Sophisticated Feature Extraction Research Articles

Retina Blood Vessels Segmentation and Classification with the Multi-featured Approach.

Segmenting retinal blood vessels poses a significant challenge due to the irregularities inherent in small vessels. The complexity arises from the intricate task of effectively merging features at multiple levels, coupled with potential spatial information loss during successive down-sampling steps. This particularly affects the identification of small and faintly contrasting vessels. To address these challenges, we present a model tailored for automated arterial and venous (A/V) classification, complementing blood vessel segmentation. This paper presents an advanced methodology for segmenting and classifying retinal vessels using a series of sophisticated pre-processing and feature extraction techniques. The ensemble filter approach, incorporating Bilateral and Laplacian edge detectors, enhances image contrast and preserves edges. The proposed algorithm further refines the image by generating an orientation map. During the vessel extraction step, a complete convolution network processes the input image to create a detailed vessel map, enhanced by attention operations that improve modeling perception and resilience. The encoder extracts semantic features, while the Attention Module refines blood vessel depiction, resulting in highly accurate segmentation outcomes. The model was verified using the STARE dataset, which includes 400 images; the DRIVE dataset with 40 images; the HRF dataset with 45 images; and the INSPIRE-AVR dataset containing 40 images. The proposed model demonstrated superior performance across all datasets, achieving an accuracy of 97.5% on the DRIVE dataset, 99.25% on the STARE dataset, 98.33% on the INSPIREAVR dataset, and 98.67% on the HRF dataset. These results highlight the method's effectiveness in accurately segmenting and classifying retinal vessels.

Understanding market sentiment analysis: A survey

AbstractMarket sentiment analysis (MSA) has evolved significantly over nearly four decades, growing in relevance and application in economics and finance. This paper extensively reviews MSA, encompassing methodologies ranging from lexicon‐based techniques to traditional Machine Learning (ML), Deep Learning (DL), and hybrid approaches. Emphasizing the transition from rudimentary word counters to sophisticated feature extraction from diverse sources such as news, social media, and share prices, the study presents an updated state‐of‐the‐art review of sentiment analysis. Furthermore, using network analysis, a bibliometric and scientometric lens is applied to map the expanding footprint of sentiment research within economics and finance, revealing key trends, dominant research hubs, and potential areas for interdisciplinary collaboration. This exploration consolidates the foundational and emerging methods in MSA and underscores its dynamic interplay with global financial ecosystems and the imperative for future integrative research trajectories.

Enhanced Diabetic Retinopathy Diagnosis: A Comparative Analysis of models with an Ensemble classifier and Deep Q Learning

- A novel method that combines the strengths of different classifiers such as Naive Bayes, Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM) is introduced in this paper. This method tackles the urgent need for cutting-edge diagnostic methods in the field of ophthalmology, mainly for the identification of diabetic retinopathy (DR). The approach is ensemble-based. Classical methods in retinal analysis of images often fail as they are static and are unable to adjust to the unique details that each distinct image presents. This constraint results in less accurate and precise diagnostic results, highlighting the urgent need for more adaptable and dynamic methods. The suggested model differs significantly from previous methods. Through the use of an ensemble approach, it capitalizes on the distinct advantages of each classifier: the MLP process's sophisticated feature extraction skill, Naive Bayes' probabilistic analysis, and SVM's non-linear pattern recognition capacity. By combining these techniques, the inherent drawbacks of utilizing a single strategy are addressed, guaranteeing a more thorough examination of retinal samples and images. The core of this idea is the system using Deep Q Learning (DQL) for adaptive classifier selection. Using learned Q Values for various contexts, this reinforcement learning technique selects the best classifier adaptively for each unique retinal image, hence optimizing the ensemble. This approach not only advances diagnosis accuracy and precision but also guarantees ongoing learning and adaptation to keep up with changing data patterns and advances in imaging technology. Extensive experiments on the IDRiD & EyePACS Dataset show the effectiveness of this model with a 5.5% increase in overall accuracy with other performance metrics, the results show a significant improvement over the current method. They represent a significant advancement in the timely and accurate identification of diabetic retinopathy, which will ultimately benefit patients and lessen the strain on healthcare systems. Thus, this work represents a major step forward in patient care as well as a technological advance, opening the door to more efficient supervision and treatment of retinal illnesses.

Design And Development of Sketch Based Image Retrieval Using Deep Learning

In this cutting edge, the common wrong doing rate is expanding day-by-day and to manage up with this the criminal divisions as well ought to discover ways in which would speed up the by and large preparation and offer assistance in bringing one to justice. In response to rising crime rates, law enforcement agencies are turning to advanced algorithms capable of matching freehand sketches with images in databases. These algorithms, utilizing sophisticated feature extraction techniques and deep learning models, significantly enhance identification accuracy. By leveraging Sketch based image retrieval technology, investigations are expedited, leading to quicker suspect apprehensions and resolution of criminal cases. This results in improved public safety and justice outcomes, as well as more efficient law enforcement practices overall.

An integrated skip convolutional network with residual learning and feature extraction for point and interval prediction of solar radiation

Spinning reserve based on solar radiation prediction can ensure the secure operation of large-scale grid-connected photovoltaic systems, but irrational spinning reserve can lead to substantial economic losses and even grid collapse. Therefore, it is imperative to identify characteristic patterns of solar radiation and establish an effective solar radiation prediction model. However, existing hybrid models often struggle to efficiently recognize and leverage input features, compromising the robustness of the model. To address this limitation, this study proposes an integrated skip-convolutional network with residual learning and feature extraction (InSCNet), which enhances the capability to represent information data and thereby improves the stability and accuracy of solar radiation prediction by employing a series of feature extraction and prediction blocks with residual learning. InSCNet includes a sophisticated feature extraction module to effectively address the underutilization of feature information, and it incorporates residual learning, skip-convolution, and recursive networks in the prediction module, reducing the risk of gradient vanishing or gradient explosion while enhancing prediction accuracy. Additionally, an interval estimation method with adaptively chosen distributions is introduced, which extends the prediction interval estimation method. The proposed InSCNet is rigorously evaluated using datasets from three major cities in Pakistan. Experimental results demonstrate that InSCNet outperforms existing solutions in both point and interval predictions.

Fabric surface defect classification and systematic analysis using a cuckoo search optimized deep residual network

Fabric defects can significantly impact the quality of a textile product. By analyzing the types and frequencies of defects, manufacturers can identify process inefficiencies, equipment malfunctions, or operator errors. Although deep learning networks are accurate in classification applications, some defects may be subtle and difficult to detect, while others may have complex patterns or occlusions. CNNs may struggle to capture a wide range of defect variations and generalize well to unseen defects. Discriminating between genuine defects and benign variations requires sophisticated feature extraction and modeling techniques. This paper proposes a residual network-based CNN model to enhance the classification of fabric defects. A pretrained residual network, ResNet50, is fine-tuned to classify fabric defects into four categories: holes, objects, oil spots, and thread errors on the fabric surface. The fine-tuned network is further optimized via cuckoo search optimization using classification error as a fitness function. The network is systematically analyzed at different layers, and the investigation of classification results are reported using a confusion matrix and classification accuracy for each class. The experimental results confirm that the proposed model achieved superior performance with 95.36% accuracy and a 95.35% F1 score for multiclass classification. In addition, the proposed model achieved higher accuracy with similar or fewer trainable parameters than traditional deep CNN networks.

PractiCPP: a deep learning approach tailored for extremely imbalanced datasets in cell-penetrating peptide prediction.

Effective drug delivery systems are paramount in enhancing pharmaceutical outcomes, particularly through the use of cell-penetrating peptides (CPPs). These peptides are gaining prominence due to their ability to penetrate eukaryotic cells efficiently without inflicting significant damage to the cellular membrane, thereby ensuring optimal drug delivery. However, the identification and characterization of CPPs remain a challenge due to the laborious and time-consuming nature of conventional methods, despite advances in proteomics. Current computational models, however, are predominantly tailored for balanced datasets, an approach that falls short in real-world applications characterized by a scarcity of known positive CPP instances. To navigate this shortfall, we introduce PractiCPP, a novel deep-learning framework tailored for CPP prediction in highly imbalanced data scenarios. Uniquely designed with the integration of hard negative sampling and a sophisticated feature extraction and prediction module, PractiCPP facilitates an intricate understanding and learning from imbalanced data. Our extensive computational validations highlight PractiCPP's exceptional ability to outperform existing state-of-the-art methods, demonstrating remarkable accuracy, even in datasets with an extreme positive-to-negative ratio of 1:1000. Furthermore, through methodical embedding visualizations, we have established that models trained on balanced datasets are not conducive to practical, large-scale CPP identification, as they do not accurately reflect real-world complexities. In summary, PractiCPP potentially offers new perspectives in CPP prediction methodologies. Its design and validation, informed by real-world dataset constraints, suggest its utility as a valuable tool in supporting the acceleration of drug delivery advancements. The source code of PractiCPP is available on Figshare at https://doi.org/10.6084/m9.figshare.25053878.v1.

Integrative Graph-Based Framework for Predicting circRNA Drug Resistance Using Disease Contextualization and Deep Learning.

Circular RNAs (circRNAs) play a crucial role in gene regulation and have been implicated in the development of drug resistance in cancer, representing a significant challenge in oncological therapeutics. Despite advancements in computational models predicting RNA-drug interactions, existing frameworks often overlook the complex interplay between circRNAs, drug mechanisms, and disease contexts. This study aims to bridge this gap by introducing a novel computational model, circRDRP, that enhances prediction accuracy by integrating disease-specific contexts into the analysis of circRNA-drug interactions. It employs a hybrid graph neural network that combines features from Graph Attention Networks (GAT) and Graph Convolutional Networks (GCN) in a two-layer structure, with further enhancement through convolutional neural networks. This approach allows for sophisticated feature extraction from integrated networks of circRNAs, drugs, and diseases. Our results demonstrate that the circRDRP model outperforms existing models in predicting drug resistance, showing significant improvements in accuracy, precision, and recall. Specifically, the model shows robust predictive capability in case studies involving major anticancer drugs such as Cisplatin and Methotrexate, indicating its potential utility in precision medicine. In conclusion, circRDRP offers a powerful tool for understanding and predicting drug resistance mediated by circRNAs, with implications for designing more effective cancer therapies.

Statistical similarity matching and filtering for clinical image retrieval by machine learning approach

Clinical image retrieval plays a pivotal role in modern healthcare for diagnostics and research, but prior research has grappled with the challenge of achieving high accuracy due to limited filtering techniques. The proposed method includes statistical distance measurements for similarity comparison and a machine learning technique for image filtering. Throughout this framework, the search area for similarity matching is reduced by first filtering away irrelevant images using the probabilistic outcomes of the Support Vector Machine (SVM) classification as class predictions of search and database images. Resizing is done as part of the preprocessing. Then, using Principal Component Analysis (PCA), the preprocessed data’s textural features, visual characteristics, and low-level features are extracted. The study also suggested an adaptive similarity matching method centered on a linear integration of feature-level similarities on the individual-level level. The precision and ranking order details of the most appropriate images retrieved and predicted by SVMs are considered when calculating the feature weights. The system continually alters weights for every distinctive search to generate beneficial outcomes. The supervised and unsupervised learning strategies are studied to link low-level global image features in the generated PCA-based Eigen Space using their high-level semantic and visual classifications to reduce the semantic gap and enhance retrieval effectiveness. The ground-truth database used in experiments has 1594 unique medical images with 3 different databases. Our method significantly improves the precision and recall rates in image retrieval tasks by combining sophisticated feature extraction, data-driven algorithms, and deep learning models. Research obtained an impressive accuracy of 0.99, demonstrating the effectiveness of our approach. This novel methodology addresses the limitations of prior research and provides a robust and reliable solution for clinicians and researchers in the medical field seeking to access and analyze relevant clinical images.

Study on Copy-Move Forgery Detection in Medical Images using Handcrafted Features

Abstract: Medical image analysis and diagnosis are significantly hampered by digital picture alteration and fabrication. Copymove forgery, which involves copying a section of a picture and pasting it somewhere else in the same image to generate a fake copy, can result in incorrect diagnoses and even damage patients. Using handmade characteristics, we suggest a reliable technique in this study for identifying copy-move forgery in medical photos. Our method uses sophisticated feature extraction methods and image processing techniques to find duplicated portions within a picture. The integrity of medical image analysis is improved by experimental findings showing the efficacy of our suggested strategy in spotting copy-move forgery in medical pictures.

Implicit Emotion Analysis Based on Improved Supervised Contrastive Learning

Implicit sentiment analysis (ISA) can be distinguished from traditional text sentiment analysis by the fact that it does not rely on emotional words as emotional clues, and the expression is usually vaguer. Identifying implicit emotions is more difficult, as it requires a deeper understanding of the context, even when emotional words are absent. Researchers have focused on context feature modeling and developing sophisticated feature extraction mechanisms instead of starting from the emotional perspective. Enhancing the difference in emotional features of text samples is an intuitive method to address this challenge. We proposed a supervised contrastive learning (SCL) method during training that enables the model to conduct contrastive learning based on emotion labels even while training on weak emotion features. SCL training can strengthen the average embedding distance between texts with different emotion labels and enhance implicit emotion discrimination. Moreover, research indicates that contextual information can improve implicit emotion classification ability. Therefore, we applied a straightforward context feature fusion method (bi-affine) over a more complicated context feature modeling approach. To evaluate the effectiveness of our proposed method, we conducted experiments on the SMP2019-ECISA (Chinese implicit sentiment analysis) dataset. The results show a 2.13% enhancement in the F1 value compared to the BERT baseline, proving the effectiveness of our methods.

3D Hand Pose and Shape Estimation from Single RGB Image for Augmented Reality

In the realm of Human-Computer Interaction (HCI), the importance of hands cannot be overstated. Hands serve as a fundamental means of communication, expression, and interaction in the physical world. In recent years, Augmented Reality (AR) has emerged as a next-generation technology that seamlessly merges the digital and physical worlds, providing transformative experiences across various domains. In this context, accurate hand pose and shape estimation plays a crucial role in enabling natural and intuitive interactions within AR environments. Augmented Reality, with its ability to overlay digital information onto the real world, has the potential to revolutionize how we interact with technology. From gaming and education to healthcare and industrial training, AR has opened up new possibilities for enhancing user experiences. This study proposes an innovative approach for hand pose and shape estimation in AR applications. The methodology commences with the utilization of a pre-trained Single Shot Multi-Box (SSD) model for hand detection and cropping. The cropped hand image is then transformed into the HSV color model, followed by applying histogram equalization on the value band. To precisely isolate the hand, specific bounds are set for each band of the HSV color space, generating a mask. To refine the mask and diminish noise, contouring techniques are applied to the mask, and gap-filling methods are employed. The resultant refined mask is then combined with the original cropped image through logical AND operations to accurately delineate the hand boundaries. This meticulous approach ensures robust hand detection even in complex scenes. To extract pertinent features, the detected hand undergoes two concurrent processes. Firstly, the Scale-Invariant Feature Transform (SIFT) algorithm identifies distinctive keypoints on the hand's outer surface. Simultaneously, a pre-trained lightweight Convolutional Neural Network (CNN), namely MobileNet, is employed to extract 3D hand landmarks, the hand's center (middle finger metacarpophalangeal joint), and handedness information. These extracted features, encompassing hand keypoints, landmarks, center, and handedness, are aggregated and compiled into a CSV file for further analysis. A Gated Recurrent Unit (GRU) is then employed to process the features, capturing intricate dependencies between them. The GRU model successfully predicts the 3D hand pose, achieving high accuracy even in dynamic scenarios. The evaluation results for the proposed model are very promising that the Mean Per Joint Position Error in 3D (MPJPE) is 0.0596 between the predicted pose and the ground truth hand landmarks, while the Percentage of Correct Keypoints (PCK) is 95%. Upon predicting the hand pose, a mesh representation is employed to reconstruct the 3D shape of the hand. This mesh provides a tangible representation of the hand's structure and orientation, enhancing the realism and usability of the AR application. By integrating sophisticated detection, feature extraction, and predictive modeling techniques, this method contributes to creating more immersive and intuitive AR experiences, thereby fostering the seamless fusion of the digital and physical worlds.

Fusion of multivariate EEG signals for schizophrenia detection using CNN and machine learning techniques

Schizophrenia is a severe mental disorder that has adverse effects on the behavior of an individual such as disorganized speech and delusions. Electroencephalography (EEG) signals are widely used for its identification as they are non-invasive and have high temporal resolution. EEG signals may be captured using wearable devices but transmission of complete data from all channels is both battery and data consuming. Several studies on Schizophrenia have either used all channels or relied on sophisticated feature extraction algorithms to find the most relevant EEG channels for further processing. That too, however, needs data from all channels beforehand to identify the most relevant features. In this study, a publicly available multi-channel EEG signals dataset from the institute of Psychiatry and Neurology in Warsaw, Poland is studied for an automated identification of Schizophrenia using only a subset of data from selected channels. To achieve this, we device a channel selection mechanism based on a rigorous performance analysis of the Convolutional Neural Network (CNN) while considering the individual EEG channels at different brain regions. The selected channels are combined, and we use a fusion of CNN and different machine learning (ML) classifiers to train the classification model. Our experiments show that a combination of three channels namely, T4, T3, and Cz achieves 90% and 98% accuracies on subject-based and non-subject based testing, respectively, using a hybridization of CNN and logistic regression (LR).

ID:16588 Neural Network Trained on Distant Cortico-Cortical Evoked Potentials Can Localize Ictal Onset in Focal Epilepsy

Patients with medically refractory focal epilepsy often undergo extensive diagnostic workups to localize areas of ictal onset. This process is long and morbid with patients often spending weeks in the hospital undergoing intracranial recording to capture seizures. A few groups have used single pulse electrical stimulation (SPES) to create cortico-cortical evoked potentials (CCEP) in distant gray matter regions in an effort to localize ictal onset.1 However, the important localizing features of CCEPs remain unclear, and ictogenic localization ability has been limited 2. A potential reason for limited efficacy could be that important CCEP features could be complex and non-linear. Neural networks are particularly suited to identifying complex non-linear features. Thus, we aimed to leverage this sophisticated feature extraction ability of neural networks to identify the ictal onset zone.

A Lightweight Network for Point Cloud Analysis via the Fusion of Local Features and Distribution Characteristics.

Effectively integrating the local features and their spatial distribution information for more effective point cloud analysis is a subject that has been explored for a long time. Inspired by convolutional neural networks (CNNs), this paper studies the relationship between local features and their spatial characteristics and proposes a concise architecture to effectively integrate them instead of designing more sophisticated feature extraction modules. Different positions in the feature map of the 2D image correspond to different weights in the convolution kernel, making the obtained features that are sensitive to local distribution characteristics. Thus, the spatial distribution of the input features of the point cloud within the receptive field is critical for capturing abstract regional aggregated features. We design a lightweight structure to extract local features by explicitly supplementing the distribution information of the input features to obtain distinctive features for point cloud analysis. Compared with the baseline, our model shows improvements in accuracy and convergence speed, and these advantages facilitate the introduction of the snapshot ensemble. Aiming at the shortcomings of the commonly used cosine annealing learning schedule, we design a new annealing schedule that can be flexibly adjusted for the snapshot ensemble technology, which significantly improves the performance by a large margin. Extensive experiments on typical benchmarks verify that, although it adopts the basic shared multi-layer perceptrons (MLPs) as feature extractors, the proposed model with a lightweight structure achieves on-par performance with previous state-of-the-art (SOTA) methods (e.g., MoldeNet40 classification, 0.98 million parameters and 93.5% accuracy; S3DIS segmentation, 1.4 million parameters and 68.7% mIoU).

Label Oriented Hierarchical Attention Neural Network for Short Text Classification

Text classification task is a very common task in natural language processing. The conventional text classification task model often uses word bag model or representation model, but the existing model usually deals with long text classification task, which is not suitable for short text classification. Short text features are relatively fewer and often need more sophisticated feature extraction, and the role of keywords play great importance roles in the classification of short text. In this paper, we propose a label-oriented hierarchical attention mechanism network for short text classification. The model achieves better results on public data sets of Tiao and Weibo, compared with convolutional neural network CNN, GATE control unit neural network GRU, gate control unit neural network fusion convolutional neural network GRU-CNN and translation model Transfomer. It is proved that the model has good performance. Our model has two significant advantages: (1) it is a hierarchical structure consisting of two levels of attention mechanisms, which facilitates interpretation and analysis; (2) Compared with other architectures, this model can be used to extend multi-label text classification tasks. In addition, it can be used for long-term importance analysis in many industrial scenarios.

Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review

Electric motors are used extensively in numerous industries, and their failure can result not only in machine damage but also a slew of other issues, such as financial loss, injuries, etc. As a result, there is a significant scope to use robust fault diagnosis technology. In recent years, interesting research results on fault diagnosis for electric motors have been documented. Deep learning in the fault detection of electric equipment has shown comparatively better results than traditional approaches because of its more powerful and sophisticated feature extraction capabilities. This paper covers four traditional types of deep learning models: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), and recurrent neural networks (RNN), and highlights their use in detecting faults of electric motors. Finally, the issues and obstacles that deep learning encounters in the fault detection mechanism as well as the prospects are discussed and summarized.

Hand Gesture Recognition Based on sEMG Signal and Convolutional Neural Network

Recently, deep learning has become a promising technique for constructing gesture recognition classifiers from surface electromyography (sEMG) signals in human–computer interaction. In this paper, we propose a gesture recognition method with sEMG signals based on a deep multi-parallel convolutional neural network (CNN), which solves the problem that traditional machine learning methods may lose too much useful information during feature extraction. CNNs provide an efficient way to constrain the complexity of feedforward neural networks by weight sharing and restriction to local connections. Sophisticated feature extraction is to be avoided and hand gestures are to be classified directly. A multi-parallel and multi-convolution layer convolution structure is proposed to classify hand gestures. Experiment results show that in comparison with five traditional machine learning methods, the proposed method could achieve higher accuracy.

Finger movements classification based on fractional Fourier transform coefficients extracted from surface EMG signals

EMG signals have played a pivotal role as a fundamental component of myriad modern prostheses to control prostheses’ movements as well as identifying individual and combined hand or finger gestures. Despite a great deal of interest in these signals, the non-stationary nature of biological EMG signals has led to complications in EMG applications. Stationary signals have been analyzed plainly by time domain approaches like Fourier Transform, while non-stationary signals analysis is not satisfactory to be carried out with such method as it is not capable to illustrate the incidence time of various frequency components, besides, extracting both time and frequency information is essential. The Fractional Fourier Transform (FrFT), which is a generalization of classical Fourier Transform, is able to demonstrate the variable frequency of non-stationary signals. In this paper, FrFT technique with different fractional orders is employed as a novel and sophisticated feature extraction method for EMG signals of 8 subjects including 6 men and 2 women recorded in 10 different finger movements which are 5 individual and 5 combined postures. Windowing method and the t-test approach are utilized to select the best FrFT extracted coefficients as features. Employing KNN method to classify 10 different classes and the average classification accuracy of 98.12% of proposed method is a significant indicator of its sufficient performance.

A Lightweight Structure Based on Feature Fusion for Point Cloud Analysis

Point cloud analysis is challenging due to the irregularity and sparsity, making it difficult to capture the underlying geometric characteristics. This paper proposes a lightweight structure that effectively aggregates the local patterns and the spatial layout of them extracted from the irregular and sparse point cloud. Unlike previous works that seek sophisticated feature extraction methods, the key to this structure is simultaneously exploring the local features and their distribution features in a concise manner. Specifically, the two features, which correspond to two different scales, are extracted independently and then fused. In this way, an abstract shape-level feature that contains much shape awareness and robustness is obtained. Moreover, since the structure exhibits rapid convergence with conventional learning schedule, we upgrade it by introducing the snapshot ensemble and creatively design a more flexible and effective cyclic annealing learning schedule. We evaluate the structure on challenging benchmarks, and experiment results prove that our model achieves on-par or better performance than previous state-of-the-art (SOTA) methods, although with the simple shared multi-layer perceptrons (MLPs) as feature extractors.