Combined Feature Vector Research Articles

APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions

More sophisticated representations of compounds attempt to incorporate not only information on the structure and physicochemical properties of molecules, but also knowledge about their biological traits, leading to the so-called bioactivity profile. The bioactive profiling of air pollutants is challenging and crucial, as their biological activity and toxicological effects have not been deeply investigated yet, and further exploration could shed light on the impact of air pollution on complex disorders. Therefore, a biological signature that simultaneously captures the chemistry and the biology of small molecules may be beneficial in predicting the behaviour of such ligands towards a protein target. Moreover, the interactivity between biological entities can be represented through combined feature vectors that can be given as input to a machine learning (ML) model to capture the underlying interaction. To this end, we propose a chemogenomic approach, called Air Pollutant Bioactivity (APBIO), which integrates compound bioactivity signatures and target sequence descriptors to train ML classifiers subsequently used to predict potential compound-target interactions (CTIs). We report the performances of the proposed methodology and, via external validation sets, demonstrate its outperformance compared to existing molecular representations in terms of model generalizability. We have also developed a publicly available Streamlit application for APBIO at ap-bio.streamlit.app, allowing users to predict associations between investigated compounds and protein targets.Scientific contributionWe derived ex novo bioactivity signatures for air pollutant molecules to capture their biological behaviour and associations with protein targets. The proposed chemogenomic methodology enables the prediction of novel CTIs for known or similar compounds and targets through well-established and efficient ML models, deepening our insight into the molecular interactions and mechanisms that may have a deleterious impact on human biological systems.

An integrated approach to Bayesian weight regulations and multitasking learning methods for generating emotion-based content in the metaverse

This paper introduces an integrated model designed to analyze topics and sentiments in textual data simultaneously, recognizing their interdependence. By tackling challenges such as data scarcity, missing information, and biased distributions, the proposed model effectively captures the dynamic interactions between topics and sentiments within complex datasets. The method combines the use of Convolutional Neural Networks (CNN) for detecting topic-related patterns with a revised Recurrent Neural Network (RNN) to trace the emotional flow within contexts, leveraging the strengths of sequential data processing. These components are integrated within a Bayesian probability framework, modeling the conditional probabilities of sentences expressing specific sentiments and documents being associated with particular topics. The combined feature and state vectors from the CNN and revised RNN within this Bayesian setup enable precise classification and prediction of topics and sentiments. Furthermore, this paper explores innovative research avenues, including sentiment analysis in 3D virtual reality environments and the development of new algorithms that reflect content creation techniques in the metaverse, offering dynamic system construction. This integrated approach not only enhances data-driven decision-making but also unlocks new possibilities for advanced multi-text analysis.

MLF-PointNet++: A Multifeature-Assisted and Multilayer Fused Neural Network for LiDAR-UAS Point Cloud Classification in Estuarine Areas

LiDAR-unmanned aerial system (LiDAR-UAS) technology can accurately and efficiently obtain detailed and accurate three-dimensional spatial information of objects. The classification of objects in estuarine areas is highly important for management, planning, and ecosystem protection. Owing to the presence of slopes in estuarine areas, distinguishing between dense vegetation (lawns and trees) on slopes and the ground at the tops of slopes is difficult. In addition, the imbalance in the number of point clouds also poses a challenge for accurate classification directly from point cloud data. A multifeature-assisted and multilayer fused neural network (MLF-PointNet++) is proposed for LiDAR-UAS point cloud classification in estuarine areas. First, the 3D shape features that characterize the geometric characteristics of targets and the visible-band difference vegetation index (VDVI) that can characterize vegetation distribution are used as auxiliary features to enhance the distinguishability of dense vegetation (lawns and trees) on slopes and the ground at the tops of slopes. Second, to enhance the extraction of target spatial information and contextual relationships, the feature vectors output by different layers of set abstraction in the PointNet++ model are fused to form a combined feature vector that integrates low and high-level information. Finally, the focal loss function is adopted as the loss function in the MLF-PointNet++ model to reduce the effect of imbalance in the number of point clouds in each category on the classification accuracy. A classification evaluation was conducted using LiDAR-UAS data from the Moshui River estuarine area in Qingdao, China. The experimental results revealed that MLF-PointNet++ had an overall accuracy (OA), mean intersection over union (mIOU), kappa coefficient, precision, recall, and F1-score of 0.976, 0.913, 0.960, 0.953, 0.953, and 0.953, respectively, for object classification in the three representative areas, which were better than the corresponding values for the classification methods of random forest, BP neural network, Naive Bayes, PointNet, PointNet++, and RandLA-Net. The study results provide effective methodological support for the classification of objects in estuarine areas and offer a scientific basis for the sustainable development of these areas.

Taking another look at intelligence and personality using an eye-tracking approach

Intelligence and personality are both key drivers of learning. This study extends prior research on intelligence and personality by adopting a behavioral-process-related eye-tracking approach. We tested 182 adults on fluid intelligence and the Big Five personality traits. Eye-tracking information (gaze patterns) was recorded while participants completed the intelligence test. Machine learning models showed that personality explained 3.18% of the variance in intelligence test scores, with Openness and, surprisingly, Agreeableness most meaningfully contributing to the prediction. Facet-level measures of personality explained a larger amount of variance (7.67%) in intelligence test scores than the trait-level measures, with the largest coefficients obtained for Ideas and Values (Openness) and Compliance and Trust (Agreeableness). Gaze patterns explained a substantial amount of variance in intelligence test performance (35.91%). Gaze patterns were unrelated to the Big Five personality traits, but some of the facets (especially Self-Consciousness from Neuroticism and Assertiveness from Extraversion) were related to gaze. Gaze patterns reflected the test-solving strategies described in the literature (constructive matching, response elimination) to some extent. A combined feature vector consisting of gaze-based predictions and personality traits explained 37.50% of the variance in intelligence test performance, with significant unique contributions from both personality and gaze patterns. A model that included personality facets and gaze explained 38.02% of the variance in intelligence test performance. Although behavioral data thus clearly outperformed “traditional” psychological measures (Big Five personality) in predicting intelligence test performance, our results also underscore the independent contributions of personality and gaze patterns in predicting intelligence test performance.

Multi-Layer Perceptron Classifier with the Proposed Combined Feature Vector of 3D CNN Features and Lung Radiomics Features for COPD Stage Classification.

Computed tomography (CT) has been regarded as the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Therefore, chest CT images should provide more information for COPD diagnosis, such as COPD stage classification. This paper proposes a features combination strategy by concatenating three-dimension (3D) CNN features and lung radiomics features for COPD stage classification based on the multi-layer perceptron (MLP) classifier. First, 465 sets of chest HRCT images are automatically segmented by a trained ResU-Net, obtaining the lung images with the Hounsfield unit. Second, the 3D CNN features are extracted from the lung region images based on a truncated transfer learning strategy. Then, the lung radiomics features are extracted from the lung region images by PyRadiomics. Third, the MLP classifier with the best classification performance is determined by the 3D CNN features and the lung radiomics features. Finally, the proposed combined feature vector is used to improve the MLP classifier's performance. The results show that compared with CNN models and other ML classifiers, the MLP classifier with the best classification performance is determined. The MLP classifier with the proposed combined feature vector has achieved accuracy, mean precision, mean recall, mean F1-score, and AUC of 0.879, 0.879, 0.879, 0.875, and 0.971, respectively. Compared to the MLP classifier with the 3D CNN features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.8% (accuracy), 5.3% (mean precision), 5.8% (mean recall), 5.4% (mean F1-score), and 2.5% (AUC). Compared to the MLP classifier with lung radiomics features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.0% (accuracy), 5.1% (mean precision), 5.0% (mean recall), 5.1% (mean F1-score), and 2.1% (AUC). Therefore, it is concluded that our method is effective in improving the classification performance for COPD stage classification.

Interpretability of a Deep Learning-Based Prediction Model for Mandibular Osteoradionecrosis.

The development of radiation-induced toxicities is a multifactorial process. Existing DVH-based prediction models use traditional multivariate analysis to combine all the potential risk factors. Recently, deep learning (DL) has been proposed for predicting mandibular osteoradionecrosis (ORN) directly from 3D dose distribution maps [1]. However, with this approach, incorporating non-imaging data such as potential risk factors presents challenges. We investigate the use of DL-based multimodality fusion for the purpose of radiation-induced ORN toxicity prediction. This study explores early and late fusion strategies for combining 3D radiation dose distribution maps and clinical and demographic variables in the prediction of mandibular ORN incidence in head and neck cancer patients treated with radiotherapy. The results are compared to single-modality predictions with a random forest (RF) trained only on clinical variables and a 3D DenseNet40 trained on dose maps alone. We investigate two different fusion approaches. In the first, the image features extracted from the radiation dose maps using a 3D DenseNet40 were concatenated with the clinical variables into one single vector using a type II early fusion strategy. The combined feature vector was input into a fully connected layer for classification of ORN vs. controls. A final softmax activation layer was added to obtain the class predicted probabilities. The second approach used a late fusion strategy, in which the outputs from the 3D DenseNet40 and the RF model were combined by averaging the predicted classification probabilities for each of the two classes (ORN and no ORN) to obtain the final class decision on a case-by-case basis. The AUROC values obtained for the late and early fusion models and the single-modality 3D DenseNet40 and RF models were 0.70, 0.68, 0.69 and 0.60, respectively. The highest AUC ROC was observed with the late fusion approach, which was statistically significantly different to that of the RF single-modality model with a significance level of 0.05. However, after Bonferroni correction (Altman 1999) for multiple comparisons was applied, resulting in a corrected significance level of 0.05/6 = 0.008 for each comparison, no statistically significant difference was observed between any of the models' AUROC values. This is most likely due to the lack of discriminative contribution observed from clinical variables, which on their own resulted in a poorly predictive RF model. To our knowledge, no previous work has been published on the use of multimodal fusion DL methods to combine dose distribution maps and clinical variables in the prediction of mandibular ORN. Although non-conclusive results were obtained, this study demonstrates the potential of DL in the prediction of the multifactorial side effects resulting from radiotherapy treatments. [1] Humbert-Vidan L et al. Prediction of Mandibular ORN Incidence from 3D Radiation Dose Distribution Maps Using DL (2022).

Deep‐SQA: A deep learning model using motor activity data for objective sleep quality assessment assisting digital wellness in healthcare 5.0

AbstractWearable sensor‐based devices like actigraphs collect motor activity data which provide objective measures of physical activity. This research puts forward a novel methodology for assessment of objective sleep quality using actigraph recordings of motor activity. High level features of sequential motor activity data are extracted using Long‐Short Term Memory (LSTM) model which are then paired with a significant statistical feature namely, zero percent which describes the percentage of events with zero activity over a series. Overlapping sliding window is used to input sequences into LSTM to capture superior features in activity recordings. The predictive ability of the combined feature vector is evaluated using support vector machine (SVM) classifier. This hybrid LSTM‐SVM framework is validated on a benchmark dataset namely, the MESA Actigraphy dataset and achieves an accuracy of 85.62% for sleep quality prediction. Effectiveness of overlapping sliding window and statistical feature are evaluated, and their significance is validated. It is validated that the concept of overlapping sliding window improves the performance accuracy by 3.51% and the use of discriminative statistical feature improves sleep quality prediction task by 2.95%. Comparison with state of the art validates that this is the first study using objective sleep quality indicator for assessment of sleep quality via actigraph‐based motor activity data.

Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder.

Predicting the therapeutic result of repetitive transcranial magnetic stimulation (rTMS) treatment could save time and costs as ineffective treatment can be avoided. To this end, we presented a machine-learning-based strategy for classifying patients with major depression disorder (MDD) into responders (R) and nonresponders (NR) to rTMS treatment. Resting state EEG data were recorded using 32 electrodes from 88 MDD patients before treatment. Then, patients underwent 7 weeks of rTMS, and 46 of them responded to treatment. By applying Independent Component Analysis (ICA) on EEG, we identified the relevant brain sources as possible indicators of neural activity in the dorsolateral prefrontal cortex (DLPFC). This was served through estimating the generators of activity in the sensor domain. Subsequently, we added physiological information and placed certain terms and conditions to offer a far more realistic estimation than the classic EEG. Ultimately, those components mapped in accordance with the region of the DLPFC in the sensor domain were chosen. Features extracted from the relevant ICs time series included permutation entropy (PE), fractal dimension (FD), Lempel-Ziv Complexity (LZC), power spectral density, correlation dimension (CD), features based on bispectrum, frontal and prefrontal cordance, and a combination of them. The most relevant features were selected by a Genetic Algorithm (GA). For classifying two groups of R and NR, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) were applied to predict rTMS treatment response. To evaluate the performance of classifiers, a 10-fold cross-validation method was employed. A statistical test was used to assess the capability of features in differentiating R and NR for further research. EEG characteristics that can predict rTMS treatment response were discovered. The strongest discriminative indicators were EEG beta power, the sum of bispectrum diagonal elements in delta and beta bands, and CD. The Combined feature vector classified R and NR with a high performance of 94.31% accuracy, 92.85% specificity, 95.65% sensitivity, and 92.85% precision using SVM. This result indicates that our proposed method with power and nonlinear and bispectral features from relevant ICs time-series can predict the treatment outcome of rTMS for MDD patients only by one session pretreatment EEG recording. The obtained results show that the proposed method outperforms previous methods.

Deep Hybrid Multimodal Biometric Recognition System Based on Features-Level Deep Fusion of Five Biometric Traits.

The need for information security and the adoption of the relevant regulations is becoming an overwhelming demand worldwide. As an efficient solution, hybrid multimodal biometric systems utilize fusion to combine multiple biometric traits and sources with improving recognition accuracy, higher security assurance, and to cope with the limitations of the uni-biometric system. In this paper, three strategies for dealing with a feature-level deep fusion of five biometric traits (face, both irises, and two fingerprints) derived from three sources of evidence are proposed and compared. In the first two proposed methodologies, each feature vector is mapped from the feature space into the reproducing kernel Hilbert space (RKHS) separately by selecting the appropriate reproducing kernel. In this higher space, where the result is the conversion of nonlinear relations to linear ones, dimensionality reduction algorithms (KPCA, KLDA) and quaternion-based algorithms (KQPCA, KQPCA) are used for the fusion of the feature vectors. In the third methodology, the fusion of feature spaces based on deep learning is administered by combining feature vectors in in-depth and fully connected layers. The experimental results on 6 databases in the proposed hybrid multibiometric system clearly show the multimodal template obtained from the deep fusion of feature spaces; while being secure against spoof attacks and making the system robust, they can use the low dimensionality of the fused vector to increase the accuracy of a hybrid multimodal biometric system to 100%, showing a significant improvement compared with uni-biometric and other multimodal systems.

Neural Correlation of EEG and Eye Movement in Natural Grasping Intention Estimation.

Decoding the user's natural grasp intent enhances the application of wearable robots, improving the daily lives of individuals with disabilities. Electroencephalogram (EEG) and eye movements are two natural representations when users generate grasp intent in their minds, with current studies decoding human intent by fusing EEG and eye movement signals. However, the neural correlation between these two signals remains unclear. Thus, this paper aims to explore the consistency between EEG and eye movement in natural grasping intention estimation. Specifically, six grasp intent pairs are decoded by combining feature vectors and utilizing the optimal classifier. Extensive experimental results indicate that the coupling between the EEG and eye movements intent patterns remains intact when the user generates a natural grasp intent, and concurrently, the EEG pattern is consistent with the eye movements pattern across the task pairs. Moreover, the findings reveal a solid connection between EEG and eye movements even when taking into account cortical EEG (originating from the visual or motor cortex) and the presence of a suboptimal classifier. Overall, this work uncovers the coupling correlation between EEG and eye movements and provides a reference for intention estimation.

Substation equipment temperature prediction based on multivariate information fusion and deep learning network.

Substation equipment temperature is difficult to achieve accurate prediction because of its typical seasonality, periodicity and instability, complex working environment and less available characteristic information. To overcome these difficulties, a substation equipment temperature prediction method is proposed based on multivariate information fusion, convolutional neural network (CNN) and gated recurrent unite (GRU) in this article. Firstly, according to the correlation analysis including linear correlation mapping, autocorrelation function and partial autocorrelation function for substation equipment temperature data, the feature vectors from ambient, time and space are determined, that is the multivariate information fusion feature vector (denoted as MIFFV); secondly, the dimension of MIFFV is reduced by principal component analysis (PCA), extract some of the most important features and form the reduced feature vector (denoted as RFV); then, CNN is used for deep learning to extract the relationship between RFV and the high-dimensional space feature, and construct the high-dimensional feature vector of multivariate time series (denoted as HDFV); finally, the high-dimensional feature vector is used to train GRU deep learning network and predict the equipment temperature. A substation equipment in Taizhou City, Zhejiang Province is conducted by the method proposed in this article. Through the comparative experiment from the two aspects of features and methods, under the two prediction performance evaluation indexes of mean absolute percentage error (MAPE) and root mean square error (RSME), two main conclusions are drawn: (1) MIFFV from three aspects of ambient features, time features and space features have better prediction performance than the single feature vector and the combined feature vector of two aspects; (2) compared with other four related models under the same conditions, RFV is regarded as the input of the models, the proposed model has better prediction performance.

An IoT based authentication system for therapeutic herbs measured by local descriptors using machine learning approach

The work aims to develop an automatic recognition model to classify medicinal plants using machine learning techniques to enrich the traditional medical system of India. Though many countries have accepted conventional medicine as the best alternative to synthetic drugs, there exists limitations such as lack of awareness among general public and unavailability of easy access to its source evidences that has led to its limited acceptance and usability. Herein, an intelligent system is proposed to use Raspberry Pi 3 Model B+ (RPi) and the RPi camera to capture the leaf images of Indian medicinal herbs and reveal their medical properties. Five types of models implemented to identify the medicinal plants. One of the models proposed as Herbmodel extracts a feature map from a captured medicinal leaf by combining three different feature extraction techniques, namely, Scale Invariant Feature Transform (SIFT), Oriented FAST and Rotated BRIEF (ORB) and histogram of oriented gradients on support vector machine as a classifier, predicts an average accuracy of 96.22% over a custom medicinal leaf dataset of 40 different species containing 2515 samples. Generate Bag of Visual Words (BoVW) by applying K-Means clustering on both SIFT and ORB descriptors to reduce the dimensionality. The combined feature vector is further analysed using random forest and k-nearest neighbor classifier. The efficacy of the proposed approach is benchmarked using Flavia dataset and artificial neural network (ANN) as a classifier. Our findings prove that the combination of local descriptors is an efficient measurement approach that benefits automatic recognition of plants based on leaf images. Also, a reliable source of medicinal leaf datasets with good quality leaf images is necessary to establish a machine learning model for medicinal plants.

RETRACTED ARTICLE: A rough set theory and deep learning-based predictive system for gender recognition using audio speech

Speech is one of the most delicate medium through which gender of the speakers can easily be identified. Though the related research has shown very good progress in machine learning, but recently, deep learning has imparted a very good research area to explore the deficiency of gender discrimination using traditional machine learning techniques. In deep learning techniques, the speech features are automatically generated by the reinforcement learning from the raw data which have more discriminating power than the human-generated features. But in some practical situations like gender recognition, it is observed that combination of both types of features sometimes provides comparatively better performance. In the proposed work, we have initially extracted and selected some informative and precise acoustic features relevant to gender recognition using entropy-based information theory and Rough Set Theory (RST). Next, the audio speech signals are directly fed into the deep neural network model consisting of Convolution Neural Network (CNN) and Gated Recurrent Unit network (GRUN) for extracting features useful for gender recognition. The RST selects precise and informative features, CNN extracts the locally encoded important features, and GRUN reduces the vanishing gradient and exploding gradient problems. Finally, a hybrid gender recognition system is developed combining both generated feature vectors. The developed model has been tested with five bench mark and a simulated dataset to evaluate its performance, and it is observed that combined feature vector provides more effective gender recognition system specially when transgender is considered as a gender type together with male and female.

RICE PLANT DISEASE IDENTIFICATION DECISION SUPPORT MODEL USING MACHINE LEARNING

In this paper, we propose a decision support system for Indian rice farmers for identifying diseases. In a country like India, food security is an essential concern. Additionally, diseases in plants can cause a significant loss. Early-stage detection of diseases can help in improving the production of rice. In this context, first we investigate the recent contributed efforts in the field of plant disease detection by analysing plant leaves using machine learning and image processing techniques. Next, the datasets and relevant algorithms are concluded. Then, a machine learning model has been presented. The model includes the edge feature extraction using canny edge detection technique, colour features are extracted using grid colour movement, and the texture analysis is performed using Local Binary Pattern (LBP). In the next step, using the extracted features, we have prepared a combined feature vector to train the Machine Learning (ML) algorithms namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). These machine learning algorithms are organized in such a manner that the proposed decision support model can identify and differentiate the leaf plants. Additionally, it also recognizes the rice plants when we query. Secondly, the model is also able to recognize rice plant diseases. The first scenario of the experiment has been carried out using Plant Village dataset. The second scenario of experiment uses the rice plant disease dataset obtained from Kaggle with three classes. The second dataset used which is known as the Mendeley dataset which contains five different diseases as class labels. The experimental study with the implemented system confirms the superiority of ANN to be used with the proposed decision support system as compared to the SVM algorithm in terms of accuracy and time consumption. Finally, future work has also been highlighted.

Audio-Visual Speech Recognition Using LSTM and CNN

Background: Multimodal speech recognition is proved to be one of the most promising solutions for robust speech recognition, especially when the audio signal is corrupted by noise. As the visual speech signal not affected by audio noise, it can be used to obtain more information used to enhance the speech recognition accuracy in noisy system. The critical stage in designing robust speech recognition system is choosing of reliable classification method from large variety of available classification techniques. Deep learning is well-known as a technique that has the ability to classify a nonlinear problem, and takes into consideration the sequential characteristic of the speech signal. Numerous researches have been done in applying deep learning to overcome Audio-Visual Speech Recognition (AVSR) problems due to its amazing achievements in both speech and image recognition. Even though optimistic results have been obtained from the continuous studies, researches on enhancing accuracy in noise system and selecting the best classification technique are still gaining lots of attention. Objective: This paper aims to build AVSR system that uses both acoustic combined with visual speech information and use classification technique based on deep learning to improve the recognition performance in a clean and noisy environment. Methods: Mel Frequency Cepstral Coefficient (MFCC) and Discrete Cosine Transform (DCT) are used to extract the effective features from audio and visual speech signal respectively. The audio feature rate is greater than the visual feature rate, so that linear interpolation is needed to obtain equal feature vectors size then early integrating them to get combined feature vector. Bidirectional Long-Short Term Memory (BiLSTM), one of the Deep learning techniques, are used for classification process and compare the obtained results to other classification techniques like Convolution Neural Network (CNN) and the traditional Hidden Markov Models (HMM). The effectiveness of the proposed model is proved by using two multi-speaker AVSR datasets termed AVletters and GRID. Results: The proposed model gives promising results where the obtained results In case of GRID, using integrated audio-visual features achieved highest recognition accuracy of 99.07% and 98.47% , with enhancement up to 9.28% and 12.05% over audio-only for clean and noisy data respectively. For AVletters, the highest recognition accuracy is 93.33% with enhancement up to 8.33% over audio- only. Conclusion: Based on the obtained results, we can conclude that increasing the size of audio feature vector from 13 to 39 doesn’t give effective enhancement for the recognition accuracy in clean environment, but in noisy environment, it gives better performance. BiLSTM is considered to be the optimal classifier for a robust speech recognition system when compared to CNN and traditional HMM, because it takes into consideration the sequential characteristic of the speech signal (audio and visual). The proposed model gives great improvement in the recognition accuracy and decreasing the loss value for both clean and noisy environments than using audio-only features. Comparing the proposed model to previously obtain results which using the same datasets, we found that our model gives higher recognition accuracy and confirms the robustness of our model.

Recognition of Persian Sign Language Alphabet Using Gaussian Distribution, Radial Distance and Centroid-Radii

Aims: Presenting a novel approach to identify Persian static symptoms in that the proposed sign language recognition system consists of two segmentation and feature extraction phases. In the segmentation phase, the hand region is separated by an effective segmentation method from the original image. Objective: (1) Introducing an effective framework to solve sensitivity to light conditions in identifying and recognizing letters of the Persian sign language alphabet. (2) Proposing a new feature extraction method to eliminate sensitivity to rotation and scale changes. (3) Creating a new dataset includes 480 images of the sign language alphabet symptoms in order to show the performance of our method. Methods: The proposed sign language recognition system uses two segmentation and feature extraction phases. In the segmentation phase, the hand region is separated by an effective segmentation method from the original image. This method is based on the unique Gaussian model in the YCbCr color space. The Bayes rule is used to precisely identify the hand region too. In the feature extraction phase, the radial model is used to obtain a one-dimensional function to display the hand region boundary and to compute the combined feature vector. In order to normalize this method, the Fourier Transformation method is applied. Results: The system was trained and tested using 480 image samples of Persian sign language characters, 15 images per sign, with the .jpg extension. Extensive experimental evaluations indicate that the proposed recognition system is less susceptible to displacement, scale, and rotation, and can detect symptoms at an accuracy of 95.62%. Conclusion: In this article, a new system was proposed for recognizing the alphabet letters in Persian sign language. This system consists of two main phases: the segmentation phase or the hand region identification and feature extraction phase. In the segmentation phase of the detection system, at first, the hand region is separated by an effective segmentation method from the original image. This method is based on the single Gaussian model in the color space of the YCbCr and the Bayes rule. Applying this method in hand segmentation, enables the system, to recognize the hand region well. In this method, after hand region detection, using the Sobel edge detector, the image edge is extracted. In the feature extraction phase, the radial distance model was used to obtain a one-dimensional function display of the hand region boundary and compute its feature vector. This model is based on the edge and image centroid and computes the centroid distance to the shape boundary as a function of the angle. Since this model by itself is sensitive to the scale variations and tilt, in order to normalize this method to tilt variations, the Fourier transformation was applied to feature vector computed by the radial model. Then the vector elements are divided by the largest vector element, thus, using this method the computed feature vector will be distinct for different images and will show less sensitivity to displacement, scale, and gradient. A database consist of 480 images of Persian sign symptoms was created to the overall accuracy evaluation of the system, the results show that the proposed recognition system can identify 32 letters of the Persian sign language alphabet with a detection rate 95.62%.

Common Gabor Features for Image Watermarking Identification

Image watermarking is one of many methods for preventing unauthorized alterations to digital images. The major goal of the research is to find and identify photos that include a watermark, regardless of the method used to add the watermark or the shape of the watermark. As a result, this study advocated using the best Gabor features and classifiers to improve the accuracy of image watermarking identification. As classifiers, discriminant analysis (DA) and random forests are used. The DA and random forest use mean squared energy feature, mean amplitude feature, and combined feature vector as inputs for classification. The performance of the classifiers is evaluated using a variety of feature sets, and the best results are achieved. In order to assess the performance of the proposed method, we use a public database. VOC2008 is a public database that we use. The findings reveal that our proposed method’s DA classifier with integrated features had the greatest TPR of 93.71 and the lowest FNR of 6.29. This shows that the performance outcomes of the proposed approach are consistent. The proposed method has the advantages of being able to find images with the watermark in any database and not requiring a specific type or algorithm for embedding the watermark.

Fish Image Classification by XgBoost Based on Gist and GLCM Features

Classification of fish image is a complex issue in the field of pattern recognition. Fish classification is a complicated task. Physical shape, size, orientation etc. made it complex to classify. Selection of appropriate feature is also a great issue in image classification. Classification of fish image is very important in fishing service and agricultural field, fish industry, survey applications of fisheries and in other related area. For the assessment and counting of fishes, classification of fish image is also necessary as it can save time. This paper presents a fish image classification method with the robust Gist feature and Gray Level Co-occurrence Matrix (GLCM) feature. Noise removal and resizing of image is applied as pre-processing task. Gist and GLCM feature are combined to make a better feature matrix. Features are also tested separately. But combined feature vector performs better than individual. Classification is made on ten types of raw images of fish from two datasets -QUT and F4K dataset. The feature set is trained with different machine learning models. Among them, XgBoost performs with 90.2% and 98.08% accuracy for QUT and F4K dataset respectively.

AI inspired EEG-based spatial feature selection method using multivariate empirical mode decomposition for emotion classification.

Classification of human emotions based on electroencephalography (EEG) is a very popular topic nowadays in the provision of human health care and well-being. Fast and effective emotion recognition can play an important role in understanding a patient’s emotions and in monitoring stress levels in real-time. Due to the noisy and non-linear nature of the EEG signal, it is still difficult to understand emotions and can generate large feature vectors. In this article, we have proposed an efficient spatial feature extraction and feature selection method with a short processing time. The raw EEG signal is first divided into a smaller set of eigenmode functions called (IMF) using the empirical model-based decomposition proposed in our work, known as intensive multivariate empirical mode decomposition (iMEMD). The Spatio-temporal analysis is performed with Complex Continuous Wavelet Transform (CCWT) to collect all the information in the time and frequency domains. The multiple model extraction method uses three deep neural networks (DNNs) to extract features and dissect them together to have a combined feature vector. To overcome the computational curse, we propose a method of differential entropy and mutual information, which further reduces feature size by selecting high-quality features and pooling the k-means results to produce less dimensional qualitative feature vectors. The system seems complex, but once the network is trained with this model, real-time application testing and validation with good classification performance is fast. The proposed method for selecting attributes for benchmarking is validated with two publicly available data sets, SEED, and DEAP. This method is less expensive to calculate than more modern sentiment recognition methods, provides real-time sentiment analysis, and offers good classification accuracy.

Classification Accuracy Enhancement Based Machine Learning Models and Transform Analysis

The problem of leak detection in water pipeline network can be solved by utilizing a wireless sensor network based an intelligent algorithm. A new novel denoising process is proposed in this work. A comparison study is established to evaluate the novel denoising method using many performance indices. Hardyrectified thresholding with universal threshold selection rule shows the best obtained results among the utilized thresholding methods in the work with Enhanced signal to noise ratio (SNR) = 10.38 and normalized mean squared error (NMSE) = 0.1344. Machine learning methods are used to create models that simulate a pipeline leak detection system. A combined feature vector is utilized using wavelet and statistical factors to improve the proposed system performance.