Improve Classification Accuracy Research Articles

SFNet: Stellar Feature Network with CWT for Stellar Spectra Recognition

Stellar spectral classification is crucial in astronomical data analysis. However, existing studies are often limited by the uneven distribution of stellar samples, posing challenges in practical applications. Even when balancing stellar categories and their numbers, there is room for improvement in classification accuracy. This study introduces a Continuous Wavelet Transform using the Super Morlet wavelet to convert stellar spectra into wavelet images. A novel neural network, the Stellar Feature Network, is proposed for classifying these images. Stellar spectra from Large Sky Area Multi-Object Fiber Spectroscopic Telescope DR9, encompassing five equal categories (B, A, F, G, K), were used. Comparative experiments validate the effectiveness of the proposed methods and network, achieving significant improvements in classification accuracy.

ANFIS-AMAL: Android Malware Threat Assessment Using Ensemble of ANFIS and GWO

Abstract The Android malware has various features and capabilities. Various malware has distinctive characteristics. Ransomware threatens financial loss and system lockdown. This paper proposes a threat-assessing approach using the Grey Wolf Optimizer (GWO) to train and tune the Adaptive Neuro-Fuzzy Inference System (ANFIS) to categorize Android malware accurately. GWO improves efficiency and efficacy in ANFIS training and learning for Android malware feature selection and classification. Our approach categorizes Android malware as a high, moderate, or low hazard. The proposed approach qualitatively assesses risk based on critical features and threats. Our threat-assessing mechanism’s scale categorizes Android malware. The proposed approach resolves the issue of overlapping features in different types of malware. Comparative results with other classifiers show that the ensemble of GWO is effective in the training and learning process of ANFIS and thus achieves 95% F-score, 94% specificity, and 94% accuracy. The ensemble makes fast learning possible and improves classification accuracy.

Preparation for CSST: Star-galaxy Classification using a Rotationally Invariant Supervised Machine Learning Method

Most existing star-galaxy classifiers depend on the reduced information from catalogs, necessitating careful data processing and feature extraction. In this study, we employ a supervised machine learning method (GoogLeNet) to automatically classify stars and galaxies in the COSMOS field. Unlike traditional machine learning methods, we introduce several preprocessing techniques, including noise reduction and the unwrapping of denoised images in polar coordinates, applied to our carefully selected samples of stars and galaxies. By dividing the selected samples into training and validation sets in an 8:2 ratio, we evaluate the performance of the GoogLeNet model in distinguishing between stars and galaxies. The results indicate that the GoogLeNet model is highly effective, achieving accuracies of 99.6% and 99.9% for stars and galaxies, respectively. Furthermore, by comparing the results with and without preprocessing, we find that preprocessing can significantly improve classification accuracy (by approximately 2.0% to 6.0%) when the images are rotated. In preparation for the future launch of the China Space Station Telescope (CSST), we also evaluate the performance of the GoogLeNet model on the CSST simulation data. These results demonstrate a high level of accuracy (approximately 99.8%), indicating that this model can be effectively utilized for future observations with the CSST.

Scalable CNN-based classification of selective sweeps using derived allele frequencies.

Selective sweeps can successfully be distinguished from neutral genetic data using summary statistics and likelihood-based methods that analyze single nucleotide polymorphisms (SNPs). However, these methods are sensitive to confounding factors, such as severe population bottlenecks and old migration. By virtue of machine learning, and specifically convolutional neural networks (CNNs), new accurate classification models that are robust to confounding factors have been recently proposed. However, such methods are more computationally expensive than summary-statistic-based ones, yielding them impractical for processing large-scale genomic data. Moreover, SNP data are frequently preprocessed to improve classification accuracy, further exacerbating the long analysis times. To this end, we propose a 1D CNN-based model, dubbed FAST-NN, that does not require any preprocessing while using only derived allele frequencies instead of summary statistics or raw SNP data, thereby yielding a sample-size-invariant, scalable solution. We evaluated several data fusion approaches to account for the variance of the density of genetic diversity across genomic regions (a selective sweep signature), and performed an extensive neural architecture search based on a state-of-the-art reference network architecture (SweepNet). The resulting model, FAST-NN, outperforms the reference architecture by up to 12% inference accuracy over all challenging evolutionary scenarios with confounding factors that were evaluated. Moreover, FAST-NN is between 30× and 259× faster on a single CPU core, and between 2.0× and 6.2× faster on a GPU, when processing sample sizes between 128 and 1000 samples. Our work paves the way for the practical use of CNNs in large-scale selective sweep detection. https://github.com/SjoerdvandenBelt/FAST-NN.

Local means-based fuzzy k-nearest neighbor classifier with Minkowski distance and relevance-complementarity feature weighting

This paper introduces an enhanced fuzzy k-nearest neighbor (FKNN) approach called the feature-weighted Minkowski distance and local means-based fuzzy k-nearest neighbor (FWM-LMFKNN). This method improves classification accuracy by incorporating feature weights, Minkowski distance, and class representative local mean vectors. The feature weighting process is developed based on feature relevance and complementarity. We improve the distance calculations between instances by utilizing feature information-based weighting and Minkowski distance, resulting in a more precise set of nearest neighbors. Furthermore, the FWM-LMFKNN classifier considers the local structure of class subsets by using local mean vectors instead of individual neighbors, which improves its classification performance. Empirical results using twenty different real-world data sets demonstrate that the proposed method achieves statistically significantly higher classification performance than traditional KNN, FKNN, and six other related state-of-the-art methods.

Improving AI-assisted video editing: Optimized footage analysis through multi-task learning

In recent years, AI-assisted video editing has shown promising applications. Understanding and analyzing camera language accurately is fundamental in video editing, guiding subsequent editing and production processes. However, many existing methods for camera language analysis overlook computational efficiency and deployment requirements in favor of improving classification accuracy. Consequently, they often fail to meet the demands of scenarios with limited computing power, such as mobile devices. To address this challenge, this paper proposes an efficient multi-task camera language analysis pipeline based on shared representations. This approach employs a multi-task learning architecture with hard parameter sharing, enabling different camera language classification tasks to utilize the same low-level feature extraction network, thereby implicitly learning feature representations of the footage. Subsequently, each classification sub-task independently learns the high-level semantic information corresponding to the camera language type. This method significantly reduces computational complexity and memory usage while facilitating efficient deployment on devices with limited computing power. Furthermore, to enhance performance, we introduce a dynamic task priority strategy and a conditional dataset downsampling strategy. The experimental results demonstrate that achieved a comprehensive accuracy surpassing all previous methods. Moreover, training time was reduced by 66.33%, inference cost decreased by 59.85%, and memory usage decreased by 31.95% on the 2-task dataset MovieShots; on the 4-task dataset AVE, training time was reduced by 95.34%, inference cost decreased by 97.23%, and memory usage decreased by 61.21%.

Migrating birds optimization-based feature selection for text classification

Text classification tasks, particularly those involving a large number of features, pose significant challenges in effective feature selection. This research introduces a novel methodology, MBO-NB, which integrates Migrating Birds Optimization (MBO) approach with naïve Bayes as an internal classifier to address these challenges. The motivation behind this study stems from the recognized limitations of existing techniques in efficiently handling extensive feature sets. Traditional approaches often fail to adequately streamline the feature selection process, resulting in suboptimal classification accuracy and increased computational overhead. In response to this need, our primary objective is to propose a scalable and effective solution that enhances both computational efficiency and classification accuracy in text classification systems. To achieve this objective, we preprocess raw data using the Information Gain algorithm, strategically reducing the feature count from an average of 62,221 to 2,089. Through extensive experiments, we demonstrate the superior effectiveness of MBO-NB in feature reduction compared to other existing techniques, resulting in significantly improved classification accuracy. Furthermore, the successful integration of naïve Bayes within MBO offers a comprehensive and well-rounded solution to the feature selection problem. In individual comparisons with Particle Swarm Optimization (PSO), MBO-NB consistently outperforms by an average of 6.9% across four setups. This research provides valuable insights into enhancing feature selection methods, thereby contributing to the advancement of text classification techniques. By offering a scalable and effective solution, MBO-NB addresses the pressing need for improved feature selection methods in text classification, thereby facilitating the development of more robust and efficient classification systems.

Advancing emotion recognition in facial expressions through PCA, RFE, and MLP Integration

Emotion recognition through facial expressions is vital for enhancing human-computer interaction, making systems more intuitive and responsive to user needs. This study introduces an innovative approach to emotion detection, leveraging Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) for feature extraction and optimization. The methodology focuses on refining facial feature representations to improve classification accuracy, which is critical for accurately detecting emotions like happiness, sadness, fear, and surprise. The approach was applied to two widely recognized facial emotion datasets: the Cohn-Kanade (CK) and the Japanese Female Facial Expression (JAFFE) datasets. By integrating PCA and RFE, the model efficiently selects the most relevant features, enhancing the overall performance of emotion recognition. A comparative analysis with existing deep learning models highlights the advantages of the proposed method. The effectiveness of this approach is further supported by the results, where the model achieves an accuracy of 98.49% on the CK dataset and 96.03% on the JAFFE dataset. These results demonstrate a significant improvement over recent methods, indicating the model's potential for real-world applications.

Seagrass classification using unsupervised curriculum learning (UCL)

Seagrass ecosystems are pivotal in marine environments, serving as crucial habitats for diverse marine species and contributing significantly to carbon sequestration. Accurate classification of seagrass species from underwater images is imperative for monitoring and preserving these ecosystems. This paper introduces Unsupervised Curriculum Learning (UCL) to seagrass classification using the DeepSeagrass dataset. UCL progressively learns from simpler to more complex examples, enhancing the model's ability to discern seagrass features in a curriculum-driven manner. Experiments employing state-of-the-art deep learning architectures, convolutional neural networks (CNNs), show that UCL achieved overall 90.12 % precision and 89 % recall, which significantly improves classification accuracy and robustness, outperforming some traditional supervised learning approaches like SimCLR, and unsupervised approaches like Zero-shot CLIP. The methodology of UCL involves four main steps: high-dimensional feature extraction, pseudo-label generation through clustering, reliable sample selection, and fine-tuning the model. The iterative UCL framework refines CNN's learning of underwater images, demonstrating superior accuracy, generalization, and adaptability to unseen seagrass and background samples of undersea images. The findings presented in this paper contribute to the advancement of seagrass classification techniques, providing valuable insights into the conservation and management of marine ecosystems. The code and dataset are made publicly available and can be assessed here: https://github.com/nabid69/Unsupervised-Curriculum-Learning—UCL.

Semantic Interaction Meta-Learning Based on Patch Matching Metric.

Metric-based meta-learning methods have demonstrated remarkable success in the domain of few-shot image classification. However, their performance is significantly contingent upon the choice of metric and the feature representation for the support classes. Current approaches, which predominantly rely on holistic image features, may inadvertently disregard critical details necessary for novel tasks, a phenomenon known as "supervision collapse". Moreover, relying solely on visual features to characterize support classes can prove to be insufficient, particularly in scenarios involving limited sample sizes. In this paper, we introduce an innovative framework named Patch Matching Metric-based Semantic Interaction Meta-Learning (PatSiML), designed to overcome these challenges. To counteract supervision collapse, we have developed a patch matching metric strategy based on the Transformer architecture to transform input images into a set of distinct patch embeddings. This approach dynamically creates task-specific embeddings, facilitated by a graph convolutional network, to formulate precise matching metrics between the support classes and the query image patches. To enhance the integration of semantic knowledge, we have also integrated a label-assisted channel semantic interaction strategy. This strategy merges word embeddings with patch-level visual features across the channel dimension, utilizing a sophisticated language model to combine semantic understanding with visual information. Our empirical findings across four diverse datasets reveal that the PatSiML method achieves a classification accuracy improvement of 0.65% to 21.15% over existing methodologies, underscoring its robustness and efficacy.

Optimized deep CNN for detection and classification of diabetic retinopathy and diabetic macular edema

Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) are vision related complications prominently found in diabetic patients. The early identification of DR/DME grades facilitates the devising of an appropriate treatment plan, which ultimately prevents the probability of visual impairment in more than 90% of diabetic patients. Thereby, an automatic DR/DME grade detection approach is proposed in this work by utilizing image processing. In this work, the retinal fundus image provided as input is pre-processed using Discrete Wavelet Transform (DWT) with the aim of enhancing its visual quality. The precise detection of DR/DME is supported further with the application of suitable Artificial Neural Network (ANN) based segmentation technique. The segmented images are subsequently subjected to feature extraction using Adaptive Gabor Filter (AGF) and the feature selection using Random Forest (RF) technique. The former has excellent retinal vein recognition capability, while the latter has exceptional generalization capability. The RF approach also assists with the improvement of classification accuracy of Deep Convolutional Neural Network (CNN) classifier. Moreover, Chicken Swarm Algorithm (CSA) is used for further enhancing the classifier performance by optimizing the weights of both convolution and fully connected layer. The entire approach is validated for its accuracy in determination of grades of DR/DME using MATLAB software. The proposed DR/DME grade detection approach displays an excellent accuracy of 97.91%.

Research on improvement strategies for a lightweight multi-object weed detection network based on YOLOv5

Traditional weed detection technology has several limitations, including low detection accuracy, substantial computational demands, and large-scale detection models. To meet the requirements of weed multi-target identification and portability, this study proposes the YOLO–WEED model for weed recognition. The proposed model has the following innovations: (1) The backbone standard convolution module in YOLOv5 was replaced by the lightweight MobileNetv3 network to simplify the network structure and reduce parameter complexity; (2) The addition of convolutional block attention module (CBAM) to the neck network enabled the model to focus on the most important features while filtering out noise and irrelevant information; (3) To further improve classification accuracy and reduce loss, the C2f module was employed to improve the C3 module in the neck network; and (4) During the model plot process, a coordinate variable was added in the box label to help the model accurately locate the weeds. In the study, six species of weeds and one crop were used as test subjects. After image enhancement techniques were used, ablation experiments were deployed. The experimental results indicated that the YOLO–WEED model achieved an average accuracy of 92.5% in identifying six types of weeds and one type of crop. The accuracies for each type of plant were 82.7%, 97.3%, 98.8%, 86%, 93.5%, 99.3% and 89.6%, respectively. The number of model parameters was reduced by 39.4% compared with YOLOv5s. Furthermore, the localisation, classification and object losses were reduced by 0.025, 0.005 and 0.014, respectively. The model optimisation and deployment of the Jetson mobile terminal for multi-target detection were realised, and the performance was better than six network models such as YOLOv5s.

Enhanced brain tumor classification using EfficientNetB0 and SVM with pareto search algorithm optimization

Classifying tumors by type, grade, and stage is crucial for treatment decisions and predicting outcomes. Deep learning, especially Convolutional Neural Networks (CNNs), has significantly advanced tumor classification by effectively analyzing complex patterns in magnetic resonance (MR) images. This work presents a hybrid image classification method using the EfficientNetB0 model and Support Vector Machine (SVM) to categorize brain MR images into pituitary tumor, glioma tumor, meningioma tumor, and normal brain. EfficientNetB0 model extracts deep features from the images, which are then classified by a linear SVM. To significantly enhance classification accuracy for brain images, we use the Pareto algorithm to determine the penalty parameter C for the linear SVM. The testing results showed that the proposed system achieved a classification accuracy of 99.30%, recall of 99.30%, precision of 99.30%, and F1-score of 99.30%, with a high specificity of 99.77%. These results demonstrate that the combination of the Pareto algorithm and SVM significantly contributes to improved classification accuracy for brain images.

A hybrid transposed attention based deep learning model for wearable and explainable stress recognition

Stress is a prevalent issue in modern society which affects a large percentage of the population. Stress has negative effects on human daily lives such as reduced memory and concentration levels, forgetfulness, restlessness, and weakness. Although it is challenging to extinguish stress from human lives, monitoring and controlling its consequences can be implemented. Current methods for stress monitoring often lack interpretability of results and making it difficult to provide an interpretable system for immediate mental health counseling and intervention. Wearable devices are of growing interest due to the impact of the COVID-19 pandemic on mental health, as wearables provide monitoring of physiological parameters which helps in measuring stress levels. Here, an investigation on the feasibility of using wrist and chest-based multimodal physiological signals collected from wearables is executed for stress classification and identification of underlying modalities that impact the stress. Multimodalities from wrist and chest sensors are used to examine the three-class classification, baseline, stress and amusement. For improved and visualized effects, the multimodal signals from wearables were transformed into images. Then, a hybrid model was introduced, which comprised of attentive convolution neural network (CNN), transposed attentive CNN connections with long-short term memory (LSTM) and followed by attention layer for efficient feature extraction and improved classification accuracy. Results found that the proposed model with images provided 97 % accuracy and 96 % F1-score for chest wearable and 94 % accuracy and 93 % F1-score for wrist wearable data. Additionally, the post-hoc explainability approach (Local interpretable model agnostic applications, LIME) provided visual representations of contributing features from each signal for each class. LIME shows electrodermal activity (EDA), temperature (TEMP) and respiration (RESP) to be the significant factor in stress recognition from chest wearable and blood volume pulse (BVP) and EDA from wrist wearable. Additionally, increasing the number of features of the explainable model influences the modalities influence on the model explainability. The results establish a benchmark for explainable stress recognition using different sensor data.

A diffusion model multi-scale feature fusion network for imbalanced medical image classification research

Background and objectiveMedicine image classification are important methods of traditional medical image analysis, but the trainable data in medical image classification is highly imbalanced and the accuracy of medical image classification models is low. In view of the above two common problems in medical image classification. This study aims to: (i) effectively solve the problem of poor training effect caused by the imbalance of class imbalanced data sets. (ii) propose a network framework suitable for improving medical image classification results, which needs to be superior to existing methods. MethodsIn this paper, we put in the diffusion model multi-scale feature fusion network (DMSFF), which mainly uses the diffusion generation model to overcome imbalanced classes (DMOIC) on highly imbalanced medical image datasets. At the same time, it is processed according to the cropped image augmentation strategy through cropping (IASTC). Based on this, we use the new dataset to design a multi-scale feature fusion network (MSFF) that can fully utilize multiple hierarchical features. The DMSFF network can effectively solve the problems of small and imbalanced samples and low accuracy in medical image classification. ResultsWe evaluated the performance of the DMSFF network on highly imbalanced medical image classification datasets APTOS2019 and ISIC2018. Compared with other classification models, our proposed DMSFF network achieved significant improvements in classification accuracy and F1 score on two datasets, reaching 0.872, 0.731, and 0.906, 0.836, respectively. ConclusionsOur newly proposed DMSFF architecture outperforms existing methods on two datasets, and verifies the effectiveness of generative model inverse balance for imbalance class datasets and feature enhancement by multi-scale feature fusion. Further, the method can be applied to other class imbalanced data sets where the results will be improved.

MTLSC-Diff: Multitask learning with diffusion models for hyperspectral image super-resolution and classification

Hyperspectral image (HSIs) super-resolution (SR) can improve the spatial resolution of images for subsequent application tasks. In recent years, SR methods based on deep learning have gained widespread attention. However, most of the existing SR methods do not take into account the needs of specific application tasks when designing the network structure. These methods may not be able to efficiently generate high-quality images that satisfy the specific application tasks, leading to degradation of the performance of subsequent application tasks. To solve this problem, we propose a multi-task learning architecture based on the diffusion model, namely MTLSC-Diff. MTLSC-Diff combines the SR network and the classification network in a multi-task learning manner on the basis of the diffusion model. MTLSC-Diff achieves mutual guidance of the two tasks by iterating the image super-resolution and classification tasks, thus gradually reconstructing high-quality images and improving classification accuracy. The guided operations for each time step are performed by the specially designed Mutual-Guidance SR-Classification Synergy Module (M-GSCS). M-GSCS refines the multi-scale image obtained at the previous time step and uses the predicted high spatial resolution image for classification. Meanwhile, a class-guided SR dynamic refinement strategy (C-GSR) is proposed in M-GSCS, which uses multi-scale classification results to guide target scale images to learn new knowledge to further reconstruct high-quality images. Experimental results on relevant datasets show that our method significantly improves the super-resolution performance as well as the classification performance.

Artificial Intelligence-Based Classification of CT Images Using a Hybrid SpinalZFNet.

The kidney is an abdominal organ in the human body that supports filtering excess water and waste from the blood. Kidney diseases generally occur due to changes in certain supplements, medical conditions, obesity, and diet, which causes kidney function and ultimately leads to complications such as chronic kidney disease, kidney failure, and other renal disorders. Combining patient metadata with computed tomography (CT) images is essential to accurately and timely diagnosing such complications. Deep Neural Networks (DNNs) have transformed medical fields by providing high accuracy in complex tasks. However, the high computational cost of these models is a significant challenge, particularly in real-time applications. This paper proposed SpinalZFNet, a hybrid deep learning approach that integrates the architectural strengths of Spinal Network (SpinalNet) with the feature extraction capabilities of Zeiler and Fergus Network (ZFNet) to classify kidney disease accurately using CT images. This unique combination enhanced feature analysis, significantly improving classification accuracy while reducing the computational overhead. At first, the acquired CT images are pre-processed using a median filter, and the pre-processed image is segmented using Efficient Neural Network (ENet). Later, the images are augmented, and different features are extracted from the augmented CT images. The extracted features finally classify the kidney disease into normal, tumor, cyst, and stone using the proposed SpinalZFNet model. The SpinalZFNet outperformed other models, with 99.9% sensitivity, 99.5% specificity, precision 99.6%, 99.8% accuracy, and 99.7% F1-Score in classifying kidney disease.

A multiscale dilated attention network for hyperspectral image classification

Hyperspectral imaging is an image obtained by combining spectral detection technology and imaging technology, which can collect electromagnetic spectra in the wavelength range of visible light to near-infrared. It is an important research content in the field of ground observation in hyperspectral remote sensing. However, hyperspectral image face significant challenges in classification task due to their high spectral dimensions, lack of labeled samples, and strong correlation between bands. In order to fully extract features from both spectral and spatial dimensions and improve classification accuracy in the case of limited training samples, a multiscale dilated attention network is proposed for hyperspectral image classification. First, a three-dimensional convolutional layer is used to extract the shallow features of the image. Then, a multiscale dilated attention module is proposed by combining dilated convolution and channel attention. Using ordinary convolution and dilated convolution to form different receptive fields. Channel attention is used to remodel the obtained multiscale features, enhancing the inter-channel correlation. After that, a multiscale spatial-spectral attention module is constructed using multiple asymmetric convolutions to obtain spatial and spectral attention features at different positions, further enhancing important feature suppression over non-important features. Finally, using softmax to classify the obtained features. Using Indian Pines, Pavia University, KSC and University of Houston as experimental datasets, the overall classification accuracy of this paper’s method achieved 98.97%, 99.14%, 99.45%, and 98.56% respectively, using only 5%, 1%, 10%, and 10% of training samples per class. Compared with seven advanced classification methods, the experimental results show that the proposed method can achieve the highest classification accuracy with limited training samples.

Smooth Ordered Weighted Averaging operators

The study demonstrates the application of OWA operators to binary and multiclass classification problems and seeks a way to improve classification accuracy using smoothing methods. OWA operators are used to aggregate class membership probabilities obtained from individual classifiers. Smoothing methods inspired by Newton-Cotes quadratures are applied before the aggregation step to improve the quality of the final results. Moreover, several sets of weights are used for OWA operators, including sets of weights based on the accuracy of individual classifiers. The experiments are conducted on 20 datasets, from which 7 are designed for binary classification and the rest are for multiclass classification. A comparison of the average accuracy for different sets of weights is shown. On the basis of experimental results, smoothing methods that significantly improve classification accuracy are identified.

Peptide clustering enhances large-scale analyses and reveals proteolytic signatures in mass spectrometry data

Recent advances in mass spectrometry-based peptidomics have catalyzed the identification and quantification of thousands of endogenous peptides across diverse biological systems. However, the vast peptidomic landscape generated by proteolytic processing poses several challenges for downstream analyses and limits the comparability of clinical samples. Here, we present an algorithm that aggregates peptides into peptide clusters, reducing the dimensionality of peptidomics data, improving the definition of protease cut sites, enhancing inter-sample comparability, and enabling the implementation of large-scale data analysis methods akin to those employed in other omics fields. We showcase the algorithm by performing large-scale quantitative analysis of wound fluid peptidomes of highly defined porcine wound infections and human clinical non-healing wounds. This revealed signature phenotype-specific peptide regions and proteolytic activity at the earliest stages of bacterial colonization. We validated the method on the urinary peptidome of type 1 diabetics which revealed potential subgroups and improved classification accuracy.