classification (Subspace Pursuit and Orthogonal Matching Pursuit). The effectiveness of the proposed method is proved by comparing the classification results obtained with and without applying preprocessing. Experimental analysis shows a significant improvement in the classification accuracies i.e., for 40 $$\%$$ of training samples, OMP shows an improvement in overall classification accuracy from 66.12 $$\%$$ to 93.20 $$\%$$ and SP shows an improvement from 66.36 $$\%$$ to 92.74 $$\%$$ .

Application of neural machine interface in individuals with chronic hemiparetic stroke is regarded as a great challenge, especially for classification of the hand opening and grasping during a functional upper extremity movement such as reach-to-grasp. The overall accuracy of classifying hand movements, while actively lifting the paretic arm, is subject to a significant reduction compared to the accuracy when the arm is fully supported. Such a reduction is believed to be due to the expression of flexion synergy, which couples shoulder abduction (SABD) with elbow/wrist and finger flexion, and is common in up to 60% of the stroke population. Little research has been done to develop methods to reduce the impact of flexion synergy on the classification of hand opening and grasping. In this study, we proposed a novel approach to classify hand opening and grasping in the context of the flexion synergy using a wavelet coherence-based filter. We first identified the frequency ranges where the coherence between the SABD muscle and wrist/finger flexion muscles is significant in each participant, and then removed the synergy-induced electromyogram (EMG) component with a subject-specific and muscle-specific coherence-based filter. The new approach was tested in 21 stroke individuals with moderate to severe motor impairments. Employing the filter, 14 participants gained improvement in classification accuracy with a range of 0.1 to 14%, while four showed 0.3 to 1.2% reduction. The remaining three participants were excluded from comparison due to the lack of significant coherence, thus no filters were applied. The improvement in classification accuracy is significant (p = 0.017) when the SABD loading equals 50% of the maximal torque. Our findings suggest that the coherence-based filters can reduce the impact of flexion synergy by removing the synergy-induced EMG component and have the potential to improve the overall classification accuracy of hand movements in individuals with poststroke flexion synergy.

Observation of broadband acoustic scatter from fish at multiple angles has the potential to offer advantages for remote classification in cases where the species exhibit differences in size and or body shape, and animal orientation is unknown or difficult to estimate. However, the total angular coverage of the observations, available bandwidth, and the inter- and intra-species variability in size can significantly influence classification accuracy. These parameters are investigated using laboratory data collected from ten different fish species using a linear array of receivers and a single transmitter with an effective angular coverage of up to 25 degrees with a 3 degree sampling increment. Results indicate that when the species exhibit significant differences in length, even two observations covering only 10 degrees are sufficient to obtain an 80 percent improvement in classification accuracy over a single observation angle. This holds true for a wide range of animal orientations. As inter-species size similarity increases, and intra-species size variability increases, more observations covering a larger range of angles are required to maintain a similar improvement in classification accuracy.

Often, previous knowledge about the classes on the ground may assist in the improvement of a land cover classification from remote sensing data. There are many ways in which the previous information in the form of a priori probabilities can be included in the training stage of a supervised classification process. The inclusion of a priori probabilities may be useful to classify the images dominated by mixed pixels. In this paper, an approach to incorporate a priori probabilities in back propagation neural network (BPNN) algorithm, by way of replicating the training data (consisting of pure pixels) of a class according to its proportional area coverage has been implemented. By doing this, the abundant classes are assigned more weights than the other classes in the image. The results show a significant improvement in classification accuracy by 20% over the case when the a priori probabilities are not included. However, the major limitation of this approach is that it depends on the use of pure pixels, which may often be hard to find in images dominated by mixed pixels. We therefore suggest an alternative approach that incorporates mixed pixels in the training stage of BPNN algorithm. The results from this approach also reflect a significant improvement in classification accuracy by 14%. Further, both the approaches produce significantly higher accuracy than the most widely used maximum likelihood classifier (MLC). However, the second approach, which does not depend on identification of pure pixels in the image and their replication, appears more attractive to produce meaningful and accurate land cover classification from remote sensing data.

Semantic information and syntactic information are two essential dimensions of language. In recent years, most researches focus on extracting semantic information or how to extract useful information from syntactic dependency tree. As the semantic representations are closely related to syntactic ones, how to effectively integrate semantic and syntactic information is still a subject worth studying. We propose attention mechanism based on graph convolutional networks. The model integrates syntactic dependence information into semantic features and automatically achieve a balance between semantic information and syntactic information. We conducted experiments on the task of sentence-level relation extraction, and the results show that our model performs better than some previous methods.

Decision tree-based Rotation Forest could generate satisfactory but lower classification accuracy for a given training sample set and image data, owing to the inherent disadvantages in decision trees, namely myopic, replication and fragmentation problem. To improve performance of Rotation Forest technique, we propose to utilize two-hidden-layered-feedforward neural network as base classifier instead of decision tree. We examine the classification performance of proposed model under two situations, namely when free network parameters are maintained the same across all ensemble components and otherwise. The proposed model, where each component is initialized with different pair of initial weights and bias, performs better than decision tree-based Rotation Forest on three different Hyperspectral sensor datasets – AVIRIS, ROSIS and Hyperion. Improvements in classification accuracy are above 2% and up to 3% depending upon dataset. Also, the proposed model achieves improvement in accuracy over Random Forest in the range 4.2–8.8%.

.The aim of this work is to develop an effective brain–computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.

Deep Learning has recently become hugely popular in machine learning for its ability to solve end-to-end learning systems, in which the features and the classifiers are learned simultaneously, providing significant improvements in classification accuracy in the presence of highly-structured and large databases. Its success is due to a combination of recent algorithmic breakthroughs, increasingly powerful computers, and access to significant amounts of data. Researchers have also considered privacy implications of deep learning. Models are typically trained in a centralized manner with all the data being processed by the same training algorithm. If the data is a collection of users' private data, including habits, personal pictures, geographical positions, interests, and more, the centralized server will have access to sensitive information that could potentially be mishandled. To tackle this problem, collaborative deep learning models have recently been proposed where parties locally train their deep learning structures and only share a subset of the parameters in the attempt to keep their respective training sets private. Parameters can also be obfuscated via differential privacy (DP) to make information extraction even more challenging, as proposed by Shokri and Shmatikov at CCS'15. Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper. In particular, we show that a distributed, federated, or decentralized deep learning approach is fundamentally broken and does not protect the training sets of honest participants. The attack we developed exploits the real-time nature of the learning process that allows the adversary to train a Generative Adversarial Network (GAN) that generates prototypical samples of the targeted training set that was meant to be private (the samples generated by the GAN are intended to come from the same distribution as the training data). Interestingly, we show that record-level differential privacy applied to the shared parameters of the model, as suggested in previous work, is ineffective (i.e., record-level DP is not designed to address our attack).

EACMFS Prizes and Awards

In this paper we propose a new plan recognition method from observations of incomplete action sequences by regarding them as prefixes in a probabilistic context-free grammar (PCFG). In previous work that uses a PCFG for plan recognition, the PCFG receives a sentence, i.e. an observation of complete action sequences to recognize the plan behind it. However, when we deal with real plan recognition problems such as the Web access log analysis, we often cannot have complete sequences of actions and the traditional PCFG approach is not applicable. To overcome this difficulty, we extend the probability computation of PCFGs to prefix probability computation though it requires an infinite sum of probabilities. We applied the proposed method to infer the intended goals of Web site visitors from the online and partial observations of their actions. Also we compared the performance of plan recognition from observations of initial sequences of visitors' actions with that from full observations.

Gene expression analysis techniques identify important genes that predict specified outcomes based on sample characteristics. Given the small sample sizes common to these studies and the large dimensionality of the data, feature selection methods are essential. In addition, cancer-related expression analysis often involves imbalanced datasets due to rare forms of disease. Popular methods of feature selection employ univariate techniques to identify the features most suitable for analysis. We propose a multivariate technique for selecting accurate subsets of features using an approach based on random subspaces. The random subspace method is used to explore random combinations of features and only subspaces that produce accurate classifiers are retained. The method is tested on two independent gene expression datasets and compared with a univariate approach. The multivariate feature selection method resulted in a 33% improvement in classification accuracy overall and 90% improvement in classification accuracy for the minority class.

Optimization of activity-driven event detection for long-term ambulatory urodynamics.

Among the blood cells, white blood cells (WBC), which play a crucial role in forming our body's defense system, are essential components. Originating in the bone marrow, these cells serve as the fundamental components of the immune system, shouldering the responsibility of safeguarding the body against foreign microbes and diseases. Insufficient WBC counts may compromise the body's skill to resist infections, a status known as leukopenia. White blood cell counting is a specialty procedure that is usually carried out by qualified physicians and radiologists. Thanks to recent advances, image processing techniques are frequently used in biological systems to identify a wide spectrum of illnesses. In this work, image processing techniques were applied to enhance the white blood cell deep learning models' classification accuracy. To expedite the classification process, Convolutional Neural Network (CNN) models were combined with Ridge feature selection and Maximal Information Coefficient techniques. These tactics successfully determined the most important characteristics. The selected feature set was then applied to the classification procedure. ResNet-50, VGG19, and our suggested model were used as feature extractors in this study. The categorizing of white blood cells was completed with an amazing 98.27% success rate. Results from the experiments demonstrated a considerable improvement in classification accuracy using the proposed CNN model.

Context Use of camera trap data in wildlife research is reliant on accurate classification of animals at the species, sex–age category or individual level. One such example is white-tailed deer (Odocoileus virginianus) camera surveys, which are often conducted to produce demographic estimates used by managers to establish harvest goals for a population. Previous research suggests that misclassification of deer by sex–age category (e.g. adult male, adult female, fawn) is common in these surveys, and represents a source of bias that could misinform important management decisions. Aim To examine whether training material has an effect on classification accuracy of white-tailed deer and explore other observer-based, experiential factors as they relate to classification accuracy. Methods We developed and tested the efficacy of species-specific training material designed to reduce sex–age misclassifications associated with white-tailed deer images. Key results Exposure to training material resulted in the greatest improvement in classification accuracy of deer images compared with any other respondent-based factors we investigated. Other factors, such as professional experience as a wildlife biologist, field experience viewing white-tailed deer and experience viewing deer images from camera traps, were positively associated with classification accuracy of deer images. Conclusions Our findings suggest that training material has the ability to reduce misclassifications, leading to more accurate demographic estimates for white-tailed deer populations. We also found that prior experience using camera traps and familiarity with target species was positively related to classification accuracy. Implications Species-specific training material would provide a valuable resource to wildlife managers tasked with classifying animals at the species, sex–age category or individual level.

Introduction: This paper presents a novel approach for classifying different types of steel rods using a combination of pre-trained convolutional neural networks (CNNs) and fuzzy logic systems. We utilize VGG16, ResNet50, and InceptionV3 models to extract features from grayscale images of steel rods, which are then classified using Support Vector Machines (SVM). Furthermore, we incorporate a fuzzy logic system to improve the final classification accuracy by combining the individual accuracies of each CNN model. Experimental results demonstrate the effectiveness of our approach in achieving high classification accuracy compared to traditional methods. Objectives: This research presents a novel multi-model approach for steel rod classification utilizing three distinct fuzzy logic systems: Fuzzy Recurrent Neural Network (RNN), Fuzzy Convolutional Neural Network (CNN), and Fuzzy Neural Network. The proposed system addresses the critical challenge of accurate defect detection and classification in steel manufacturing processes. By combining these complementary approaches, we achieve enhanced feature extraction capabilities and improved classification accuracy. Experimental results demonstrate that our multi-model system achieves a classification accuracy of 97.8%, surpassing single-model approaches by an average of 8.2%. The system successfully identifies surface defects, structural anomalies, and dimensional variations with high precision, making it suitable for real-time industrial applications. Methods: This paper introduces a novel approach for the feature extraction and classification of steel rods by integrating multiple models and utilizing fuzzy logic systems. The methodology leverages pre-trained convolutional neural networks (CNNs) for extracting comprehensive feature representations from steel rod images. These extracted features are then combined and integrated using fuzzy logic to enhance classification accuracy and reliability. By addressing the limitations of traditional manual inspection methods and single-model approaches, this integrated multi-model system offers a robust solution for real-time industrial implementation. The proposed system aims to improve the efficiency and effectiveness of quality control processes in steel rod manufacturing, ensuring consistent and accurate classification even under varying conditions and complexities. This innovative approach paves the way for more advanced and adaptable automated inspection systems in the industry. Results: The paper aims to develop an integrated multi-model system that combines three distinct fuzzy logic approaches to enhance feature extraction capabilities through complementary methodologies. By improving the classification accuracy and reliability, the system addresses the limitations of existing methods. The goal is to create a robust and adaptable system that is suitable for real-time industrial implementation for steel rod analysis and classification. This comprehensive approach leverages the strengths of multiple models and fuzzy logic techniques, ensuring accurate and efficient classification processes, even under complex and varying conditions. The proposed multi model system is designed to meet the stringent quality control requirements of industrial applications, ultimately improving productivity and product quality through reliable automated inspection and classification for steel rod with accuracy of 97.80%, precision of 97.30% and recall 97.50%. Conclusions: This paper presents a novel approach for the classification of steel rods using a combination of pre-trained CNN models and fuzzy logic systems. The experimental results demonstrate the effectiveness of our method in achieving high classification accuracy. This research demonstrates the effectiveness of a multi-model fuzzy logic approach for steel rod classification. The system achieves significant improvements over single-model approaches while maintaining real-time processing capabilities. Our approach provides a foundation for further research and development in automated industrial image classification.