Classification Accuracy Of Data Research Articles

Identifying Autism Spectrum Disorder Based on Machine Learning for Multi-site fMRI.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by repetitive stereotypical behavior and social impairment. Early diagnosis is essential for developing a treatment plan for autism. Although multi-site data can expand the dataset to facilitate the process of data analysis, data heterogeneity between sites and the large amount of data make data analysis difficult. To address these issues, this paper proposes a multi-site autism identification method based on machine learning technique. Firstly, the fMRI data from all sites are converted into a glass brain dataset and their features are extracted with LeNet5. Then, the extracted glass brain features are used to construct a partial correlation matrix at subject-level and the multi-site dataset are constructed by feature selection, which is finally classified using MLP. In order to alleviate the heterogeneity of the data and improve the accuracy of data classification, a new dataset partitioning method, Split-Merge-Split (SMS), is proposed in this paper to reduce the variability between the features extracted by the model in the training and test sets. Extensive quantitative and qualitative evaluations demonstrate the proposed method enhanced the recognition accuracy on both single-site and multi-site dataset, which shows the effectiveness of this method. Specifically, in single-site classification, our method achieved its highest accuracy at the OHSU site, reaching an accuracy of 93%. In multi-site classification, our method attained an accuracy of 83.5%.

Influential Features Analysis And AI-Driven Accuracy Enhancement: A Study Case For DDoS Detection

Cybersecurity is known today as one of the greatest challenges of the modern era. Among the various types of cyber attacks that threaten our security, the Distributed Denial of Service (DDoS) attack is among some of the most common, effective, and well-recognized attack strategies. Since this form of attack is meant to disrupt the availability factor covertly, it can be detrimental to the targeted machines and difficult to be discovered. Because of that, there have been a number of approaches, as well as solutions that have been devised in order to detect it as accurately and efficiently as possible. Impressively, data mining methods have been employed to identify patterns of DDoS attacks from the computer network traffic. Nevertheless, the recent works’ results have not yet mentioned which factors of the computer network traffic play the most vital role in indicating the potential for true positive attacks. Additionally, with the Machine Learning approach, there are still ample opportunities to enhance the attack prediction accuracy of the detection model. As such, in this paper, we attempt to explore factors that would influence the classification result, and leverage a variety of Machine Learning algorithms, i.e. Random Forest, Naive Bayes, Logistic Regression, and Multilayer Perceptron, for the purpose of improving the accuracy of data classification. The experiments were deployed using CICIDS2017 dataset and compared with the other related works on the same dataset. The experimental outcomes of our methodologies and analyses demonstrate some potential and effectiveness enhancement compared to previous works. Moreover, we analysized and concluded the insight of how side factors affect the attack identification result. The collected information from our analysis identifies dominant factors, and opens a new view for their hidden correlationship directly affecting the attack labeling.

The Effect of Processing Techniques on the Classification Accuracy of Brain-Computer Interface Systems.

Background/Objectives: Accurately classifying Electroencephalography (EEG) signals is essential for the effective operation of Brain-Computer Interfaces (BCI), which is needed for reliable neurorehabilitation applications. However, many factors in the processing pipeline can influence classification performance. The objective of this study is to assess the effects of different processing steps on classification accuracy in EEG-based BCI systems. Methods: This study explores the impact of various processing techniques and stages, including the FASTER algorithm for artifact rejection (AR), frequency filtering, transfer learning, and cropped training. The Physionet dataset, consisting of four motor imagery classes, was used as input due to its relatively large number of subjects. The raw EEG was tested with EEGNet and Shallow ConvNet. To examine the impact of adding a spatial dimension to the input data, we also used the Multi-branch Conv3D Net and developed two new models, Conv2D Net and Conv3D Net. Results: Our analysis showed that classification accuracy can be affected by many factors at every stage. Applying the AR method, for instance, can either enhance or degrade classification performance, depending on the subject and the specific network architecture. Transfer learning was effective in improving the performance of all networks for both raw and artifact-rejected data. However, the improvement in classification accuracy for artifact-rejected data was less pronounced compared to unfiltered data, resulting in reduced precision. For instance, the best classifier achieved 46.1% accuracy on unfiltered data, which increased to 63.5% with transfer learning. In the filtered case, accuracy rose from 45.5% to only 55.9% when transfer learning was applied. An unexpected outcome regarding frequency filtering was observed: networks demonstrated better classification performance when focusing on lower-frequency components. Higher frequency ranges were more discriminative for EEGNet and Shallow ConvNet, but only when cropped training was applied. Conclusions: The findings of this study highlight the complex interaction between processing techniques and neural network performance, emphasizing the necessity for customized processing approaches tailored to specific subjects and network architectures.

Enhancing Healthcare Through Metaheuristic Based Deep Learning: Microarray Gene Expression Image Classification Model

Microarray gene expression data analysis has revolutionized genomics by providing insights into the genetic basis of diseases, including cancer. However, the high dimensionality of microarray data poses significant challenges for accurate classification, often leading to overfitting and poor generalization. This study proposes a novel framework that integrates the DNA Bidirectional Encoder Representations from Transformers (DNABERT) with the Mayfly Optimization Algorithm (MFO) to improve the classification accuracy of microarray gene expression data. DNABERT, a transformer-based model pretrained on genomic sequences, excels in learning complex gene interactions through bidirectional contextual embeddings. MFO, a bio-inspired optimization technique, addresses feature selection and hyperparameter tuning by balancing exploration and exploitation in high-dimensional search spaces. The framework, DNABERT-MFO, leverages MFO to identify the most relevant gene subsets and optimize DNABERT's hyperparameters, improving the model’s performance. Evaluations conducted on multiple microarray datasets, demonstrate that DNABERT-MFO with overall accuracy of above 80% significantly outperforms traditional machine learning methods and standalone deep learning models in classification accuracy. This integrated method not only enhances the robustness of gene expression data analysis but also offers a powerful tool for research and clinical applications in genomics. The proposed method addresses key limitations of existing techniques and provides a promising avenue for future advancements in gene expression classification.

Practical classification accuracy of sequential data using neural networks

Practical classification accuracy of sequential data using neural networks

Assessment of land use and land cover change detection for Argungu Urban: A Remote Sensing and GIS Approach

Urbanization is a significant driver of land use and land cover (LULC) changes, impacting environmental and socio-economic systems. Accurate monitoring and analysis of urban LULC changes are crucial for sustainable urban planning and management. This study employed remote sensing and Geographic Information Systems (GIS) techniques to detect and analyze LULC changes in Argungu urban, Nigeria, between 2013 and 2023. Landsat 8 images were classified using supervised classification, revealing significant changes: 9.5% expansion of built-up areas, -32.9% decrease in vegetation cover, and 23.3% increase in bare soil areas. The study highlights rapid urbanization and infrastructure development, potentially leading to environmental degradation. However, limitations include data quality, classification accuracy, and temporal resolution. Future research should integrate high-resolution imagery, additional data sources, and predictive models to enhance understanding of LULC changes. This study informs sustainable urban planning, environmental management, and policy-making in Argungu urban.

Detection and restoration of abnormal band data in photometric images

Addressing the issue of abnormal band data processing in photometric surveys is imperative. Restoring of abnormal band data not only salvages a significant amount of existing astronomical observation data, but also has profound implications on the data processing of new optical telescopes in the future. This paper firstly designs Band Data MogaNet(BDMogaNet) classification model for normal or abnormal band data, which can automatically identify abnormal data. Then, for the restoration of abnormal band data, Global–Local Recursive Generalization(GLRG) restoration network is designed. The experiment used the SDSS image library, and the results proved that the classification accuracy of normal band data and abnormal band data using BDMogaNet reached 99.2% in the training set and 98.0% in the validation set, which had a better classification comparing to some newest methods. Moreover, PSNR of restoring abnormal band data using GLRG reached 33.96 dB, SSIM reached 0.73, and CM reached 6.09, which are all better compared to some newest methods.

Small-sample cucumber disease identification based on multimodal self-supervised learning

It is difficult and costly to obtain large-scale, labeled crop disease data in the field of agriculture. How to use small samples of unlabeled data for feature learning has become an urgent problem that needs to be solved. The emergence of self-supervised contrastive learning methods and self-supervised mask learning methods can solve the problem of missing labels on the training data. However, each of these paradigms comes with its own advantages and drawbacks. At the same time, the features learned by dataset in a single modality are limited, ignoring the correlation with other modal information. Hence, this paper introduced an effective framework for multimodal self-supervised learning, denoted as MMSSL, to address the task of identifying cucumber diseases with small sample sizes. Integrating image self-supervised mask learning, image self-supervised contrastive learning, and multimodal image-text contrastive learning, the model can not only learn disease feature information from different modalities, but also capture global and local disease feature information. Simultaneously, the mask learning branch was enhanced by introducing a prompt learning module based on a cross-attention network. This module aided in approximately locating the masked regions in the image data in advance, facilitating the decoder in making accurate decoding predictions. Experimental results demonstrate that the proposed method achieves a 95% accuracy in cucumber disease identification in the absence of labels. The approach effectively uncovers high-level semantic features within multimodal small-sample cucumber disease data. GradCAM is also employed for visual analysis to further understand the decision-making process of the model in disease identification. In conclusion, the proposed method in this paper is advantageous for enhancing the classification accuracy of small-sample cucumber data in a multimodal, unlabeled context, demonstrating good generalization performance.

An Adaptive Transfer Learning Framework for Functional Classification

In this article, we study the transfer learning problem in functional classification, aiming to improve the classification accuracy of the target data by leveraging information from related source datasets. To facilitate transfer learning, we propose a novel transferability function tailored for classification problems, enabling a more accurate evaluation of the similarity between source and target dataset distributions. Interestingly, we find that a source dataset can offer more substantial benefits under certain conditions than another dataset with an identical distribution to the target dataset. This observation renders the commonly-used debiasing step in the parameter-based transfer learning algorithm unnecessary under some circumstances to the classification problem. In particular, we propose two adaptive transfer learning algorithms based on the functional Distance Weighted Discrimination (DWD) classifier for scenarios with and without prior knowledge regarding informative sources. Furthermore, we establish the upper bound on the excess risk of the proposed classifiers, providing the statistical gain via transfer learning mathematically provable. Simulation studies are conducted to thoroughly examine the finite-sample performance of the proposed algorithms. Finally, we implement the proposed method to Beijing air-quality data, and significantly improve the prediction of the PM 2.5 level of a target station by effectively incorporating information from source datasets. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Application Research of Power Measurement Data Exchange Based on Uninterruptible Power Supply

With the maturity of the smart meter embedded system integration mode, the power consumption information can be fed back to the management center by the intelligent terminal through the power Internet of Things. The smart terminal adopts the narrowband data communication mode. However, the narrowband channel interval is hindered by interference signals, resulting in serious data packet loss. Therefore, a stable and efficient acquisition scheme is needed to ensure its smooth interaction. Based on the application of smart meter data communication, we analyze the power consumption optimization and carrier transmission optimization of the power Internet of Things and propose a data analysis method based on self-learning and external characteristics, which is used to calculate the offline execution strategy of smart meters when they encounter factors such as power outage or intrusion. A production task-driving rule is designed. The external characteristics of deep learning data of the power Internet of Things are collected. The accuracy of data classification is judged according to the parallel Internet of Things of a convolutional neural network and a long-term and short-term memory deep neural network. When dealing with high-dimensional original data, the data network is graded to reduce the work of feature selection and the difficulty of training. Moreover, we improve the autonomous execution ability of an embedded terminal device in an offline state. We also strengthen the ability of abnormal data detection and judgment learning to meet the requirements of efficient and stable operation of the power grid. Simulation results verify that compared with the current mainstream schemes, the performance of the proposed scheme is improved by more than 17%. Besides, the fault tolerance rate is enhanced by 29%, and the power consumption after optimization is reduced by 26.3% and 42.8%. These effects highlight the role of data processing in embedded systems. It solves the problem of abnormal data and energy loss affecting the accuracy of error estimation of electric energy meters, opens up a new way for data acquisition and processing of large-scale smart meters, and further improves the service level of information management of power systems.

Information technology for image data processing based on hybrid neural networks using geometric features

Abstract. Progress in computing technology has led to a steady increase in computing power, resulting in an exponential growth in the amount of data that needs to be processed. In particular, the enhanced performance of automated systems enables the storage and analysis of large volumes of medical data with high speed and accuracy. Modern medicine is characterized by a significant increase in the information load, necessitating complex processing and in-depth analysis to support clinical decision-making. Information technology plays a pivotal role in ensuring efficient processing of these large datasets, contributing to the accuracy and speed of diagnosis, as well as the effectiveness of subsequent patient treatment. The purpose of this article is to develop and study information technology for processing graphic data based on hybrid neural networks using geometric features of image objects. The paper proposes advanced machine learning methods, deep neural network architectures, and specialized tools for processing graphic data, such as OpenCV, TensorFlow, and others. The data processing workflow during the validation of the proposed methods and architectures included several stages: data pre-processing, model training, and thorough testing of the results. The developed information technology demonstrates a significant improvement in the accuracy of graphic data classification. Experimental studies have shown that the proposed approach ensures efficient processing of large volumes of biomedical data, as evidenced by the high accuracy and speed of analysis. In particular, the accuracy of pathology classification using hybrid neural networks increased by more than 11% compared to the results obtained using classical methods. The practical value of the developed technology lies in its high potential for use in the field of machine vision, including enhancing the efficiency of diagnosis and treatment of patients in the medical field. It can be integrated into modern decision support systems, providing more accurate and faster processing of medical images.

Classification of BPG-Based Lossy Compressed Noisy Images

Acquired remote sensing images can be noisy. This fact has to be taken into account in their lossy compression and classification. In particular, a specific noise filtering effect is usually observed due to lossy compression and this can be positive for classification. Classification can be also influenced by methodology of classifier learning. In this paper, we consider peculiarities of lossy compression of three-channel noisy images by better portable graphics (BPG) encoder and their further classification. It is demonstrated that improvement of data classification accuracy is not observed if a given image is compressed in the neighborhood of optimal operation point (OOP) and the classifier training is performed for the noisy image. Performance of neural network based classifier is studied. As demonstrated, its training for compressed remote sensing data is able to provide certain benefits compared to training for noisy (uncompressed) data. Examples for Sentinel data used in simulations are offered.

Channel adaptive CVFCN using a new transfer method for PolSAR terrain classification

ABSTRACT This paper mainly addresses the lack of labelled data and insufficient data utilization in PolSAR image classification. We propose a channel adaptive Complex-Valued Fully Convolutional Networks based on Transfer learning (TF-CVFCN). To solve the problem of less labelled data, we borrowed from the transfer learning method, which can use a large amount of easily obtained relevant or even irrelevant data for pre-training and then use a small amount of source data for network training fine-tuning. Since PolSAR data can obtain different data information by using different decomposition methods, the current research trend is to process PolSAR data in the complex-valued containing more information. In our work, to cater to the proposed network model and make the network design more liberal, a new transfer learning method is proposed, which uses a new method to realize partial layer parameter transfer, and achieves the purpose of optimizing the traditional Transfer learning method. On this basis, we optimize the network structure to improve the classification accuracy of PolSAR data, and evaluate its performance on three datasets. Furthermore, we lighten the TF-CVFCN and optimize the relevant parameters using Competitive Multi-Objective Particle Swarm Optimizer, emphasizing the importance of this parameter optimization in large networks.

Optimizing Capsule Network Performance with Enhanced Squash Function for Classification Large-Scale Bone Marrow Cells

Capsule networks (CapsNet) have emerged as a promising architectural framework for various machine-learning tasks and offer advantages in capturing hierarchical relationships and spatial hierarchies within data. One of the most crucial components of CapsNet is the squash function, which plays a pivotal role in transforming capsule activations. Despite the success achieved by standard squash functions, some limitations remain. The difficulty learning complex patterns with small vectors and vanishing gradients are major limitations. Standard squash functions may struggle to handle large datasets. We improve our methodology to enhance squash functions to address these challenges and build on our previous research, which recommended enhancing squash functions for future improvements. Thus, high-dimensional, and complex data scenarios improve CapsNet’s performance. Enhancing CapsNet for complex tasks like bone marrow (BM) cell classification requires optimizing its fundamental operations. Additionally, the squash function affects feature representation and routing dynamics. Additionally, this enhancement improves feature representation, preserves spatial relationships, and reduces routing information loss. The proposed method increased BM data classification accuracy from 96.99% to 98.52%. This shows that our method improves CapsNet performance, especially in complex and large-scale tasks like BM cells. Comparing the improved CapsNet model to the standard CapsNet across datasets supports the results. The enhanced squash CapsNet outperforms the standard model on MNIST, CIFAR-10, and Fashion MNIST with an accuracy of 99.83%, 73%, and 94.66%, respectively. These findings show that the enhanced squash function improves CapsNet performance across diverse datasets, confirms its potential for real-world machine learning applications, and highlight the necessity for additional research.

An Efficient and Versatile Variational Method for High-Dimensional Data Classification

High-dimensional data classification is a fundamental task in machine learning and imaging science. In this paper, we propose an efficient and versatile multi-class semi-supervised classification method for classifying high-dimensional data and unstructured point clouds. To begin with, a warm initialization is generated by using a fuzzy classification method such as the standard support vector machine or random labeling. Then an unconstraint convex variational model is proposed to purify and smooth the initialization, followed by a step which is to project the smoothed partition obtained previously to a binary partition. These steps can be repeated, with the latest result as a new initialization, to keep improving the classification quality. We show that the convex model of the smoothing step has a unique solution and can be solved by a specifically designed primal–dual algorithm whose convergence is guaranteed. We test our method and compare it with the state-of-the-art methods on several benchmark data sets. Thorough experimental results demonstrate that our method is superior in both the classification accuracy and computation speed for high-dimensional data and point clouds.

A Refined Approach Involving Feature Correlation Analysis Followed by Classification using Python for Gene Assortment

Objectives: To make a study on hybrid scheme of selecting a most representative subset of genes to improve microarray data classification accuracy using machine learning algorithm. Method: ML-based SGSA approach proposed here has two parts of execution on gene expression datasets. First it utilizes an entropy based IGKullback–Leibler divergence to select the most informative genes. Subsequently, an attribute evaluation performed using correlation-based feature selection. After that a random forest based classifier is employed with 10-fold cross-validation. The proposed method involves data pre processing, testing, training, fitting an algorithm and then finds the best accuracy with comparable CPU cost. Findings: The rationale behind this study is that only most informative genes are submitted to classifier for classification task. Proven accuracy by this approach is 98.48 for Lymphoma, 89.69 for Breast, 86.67 for CNS, 97.22 for Leukemia, 98.4 for Lung cancer, 96.6 for MLL, 97.21 for Ovarian cancer and 98.5 for SRBCT over traditional machine learning algorithms like naïve bias, J48 and SMO. These results demonstrate the effectiveness of the suggested approach in accurately classifying tumors. The numerical illustration also showed that the new estimator is more efficient in terms of CPU cost. Novelty: The major problem with traditional machine learning algorithms is that all features are treated equally important during the classification process which makes them susceptible to the influence of outliers and difficult to find a meaningful class in the dataset. In this study, classification accuracy is improved by processing the most informative features in the classifiers. The primary contribution of the research is a hybrid ML model which uses IG based feature selection followed by correlation analysis. The result is then fed to RF based classifier which significantly enhance accuracy as well as CPU cost. Keywords: Machine Learning, Classification, Correlation, Feature Selection, Gene Expression

Evidential dissonance measure in robust multi-view classification to resist adversarial attack

Multi-view learning is effective in improving data classification accuracy through integrating information from multiple sources. To guarantee the reliability of multi-view classification, the trusted multi-view classification methods have been explored. However, extant trusted multi-view classification methods are still vulnerable to the low-quality views containing adversarial samples. This vulnerability arises from a challenge in accurately assessing the quality of data views that are subjected to adversarial attacks. To address this issue, we propose a robust multi-view classification method with dissonance measure of adversarial samples. Specifically, the proposed method utilizes the evidential dissonance measure in subjective logic to assess the quality of data views when encountering adversarial attacks. Based on the dissonance measure, we further propose a strategy of dissonance-aware belief integration for multi-view information fusion and construct the inter-view evidential gradient penalty in the learning objective of multi-view classification to improve the model robustness to resist adversarial samples. Experiments on diverse multi-view datasets confirm the reliability and robustness of the proposed multi-view classification method.

Multi-Feature Cross Attention-Induced Transformer Network for Hyperspectral and LiDAR Data Classification

Transformers have shown remarkable success in modeling sequential data and capturing intricate patterns over long distances. Their self-attention mechanism allows for efficient parallel processing and scalability, making them well-suited for the high-dimensional data in hyperspectral and LiDAR imagery. However, further research is needed on how to more deeply integrate the features of two modalities in attention mechanisms. In this paper, we propose a novel Multi-Feature Cross Attention-Induced Transformer Network (MCAITN) designed to enhance the classification accuracy of hyperspectral and LiDAR data. The MCAITN integrates the strengths of both data modalities by leveraging a cross-attention mechanism that effectively captures the complementary information between hyperspectral and LiDAR features. By utilizing a transformer-based architecture, the network is capable of learning complex spatial-spectral relationships and long-range dependencies. The cross-attention module facilitates the fusion of multi-source data, improving the network’s ability to discriminate between different land cover types. Extensive experiments conducted on benchmark datasets demonstrate that the MCAITN outperforms state-of-the-art methods in terms of classification accuracy and robustness.

Comparison of Binary Logistic Regression and SVM to Classify Diabetes Sufferers

Diabetes is a chronic metabolic disorder characterized by high levels of glucose in the blood due to disruption of the insulin hormone which functions as a regulator of the balance of blood sugar levels. This disease continues to increase in prevalence in various countries, making it a global health problem. Diabetes has trigger factors that contribute to the incidence of the disease, such as age, gender, smoking habits, healthy eating patterns, high blood pressure, and others. Diagnosis of diabetes can be done by carrying out a fasting blood sugar test, a 2-hour postprandial (PP) blood sugar test, and a random blood sugar test. However, it is very possible for diagnoses made by health workers to have errors due to subjectivity and different experiences, so a fast and precise classification method is needed to classify patients undergoing diabetes examination based on variables related to diabetes. The classification method used in this research is binary logistic regression and Support Vector Machine (SVM). A similar study carried out classification of diabetes sufferers using the Naive Bayes and KNN methods by comparing the results with SVM, so in this study the binary logistic regression method and SVM will be used to determine the performance of the classification method. The data used is secondary data. Next, the data is divided into training and testing data. The analysis results show that the SVM method is slightly superior in classification accuracy of testing data, namely 97.75%. With this research, it is hoped that decisions on patients undergoing diabetes examination will be faster, more precise and effective, and classification methods with better performance can be applied

An Analysis on Intelligent Systems for Remote Sensing Satellite Image Processing and Classification

The integration of intelligent systems to the remote sensing satellite image processing and classification has greatly changed. This paper offers a synthesis of the subject, with respect to intelligent systems’ contribution to the improvement of these processes’ accuracy and speed. Accuracy of different methods such as machine learning algorithms, artificial neural networks, and deep learning techniques in the extraction of information from satellite image comprehension is considered a research interest. The presented problems and open issues are data complexity, feature extraction, and classification accuracy over the data, along with new methods in enhancing the intelligent systems to minimize those problems. It expands knowledge of intelligent systems’ contribution to remote sensing applications by outlining how these advancements have influenced the progression of image analysis for the given research goals. This research work gives the summary of our research by outlining the techniques used in the study, the problems solved, and the general outcomes of incorporating intelligent systems in the area of remote sensing and satellite image analysis. Accuracy analysis results for the SVM based methodology with spatial-spectral features include 90% of accuracy, 88% of the precision, and 90% of the F1-score, which in turn makes it easy to make sound decisions when using satellite imagery in different fields like agriculture, urban development, and environment.