Classification Techniques Research Articles

Hybrid convolutional network with enhanced graph attention mechanism (HCN-EGAM) for hyperspectral image classification

ABSTRACT Due to their reliance on superficial spectral or spatial information, current hyperspectral image classification techniques frequently fail to generate similarity measures that are accurate enough to create graph structures. We have developed a new end-to-end model, the hybrid convolutional network with enhanced graph attention mechanism (HCN-EGAM), to address this issue. It integrates an enhanced graph attention mechanism with 3D–2D hybrid convolution. In order to improve classification accuracy, this model synergistically uses the graph attention network module with the hybrid convolutional feature extraction module. The model starts by creating a 3D–2D hybrid convolutional network. This lets it quickly pull out deep spatial-spectral features that make different ground objects stand out in hyperspectral images. The model then employs these deep spatial-spectral properties to construct a graph structure, thereby enhancing its feature representation capabilities. Lastly, we use the Enhanced Graph Attention Mechanism (EGAM) to learn long-range spatial associations, successfully distinguishing between intra-class differences and inter-class similarities between distinct samples. Comparing our suggested method to other state-of-the-art methods, experimental assessments on three datasets, including the University of Pavia, Kennedy Space Center (KSC) and Indian Pines (IP) datasets, show a considerable improvement in classification accuracy.

2024/00264 Harnessing Transdisciplinary Knowledge: Integrated Deep Learning Techniques for Accurate Tomato Leaf Disease Classification

The study proposes a transdisciplinary approach integrating knowledge from fields such ascomputer science, botany, and data science to classifying leaf diseases. We integrated two deep-learningmodels that combine the strengths of the Inception network and the ResNet architecture to address thechallenge of accurately classifying tomato leaf diseases. The Inception network’s ability to quickly pick upvisual features on multiple scales is used to pull out fine-grained details that are needed to tell the differencebetween small changes in the shape of tomato leaves and disease symptoms. The ResNet architecture isgood at learning deep representations and getting around the vanishing gradient problem. This lets themodel learn the high-level concepts and complicated connections between different tomato leaf diseasepatterns. The integration of these two powerful deep-learning techniques results in a robust and highlyperformant tomato leaf classification model. Extensive tests on a 10-class dataset of tomato leaves, with 9disease categories and 1 healthy class, show that the proposed model works better than others, with a testset accuracy of 98.07%. The findings of this research contribute to the advancement of automated andefficient tomato leaf disease detection systems, which can aid in the early identification and management oftomato diseases, leading to improved crop yields and quality.

Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine

The improvements in collecting and processing data using machine learning algorithms have increased the interest in data mining. This trend has led to the development of real-life decision support systems (DSSs) in diverse areas such as biomedical informatics, fraud detection, natural language processing, face recognition, autonomous vehicles, image processing, and each part of the real production environment. The imbalanced datasets in some of these studies, which result in low performance measures, have highlighted the need for additional efforts to address this issue. The proposed method (HOUM) is used to address the issue of imbalanced datasets for classification problems in this study. The aim of the model is to prevent the overfitting problem caused by oversampling and valuable data loss caused by undersampling in imbalanced data and obtain successful classification results. The HOUM is a hybrid approach that tackles imbalanced class distribution challenges, refines datasets, and improves model robustness. In the first step, majority-class data points that are distant from the decision boundary obtained via SVM are reduced. If the data are not balanced, SLS is employed to augment the minority-class data. This loop continues until the dataset becomes balanced. The main contribution of the proposed method is reproducing informative minority data using SLS and diminishing non-informative majority data using the SVM before applying classification techniques. Firstly, the efficiency of the proposed method, the HOUM, is verified by comparison with the SMOTE, SMOTEENN, and SMOTETomek techniques using eight datasets. Then, the results of the W-SIMO and RusAda algorithms, which were developed for imbalanced datasets, are compared with those of the HOUM. The strength of the HOUM is revealed through this comparison. The proposed HOUM algorithm utilizes a real dataset obtained from a project endorsed by The Scientific and Technical Research Council of Turkey. The collected data include quality control and processing parameters of yarn data. The aim of this project is to prevent yarn breakage errors during the weaving process on looms. This study introduces a decision support system (DSS) designed to prevent yarn breakage during fabric weaving. The high performance of the algorithm may encourage producers to manage yarn flow and enhance the HOUM’s efficiency as a DSS.

Adadelta-CSA: Adadelta-Chameleon Swarm Algorithm for EEG-Based Epileptic Seizure Detection

Epilepsy is referred to as a neurological disorder, which is detected via examination and manual comprehension of Electroencephalogram (EEG) signals. In deep learning schemes, various enhancements have emerged to efficiently address complex issues by end-to-end learning. The major objective of this research is to propose a new seizure detection approach from EEG signals using a deep learning-based classification technique. The pre-processing is the initial stage, where denoising is performed using a Short-Time Fourier Transform (STFT). Subsequently, the statistical features, time-domain features and spectral features are extracted from the pre-processed signal. Finally, an efficient optimization approach, named Adadelta-Chameleon Swarm Algorithm (Adadelta-CSA), is proposed and employed to train Deep Neural Network (DNN) to carry out the precise seizure prediction. Here, the integration of the Adadelta concept in the Chameleon Swarm Algorithm (CSA) has resulted in Adadelta-CSA. At last, the performance of the Adadelta-CSA scheme-based DNN is compared with the existing techniques by considering accuracy, sensitivity and specificity, and it is found to produce better values of 0.951, 0.966, and 0.935, respectively.

A systematic review on feature extraction methods and deep learning models for detection of cancerous lung nodules at an early stage –The Recent trends and challenges

Abstract Lung cancer is one of the most common life-threatening worldwide cancers affecting both the male and female populations. The appearance of nodules in the scan image is an early indication of the development of cancer cells in the lung. The Low Dose Computed Tomography screening technique is used for the early detection of cancer nodules. Therefore, with more Computed Tomography (CT) lung profiles, an automated lung nodule analysis system can be utilized through image processing techniques and neural network algorithms. A CT image of the lung consists of many elements such as blood vessels, ribs, nodules, sternum, bronchi, and nodules. These nodules can be both benign and malignant, where the latter leads to lung cancer. Detecting them at an earlier stage can increase life expectancy by up to 5 to 10 years. To analyze only the nodules from the profile, respected features are calculated using image processing techniques. Based on the review, textural features were promising ones in medical image analysis and for solving computer vision problems. The significance of extracting the hidden features (textural) enables the higher performance of deep learning algorithms (DL) predominantly in medical imaging, which has resulted in improvement in accuracy in recent years. Earlier detection of cancerous lung nodules is possible through the combination of multi-featured extraction and classification techniques using image data. In this paper, we discussed the overview of lung cancer along with publicly available datasets for research purposes. The primary objective of the paper is to provide the importance of textural features when combined with different deep-learning models. It gives insights into their advantages, disadvantages, and limitations regarding possible research gaps. The paper compared the recent studies of deep learning models with and without feature extraction and concluded that DL models that include feature extraction were better than the others.

Investigation of Freeway Incident Duration Using Classification and Regression Trees Based on Multisource Data

Targeted contingency measures have proven highly effective at reducing the duration and harm caused by incidents. This study utilized the Classification and Regression Trees (CART) data mining technique to predict and quantify the duration of incidents. To achieve this, multisensor data collected from the Hangzhou freeway in China spanning from 2019 to 2021 was utilized to construct a regression tree with eight levels and 14 leaf nodes. By extracting 14 rules from the tree and establishing contingency measures based on these rules, accurate incident assessment and effective implementation of post-incident emergency plans were achieved. In addition, to more accurately apply the research findings to actual incidents, the CART method was compared with XGBoost, Random Forest (RF), and AFT (accelerated failure time) models. The results indicated that the prediction accuracy of the CART model is better than the other three models. Furthermore, the CART method has strong interpretability. Interactions between explanatory variables, up to seven, are captured in the CART method, rather than merely analyzing the effect of individual variables on the incident duration, aligning more closely with actual incidents. This study has important practical implications for advancing the engineering application of machine learning methods and the analysis of sensor data.

Machine learning techniques for the smart faults detection and diagnosis of centrifugal compressor

In this work, we have conducted a comparative study among several machine learning techniques with the aim of selecting the best one for classifying faults affecting the compressor system to enable smart monitoring. This study encompasses various machine learning techniques, including Support Vector Machine, k-nearest neighbor, Decision Tree, Naive Bayes, AdaBoost, and Bag ensembles. To determine the optimal classification technique, we applied three distinct criteria: the confusion matrix, error histogram, and mean square error through cross-validation. Based on these criteria, the results indicate a tie for the top position between two classification models: Decision Tree and Bag ensemble. To solidify our choice of a single model, we employed the new AutoML technique to automatically identify the most suitable machine learning classification model for our case study. We evaluated this approach using process data obtained from an operational industrial centrifugal compressor. Consequently, the results presented in this work affirm that Decision Tree is the superior technique for classifying faults in the 3MCL compressor.

Hierarchical multi-label classification model for science and technology news based on heterogeneous graph semantic enhancement

In the context of high-quality economic development, technological innovation has emerged as a fundamental driver of socio-economic progress. The consequent proliferation of science and technology news, which acts as a vital medium for disseminating technological advancements and policy changes, has attracted considerable attention from technology management agencies and innovation organizations. Nevertheless, online science and technology news has historically exhibited characteristics such as limited scale, disorderliness, and multi-dimensionality, which is extremely inconvenient for users of deep application. While single-label classification techniques can effectively categorize textual information, they face challenges in leading science and technology news classification due to a lack of a hierarchical knowledge framework and insufficient capacity to reveal knowledge integration features. This study proposes a hierarchical multi-label classification model for science and technology news, enhanced by heterogeneous graph semantics. The model captures multi-dimensional themes and hierarchical structural features within science and technology news through a hierarchical transmission module. It integrates graph convolutional networks to extract node information and hierarchical relationships from heterogeneous graphs, while also incorporating prior knowledge from domain knowledge graphs to address data scarcity. This approach enhances the understanding and classification capabilities of the semantics of science and technology news. Experimental results demonstrate that the model achieves precision, recall, and F1 scores of 84.21%, 88.89%, and 86.49%, respectively, significantly surpassing baseline models. This research presents an innovative solution for hierarchical multi-label classification tasks, demonstrating significant application potential in addressing data scarcity and complex thematic classification challenges.

QLTI-07. COLLABORATIVE RESEARCH SUITE FOR GENOMIC ANALYSIS, DATA REPORTING AND WORKFLOW CUSTOMISATION

Abstract The genomic analysis pipeline involves multiple stages, including data extraction, transformation, and loading (ETL), exploratory data analysis (EDA), machine learning (ML) modeling, and reporting. The ETL stage involves data profiling and quality control measures to ensure data integrity and consistency. The EDA stage employs statistical and visualization methods to understand data distribution, identify patterns, and detect anomalies. Machine learning modelling involves supervised classification techniques, such as logistic regression and support vector machines (SVMs), to predict tumour classification and develop gene signatures. Unsupervised classification techniques, such as K-means clustering and Hierarchical clustering, identify patterns and relationships in genomic data. Quality metrics evaluate data completeness, accuracy, and consistency, ensuring data reliability and validity. The integration of data science techniques into a comprehensive framework can be achieved with the development of Application Dashboards that provide real-time insights into genomic data across every stage of the analysis pipeline. This enables clinical researchers and bioinformaticians to make informed decisions quickly, improving turn-around time and accelerating the discovery of new insights. In contrast to conventional methods of using stand-alone scripts, these dashboards offer a reusable and scalable solution, allowing for easy modification and adaptation to new datasets and analysis pipelines, thereby reducing development effort and increasing productivity. The dashboards constitute a centralized platform for data visualization, exploration, and analysis, facilitating collaboration and communication among research team members. With customizable and interactive visualizations, users can explore genomic data from multiple dimensions and the integration of multiple sources, including genomic and transcriptomic data. The technologies used to power this Research suite include Streamlit for Web App development, Python for data analysis, Opensearch for data storage and other utility libraries for reporting.

Machine learning techniques for coffee classification: a comprehensive review of scientific research

Machine learning techniques for coffee classification: a comprehensive review of scientific research

Comparative analysis of vision transformers and fine-tuned transfer learning models for brain tumor classification

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

PYRAMID CHANNEL-BASED FEATURE ATTENTION NETWORK WITH ENSEMBLE LEARNING-BASED UAV IMAGE CLASSIFICATION ON IoT-ASSISTED REMOTE SENSING ENVIRONMENT

Advances in Unmanned Aerial Vehicles (UAVs), otherwise recognized as drones, have tremendous promise in improving the wide-ranging applications of the Internet of Things (IoT). UAV image classification using deep learning (DL) is an amalgamation to modernize data analysis, collection, and decision-making in a variety of sectors. IoT devices collect information in real time, while remote sensing captures data afar without direct contact. UAVs equipped with sensors offer high-quality images for classification tasks. DL techniques, especially the convolutional neural networks (CNNs), analyze data streams, extracting complicated features for the accurate classification of objects or environmental features. This synergy enables applications including urban planning and precision agriculture, fostering smarter disaster response, decision support systems, and efficient resource management. This paper introduces a novel Pyramid Channel-based Feature Attention Network with an Ensemble Learning-based UAV Image Classification (PCFAN-ELUAVIC) technique in an assisted remote sensing environment. The PCFAN-ELUAVIC technique begins with the contrast enhancement of the UAV images using the CLAHE technique. Following that, the feature vectors are derived by the use of the PCFAN model. Meanwhile, the hyperparameter tuning procedure is executed by the inclusion of a vortex search algorithm (VSA). For image classification, the PCFAN-ELUAVIC technique comprises an ensemble of three classifiers like long short-term memory (LSTM), graph convolutional networks (GCNs), and Hermite neural network (HNN). To exhibit the improved detection results of the PCFAN-ELUAVIC system, an extensive range of experiments are carried out. The experimental values confirmed the enhanced performance of the PCFAN-ELUAVIC model when compared to other techniques.

Multi-temporal image analysis of wetland dynamics using machine learning algorithms

Wetlands play a crucial role in enhancing groundwater quality, mitigating natural hazards, controlling erosion, and providing essential habitats for unique flora and wildlife. Despite their significance, wetlands are facing decline in various global locations, underscoring the need for effective mapping, monitoring, and predictive modeling approaches. Recent advances in machine learning, time series earth observation data, and cloud computing have opened up new possibilities to address the challenges of large-scale wetlands mapping and dynamics forecasting. This research conducts a comprehensive analysis of wetland dynamics in the Thatta region, encompassing Haleji & Kinjhar Lake in Pakistan, and evaluates the efficacy of different classification systems. Leveraging Google Earth Engine, Landsat imagery, and various spectral indices, we assess four classification techniques to derive accurate wetland mapping results. Our findings demonstrate that Random Forest emerged as the most efficient and accurate method, achieving 87% accuracy across all time periods. Change detection analysis reveals a significant and alarming decline in Haleji & Kinjhar Lake wetlands over 1990–2020, primarily driven by agricultural expansion, urbanization, groundwater extraction, and climate change impacts like rising temperatures and reduced precipitation. If left unaddressed, this continued wetland loss could have severe implications for aquatic and terrestrial species, water and soil quality, wildlife populations, and local livelihoods. The study predicts future wetland dynamics under different scenarios - enhancing drainage for farmland conversion (10–20% increase), increasing urbanization (10–20% expansion), escalating groundwater extraction (7.2m annual decline), and climate change (up to 5 °C warming and 54% precipitation deficit by 2050). These scenarios forecast sustained long-term wetland deterioration driven by anthropogenic pressures and climate change. To guide conservation strategies, the research integrates satellite data analytics, machine learning algorithms, and spatial modeling to generate actionable insights into multifaceted wetland vulnerabilities. Findings provide a robust baseline to inform policies ensuring sustainable management and preservation of these vital ecosystems amidst escalating human and climate threats. Over 1990–2020, the Thatta region witnessed a 352.8 sq.km loss of wetlands, necessitating urgent restoration efforts to safeguard their invaluable ecosystem services.

Exploring the Biology of Quasi-Social Idiobiont Parasitoids in the Genus Sclerodermus (Hymenoptera: Bethylidae)

Species in the genus Sclerodermus are among the most socially complex parasitoids, unlike most parasitoids, which are solitary and do not provide care after laying eggs. In Sclerodermus, groups of females paralyse their host, lay eggs on it, and work together to care for the brood (a quasi-social form of reproduction). This research, through database analysis and meta-analysis, covers the biology of the genus, which has 80 species, though only 24 have been studied in detail. It describes their morphology and behaviour, focusing on offspring production, developmental time, and the factors influencing these, such as kinship and the number of females tending the brood. The materials and methods used provide a comprehensive approach to data collection and analysis, drawing on diverse sources, rigorous classification, and advanced statistical techniques. This approach revealed that Sclerodermus species display a high degree of consistency in their responses to temperature, host size, and foundress number.

Mental Health Identification System

In an era where technology's reach extends to the intricacies of human well- being, "Mental Health Identification" emerges as a thought-provoking narrative that explores the convergence of innovation, ethics, and mental health. Relationships strain under the weight of newfound possibilities, underscoring. Developing an AI-Enabled Mental Health Identification for Early Intervention This project introduces an innovative AI-enabled mental health identification designed to revolutionize the early identification of mental health conditions. The project’s goal is to create a valuable tool that contributes to improved mental health awareness and support. The abstract provides a concise overview of the project. It highlights the development of an artificial intelligence-based mental health identification that utilizes cutting- edge machine learning algorithms to analyze face patterns, facial expressions, and textual inputs. The project’s focus is on creating an automated and non-intrusive tool for early detection and assessment of mental health conditions, aiming to contribute to improved mental health awareness and support. Mental health disorders have become one of the leading causes of disability worldwide, affecting millions of individuals. The early identification of mental health conditions is crucial for effective treatment and prevention. This paper presents a Mental Health Identification System that utilizes machine learning algorithms to predict and identify common mental health disorders based on a questionnaire dataset. The model is designed to focus on anxiety, depression, and stress disorders, using classification techniques to analyze user inputs

Iris Recognition System Using Advanced Segmentation Techniques and Fuzzy Clustering Methods for Robotic Control

The idea of developing a robot controlled by iris movement to assist physically disabled individuals is, indeed, innovative and has the potential to significantly improve their quality of life. This technology can empower individuals with limited mobility and enhance their ability to interact with their environment. Disability of movement has a huge impact on the lives of physically disabled people. Therefore, there is need to develop a robot that can be controlled using iris movement. The main idea of this work revolves around iris recognition from an eye image, specifically identifying the centroid of the iris. The centroid’s position is then utilized to issue commands to control the robot. This innovative approach leverages iris movement as a means of communication and control, offering a potential breakthrough in assisting individuals with physical disabilities. The proposed method aims to improve the precision and effectiveness of iris recognition by incorporating advanced segmentation techniques and fuzzy clustering methods. Fast gradient filters using a fuzzy inference system (FIS) are employed to separate the iris from its surroundings. Then, the bald eagle search (BES) algorithm is employed to locate and isolate the iris region. Subsequently, the fuzzy KNN algorithm is applied for the matching process. This combined methodology aims to improve the overall performance of iris recognition systems by leveraging advanced segmentation, search, and classification techniques. The results of the proposed model are validated using the true success rate (TSR) and compared to those of other existing models. These results highlight the effectiveness of the proposed method for the 400 tested images representing 40 people.

Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques

This paper presents a comprehensive fault diagnosis approach for a delta robot utilizing advanced feature extraction and classification techniques. A four-arm delta robot prototype is designed in SolidWorks for realistic fault analysis. Two case studies investigate faults through control effort and vibration signals, with control effort detecting motor and encoder faults, while vibration signals identify bearing faults. This study compares time-domain signal features and wavelet scattering networks, applied by classification algorithms including wide neural networks (WNNs), efficient linear support vector machine (ELSVM), efficient logistic regression (ELR), and kernel naive Bayes (KNB). Results indicate that a WNN, using wavelet scattering features ranked by one-way anova, is optimal due to its consistency and reliability, while these features enhance computational efficiency by reducing classifier size. Sensitivity analysis demonstrates the classifier’s capacity to detect untrained faults, highlighting the importance of effective feature extraction and classification methods for fault diagnosis in complex robotic systems. This research significantly contributes to fault diagnosis in delta robots and lays the groundwork for future studies on fault tolerance control and predictive maintenance planning. Future work will focus on the physical implementation of the delta robot in laboratory settings, aiming to improve operational efficiency and reliability in industrial applications.

Rapid Raman spectroscopy analysis assisted with machine learning: a case study on Radix Bupleuri

AbstractBACKGROUNDRadix Bupleuri has been widely used for its plentiful pharmacological effects. But it is hard to evaluate their safety and efficacy because the concentrations of components are tightly affected by the surrounding environment. Thus, Radix Bupleuri samples from different regions and varieties were collected. Based on the experimental and computational Raman spectrum, machine learning is emphasized for certain obscured characteristics; for example, linear discriminant analysis (LDA), support vector machine (SVM), eXtreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM).RESULTSAfter dimension reduction by LDA, models of SVM, XGBoost and LightGBM were trained for classification and regression prediction of Bupleurum production regions. Support vector classifiers achieved the best accuracy of 98% and an F1 score above 0.96 on the test set. Support vector regression has a good fitting performance with an R2 score above 0.90 and a relatively low mean square error. However, complex models were prone to overfitting, resulting in poor generalization ability.CONCLUSIONAmong these machine learning models, the typical LDA‐SVM models, consistent with the high‐performance liquid chromatography results, demonstrate great performance and stability. We envision that this rapid classification and regression technique can be extended to predictions for other herbs. © 2024 Society of Chemical Industry.

Research on gesture recognition technology based on machine learning

Abstract. Hand Gesture Recognition (HGR), as a significant technological advancement in the field of Human-Computer Interaction (HCI), aims to develop systems capable of accurately recognizing and interpreting human gestures for a diverse range of applications, including device control, virtual reality, gesture passwords, and gesture interaction. With the continuous advancement of machine learning algorithms, especially deep learning techniques, machine learning-based gesture recognition has garnered widespread attention. This paper presents a review of the development of gesture recognition techniques from traditional approaches to the current mainstream deep learning-based methods, and outlines the challenges and technical difficulties encountered. It analyzes several of the most popular classification techniques, including Naive Bayes, K-Nearest Neighbors (KNN), Random Forests, XGBoost, Support Vector Classifiers (SVCs), and Convolutional Neural Networks (CNNs). Furthermore, this paper examines the application of these algorithms in both dynamic and static gesture recognition and compares their performance and suitability in different scenarios. The results demonstrate that the accuracy and robustness of gesture recognition systems can be markedly enhanced through the prudent selection and optimization of the algorithms, which serves as a valuable reference for future research and applications.

Predicting the impact of adding metakaolin on the flexural strength of concrete using ML classification techniques – a comparative study

Predicting the impact of adding metakaolin on the flexural strength of concrete using ML classification techniques – a comparative study