Enhanced Support Vector Machine Research Articles

Dual Multi Scale Attention Network Optimized With Archerfish Hunting Optimization Algorithm for Diabetics Prediction

ABSTRACTDiabetes is a chronic disease that occurs when the body cannot regulate blood sugar levels. Nowadays, the screening tests for diabetes are developed using multivariate regression methods. An increasing amount of data is automatically collected to provide an opportunity for creating challenging and accurate prediction modes that are updated constantly with the help of machine learning techniques. In this manuscript, a Dual Multi Scale Attention Network optimized with Archerfish Hunting Optimization Algorithm is proposed for Diabetes Prediction (DMSAN‐AHO‐DP). Here, the data is gathered through PIMA Indian Diabetes Dataset (PIDD). The collected data is fed towards the preprocessing to remove the noise of input data and improves the data quality by using Contrast Limited Adaptive Histogram Equalization Filtering (CLAHEF) method. Then the preprocessed data are fed to Multi‐Level Haar Wavelet Features Fusion Network (MHWFFN) based feature extraction. Then the extracted data is supplied to the Dual Multi Scale Attention Network (DMSAN) for diabetic or non‐diabetic classification. The hyper parameter of Dual Multi Scale Attention Network is tuned with Archerfish Hunting Optimization (AHO) algorithm, which classifies diabetic or non‐diabetic accurately. The proposed DMSAN‐AHO‐DP technique is implemented in Python. The efficacy of the DMSAN‐AHO‐DP approach is examined with some metrics, like Accuracy, F‐scores, Sensitivity, Specificity, Precision, Recall, Computational time. The DMSAN‐AHO‐DP technique achieves 23.52%, 36.12%, 31.12% higher accuracy and 16.05%, 21.14%, 31.02% lesser error rate compared with existing models: Enhanced Deep Neural Network based Model for Diabetes Prediction (EDNN‐DP), Indian PIMA Dataset using Deep Learning for Diabetes Prediction (ANN‐DP), and Enhanced Support Vector Machine with Deep Neural Network Learning strategies for Diabetes Prediction (SVM‐DNN‐DP).

Advanced image processing techniques for multi-level characterization of significant flame features in carbon-neutral combustion

This research presents an advanced image processing framework designed for the multi-level characterization of significant flame features in carbon-neutral combustion systems. The study introduces an optimized neural network based on a brainstorming algorithm and an enhanced support vector machine (SVM) algorithm, which utilizes an improved particle swarm optimization (PSO) technique. The framework is capable of accurately processing complex flame image data, achieving flame classification accuracy rates of up to 100 %. The proposed system combines detailed analysis of flame brightness, texture, and morphological features, contributing to the comprehensive monitoring of combustion states. Additionally, the paper describes the design of a clean burner that promotes the full combustion of zero-carbon gases. The integration of this burner with the advanced monitoring system significantly enhances safety and efficiency in industrial applications. This work addresses the challenges associated with the transition to renewable gases and provides a robust solution for maintaining stable and efficient combustion processes.

Supply Chain Management Optimization and Cost Reduction Based on Data Mining Algorithm

Supply Chain Management (SCM) is an evolving industry that constantly evolves, but one constant is the significance of effective inventory control in optimizing profits and customer satisfaction. The primary objective of the research is to identify the most effective method for a business to supply several products while maintaining customer service and keeping inventory costs low. The main aim of this paper's Stochastic Gradient Descent (SGD)-enhanced Support Vector Machine (SVM) approach is to predict ideal order and lack quantities, considering into account historical demand, level of inventory, vendor performance, and cost data. The key goal is to reduce the total cost of inventory (which includes holding, placing orders, and supply-chain costs) while maintaining excellent customer service. In order to accomplish this, the proposed system combines SGD's efficiency to enhance predictions and SVM's predictability. A consumer products business chain's real-world dataset was employed to train and test the algorithm. The dataset provides three years' worth of revenue, stock levels, vendors, and costs. Parameters like Mean Absolute Error (MAE), Inventory Turnover Ratio (ITR), and Service Level Achievement (SLA) have been employed to test the methodology, and parameters were optimized using a 5-fold cross-validation. Based on the study findings, the simulation model obtains an average MAE of 1.81 following 100 training epochs, substantially boosting ITR and SLA. As it effectively and accurately balances inventory levels with customer needs, the findings demonstrate that the SVM model with SGD is an excellent match for IMO.

Enhanced SVM Algorithm-Based Dynamic Early Warning System for College English Ideological and Political Course Education Using Machine Learning

This study explores the use of an enhanced Support Vector Machine (SVM) algorithm to address challenges faced by students with lower academic performance in college English education, particularly in integrating ideological and political education. It develops a dynamic early warning system to provide timely support, employing targeted English courses and practical teaching methods. Methodologically, an improved SVM algorithm constructs a robust early warning model, enhancing monitoring and support in college English courses. The system's application contributes to advancements in machine learning and AI, emphasizing data-driven decision-making in education. Future research could explore scalability, long-term impacts, and further refinements to the SVM algorithm. In summary, the study successfully applies machine learning techniques to devise an innovative dynamic early warning system for English ideological and political education in college, offering valuable insights for practitioners and researchers alike in the realm of AI-assisted pedagogy.

Enhanced Support Vector Machine Based on Grey Wolf Optimizer for Fruits Image Classification using MPEG-7 Color and Texture Features Fusion

Enhanced Support Vector Machine Based on Grey Wolf Optimizer for Fruits Image Classification using MPEG-7 Color and Texture Features Fusion

Evaluating the Efficacy of Real-Time Connected Vehicle Basic Safety Messages in Mitigating Aberrant Driving Behaviour and Risk of Vehicle Crashes: Preliminary Insights from Highway Scenarios

Connected vehicle (CV) technology has revolutionised the intelligent transportation management system by providing new perspectives and opportunities. To further improve risk perception and early warning capabilities in intricate traffic scenarios, a comprehensive field test was conducted within a CV framework. Initially, data for basic safety messages (BSM) were systematically gathered within a real-world vehicle test platform. Subsequently, an innovative approach was introduced that combined multimodal interactive filtering with an advanced vehicle dynamics model to integrate BSM vehicle motion data with observations from roadside units. In addition, a driving condition perception methodology was developed, leveraging rough sets and an enhanced support vector machine (SVM), to identify aberrant driver behaviours and potential driving risks effectively. Furthermore, this study integrated BSM data from various scenarios, including car-following, lane changes, and free driving within the CV environment, to formulate multidimensional driving state sequence patterns for short-term predictions (0.5 s) utilising the long short-term memory (LSTM) model framework. The results demonstrated the effectiveness of the proposed approach in accurately identifying potentially hazardous driving conditions and promptly predicting collision risks. The findings from this research hold substantial promise in advancing road traffic safety management.

Enhanced Support Vector Machine for Cooperative Spectrum Sensing Against Byzantine Attack in Cognitive Wireless Sensor Networks

Enhanced Support Vector Machine for Cooperative Spectrum Sensing Against Byzantine Attack in Cognitive Wireless Sensor Networks

An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology.

In the rapidly evolving landscape of agricultural technology, image processing has emerged as a powerful tool for addressing critical agricultural challenges, with a particular focus on the identification and management of crop diseases. This study is motivated by the imperative need to enhance agricultural sustainability and productivity through precise plant health monitoring. Our primary objective is to propose an innovative approach combining support vector machine (SVM) with advanced image processing techniques to achieve precise detection and classification of fig leaf diseases. Our methodology encompasses a step-by-step process, beginning with the acquisition of digital color images of diseased leaves, followed by denoising using the mean function and enhancement through Contrast-limited adaptive histogram equalization. The subsequent stages involve segmentation through the Fuzzy C Means algorithm, feature extraction via Principal Component Analysis, and disease classification, employing Particle Swarm Optimization (PSO) in conjunction with SVM, Backpropagation Neural Network, and Random Forest algorithms. The results of our study showcase the exceptional performance of the PSO SVM algorithm in accurately classifying and detecting fig leaf disease, demonstrating its potential for practical implementation in agriculture. This innovative approach not only underscores the significance of advanced image processing techniques but also highlights their substantial contributions to sustainable agriculture and plant disease mitigation. In conclusion, the integration of image processing and SVM-based classification offers a promising avenue for advancing crop disease management, ultimately bolstering agricultural productivity and global food security.

An Enhanced Support Vector Machine based Multiclass Classification Method For Crop Prediction

Crop type classification is a fundamental task in precision agriculture, enabling informed decision-making for crop management and resource allocation. Support Vector Machines (SVMs) have emerged as robust and effective tools for multi-class classification tasks. This study explores the application of SVM-based multi-class classification techniques to accurately categorize various crop types based on remote sensing data. he SVM algorithm is employed to create decision boundaries that maximize the margin between different crop classes while minimizing classification errors. To enhance classification performance, various kernel functions such as linear, polynomial, and radial basis function are evaluated to capture complex relationships within the data. The proposed SVM-based approach is compared with other commonly used classification methods to assess its superiority in terms of accuracy, precision, recall, and F1-score.

FAULT DETECTION AND LOCATION BASED SVM FOR THREE PHASE TRANSMISSION LINES APPLYING POSITIVE SEQUENCE FAULT COMPONENTS

Transmission lines are an imperative element of the modern power systems. Any faults in them can cause an undesirable interruption in power supply. Precise analysis of these faults is important in-order to ensure an incessant supply of power. For this purpose, fault detection and classification are needed to clear any such faults and re-establish the system to maintain its normal operation. In this paper, a novel integrated approach of protective relaying with enhanced support vector machine algorithm has been adopted for detecting faults and its location estimation in long transmission line. The proposed scheme is successfully able to detect and classify different symmetrical and unsymmetrical faults along with some peculiar cases related to High Impedance Faults (HIF) and evolving faults, current transformer (CT) saturation/ capacitive voltage transformer (CVT) transient, close-in faults, swing condition, source strength variation, etc. The comparative analysis with recent proposed techniques declared the potentiality and robustness of the scheme

Enhanced Support Vector Machine with Particle Swarm Optimization and Genetic Algorithm for Estimating Discharge Coefficients of Circular-Crested Oblique Weirs

Enhanced Support Vector Machine with Particle Swarm Optimization and Genetic Algorithm for Estimating Discharge Coefficients of Circular-Crested Oblique Weirs

Enhanced Machine-Learning Techniques for Medium-Term and Short-Term Electric-Load Forecasting in Smart Grids

Nowadays, electric load forecasting through a data analytic approach has become one of the most active and emerging research areas. It provides future consumption patterns of electric load. Since there are large fluctuations in both electricity production and use, it is a difficult task to achieve a balance between electric load and demand. By analyzing past electric consumption records to estimate the upcoming electricity load, the issue of fluctuating behavior can be resolved. In this study, a framework for feature selection, extraction, and regression is put forward to carry out the electric load prediction. The feature selection phase uses a combination of extreme gradient boosting (XGB) and random forest (RF) to determine the significance of each feature. Redundant features in the feature extraction approach are removed by applying recursive feature elimination (RFE). We propose an enhanced support vector machine (ESVM) and an enhanced convolutional neural network (ECNN) for the regression component. Hyperparameters of both the proposed approaches are set using the random search (RS) technique. To illustrate the effectiveness of our proposed strategies, a comparison is also performed between the state-of-the-art approaches and our proposed techniques. In addition, we perform statistical analyses to prove the significance of our proposed approaches. Simulation findings illustrate that our proposed approaches ECNN and ESVM achieve higher accuracies of 98.83% and 98.7%, respectively.

A novel solution of deep learning for enhanced support vector machine for predicting the onset of type 2 diabetes

Type 2 Diabetes is one of the most major and fatal diseases known to human beings, where thousands of people are subjected to the onset of Type 2 Diabetes every year. However, the diagnosis and prevention of Type 2 Diabetes are relatively costly in today's scenario; hence, the use of machine learning and deep learning techniques is gaining momentum for predicting the onset of Type 2 Diabetes. This research aims to increase the accuracy and Area Under the Curve (AUC) metric while improving the processing time for predicting the onset of Type 2 Diabetes. The proposed system consists of a deep learning technique that uses the Support Vector Machine (SVM) algorithm along with the Radial Base Function (RBF) along with the Long Short-term Memory Layer (LSTM) for prediction of onset of Type 2 Diabetes. The proposed solution provides an average accuracy of 86.31 % and an average AUC value of 0.8270 or 82.70 %, with an improvement of 3.8 milliseconds in the processing. Radial Base Function (RBF) kernel and the LSTM layer enhance the prediction accuracy and AUC metric from the current industry standard, making it more feasible for practical use without compromising the processing time.

Analysis and Research on the Marketing Strategy of Agricultural Products Based on Artificial Intelligence

With the gradual development of artificial intelligence (AI), the traditional production, marketing, and management methods for agricultural products have undergone dramatic changes, necessitating a greater optimization of these methods. Agricultural product operators have begun incorporating AI technology into product production, marketing, and distribution processes. This article examines the current state of agricultural product management and then investigates the integration of production, marketing, and distribution using artificial intelligence. In addition, given the limitations of conventional methods for classifying agricultural products, this article presents a classification model that combines factor analysis with an enhanced support vector machine (SVM) based on genetic algorithms (GAs). The results of the experiments indicate that the improved method is capable of distinguishing agricultural product quality categories rapidly and precisely, significantly improving the classification accuracy of agricultural product quality, and being broadly applicable to the evaluation of agricultural product quality.

An efficient cyber‐physical system using hybridized enhanced support‐vector machine with Ada‐Boost classification algorithm

SummaryThe necessity of cyber‐security has obtained immense importance in day‐to‐day concerns of network communication. Therefore, several available research works predominantly focus on network security to protect the resources, services, and networks from any unauthorized access. A CPS (cyber‐physical system) model using a dual mutation‐based genetic algorithm, with feature classification through Ada‐Boost and SVM classifier is proposed in this paper. Dual‐mutation based genetic‐algorithm overcomes the issues of conventional techniques including convergence issues and local fine‐tuning of features. In this paper, necessary modifications were made to the existing Genetic Algorithm (GA) method to reduce the random nature of the traditional GA method. Particularly, the goal of this work is to develop the modified reproduction operators with appropriate fitness functions to guide simulations to gain optimal solutions. In floating‐point representation, every chromosome vector has been coded as a floating‐point number vector having the same length as the solution vector. Each element was selected initially, to stand within the desired domain, and operators were designed carefully in satisfying the constraints. As a result, there are various enhancements employed in the dual‐mutation algorithm that handles local fine‐tuned features. The relevant features of dataset samples are extracted and rescaled using feature selection and resampling phase aided by the Markov‐resampling process. Followed by this, a hybrid approach of ESVM (enhanced support‐vector machine) algorithm with Ada‐Boost classifier is implemented for the fault classification process. The performance assessment was explicated in terms of accuracy‐factor, F1‐score, and execution time. Comparative analysis expounded the efficacy of the proposed model than other conventional methods attaining higher accuracy (97%), F1‐score (99%) rates, and less execution time (15.33 s).

Hybrid metaheuristic algorithm enhanced support vector machine for epileptic seizure detection

Analysis and detection of epileptic seizures are usually done by physicians through the visual scanning of EEG signals, which tends to be subjective, time-consuming, inaccurate, and prone to errors. Various research directions have focused on the detection and classification of epilepsy using machine learning methods. However, the prevailing methods have shortcomings like low classification rates and slow convergence. To address those limitations, this paper designs a fully automated system based on Hybrid Grey Wolf Optimizer-Improved Sine Cosine Algorithm (HGWOISCA) enhanced Support Vector Machine (SVM) named HGWOISCA-SVM for EEG signal classification. Primarily, unprocessed EEG signals are denoised by using an improved wavelet thresholding function to diminish noise and redundant data. Secondarily, three types of features such as wavelet domain features, time-domain features, and chaotic features are computed from the preprocessed EEG signals. In the following phase, Enhanced Grasshopper Optimization Algorithm (EGOA) is adopted to select optimal features with high discriminative power and to reduce dimensionality. Finally, the selected features are input into the HGWOISCA-SVM to differentiate healthy EEG signals from epileptic seizure signals. The effectiveness of the developed system is investigated by using Bonn university data and clinical data. Experiential results prove that the developed system yields a high classification rate of 100% for Bonn university data and 99.9% for clinical data, proving to be a powerful system for epilepsy classification. A comparison of the proposed system with the existing epileptic seizure detection methods is also done for validation.

Tool for Predicting College Student Career Decisions: An Enhanced Support Vector Machine Framework

The goal of this research is to offer an effective intelligent model for forecasting college students’ career decisions in order to give a useful reference for career decisions and policy formation by relevant departments. The suggested prediction model is mainly based on a support vector machine (SVM) that has been modified using an enhanced butterfly optimization approach with a communication mechanism and Gaussian bare-bones mechanism (CBBOA). To get a better set of parameters and feature subsets, first, we added a communication mechanism to BOA to improve its global search capability and balance exploration and exploitation trends. Then, Gaussian bare-bones was added to increase the population diversity of BOA and its ability to jump out of the local optimum. The optimal SVM model (CBBOA-SVM) was then developed to predict the career decisions of college students based on the obtained parameters and feature subsets that are already optimized by CBBOA. In order to verify the effectiveness of CBBOA, we compared it with some advanced algorithms on all benchmark functions of CEC2014. Simulation results demonstrated that the performance of CBBOA is indeed more comprehensive. Meanwhile, comparisons between CBBOA-SVM and other machine learning approaches for career decision prediction were carried out, and the findings demonstrate that the provided CBBOA-SVM has better classification and more stable performance. As a result, it is plausible to conclude that the CBBOA-SVM is capable of being an effective tool for predicting college student career decisions.

Intelligent fault diagnosis for power distribution system-comparative studies

Short circuit is one of the most popular types of permanent fault in power distribution system. Thus, fast and accuracy diagnosis of short circuit failure is very important so that the power system works more effectively. In this paper, a newly enhanced support vector machine (SVM) classifier has been investigated to identify ten short-circuit fault types, including single line-to-ground faults (XG, YG, ZG), line-to-line faults (XY, XZ, YZ), double line-to-ground faults (XYG, XZG, YZG) and three-line faults (XYZ). The performance of this enhanced SVM model has been improved by using three different versions of particle swarm optimization (PSO), namely: classical PSO (C-PSO), time varying acceleration coefficients PSO (T-PSO) and constriction factor PSO (K-PSO). Further, utilizing pseudo-random binary sequence (PRBS)-based time domain reflectometry (TDR) method allows to obtain a reliable dataset for SVM classifier. The experimental results performed on a two-branch distribution line show the most optimal variant of PSO for short fault diagnosis.

Osteoporosis Risk Prediction Using Enhanced Support Vector Machine over Artificial Neural Network

Osteoporosis Risk Prediction Using Enhanced Support Vector Machine over Artificial Neural Network

Diabetes Prediction using Enhanced SVM and Deep Neural Network Learning Techniques – An Algorithmic Approach for Early Screening of Diabetes

Diabetes, caused by the rise in level of glucose in blood, has many latest devices to identify from blood samples. Diabetes, when unnoticed, may bring many serious diseases like heart attack, kidney disease. In this way, there is a requirement for solid research and learning model’s enhancement in the field of gestational diabetes identification and analysis. SVM is one of the powerful classification models in machine learning, and similarly, Deep Neural Network is powerful under deep learning models. In this work, we applied Enhanced Support Vector Machine and Deep Learning model Deep Neural Network for diabetes prediction and screening. The proposed method uses Deep Neural Network obtaining its input from the output of Enhanced Support Vector Machine, thus having a combined efficacy. The dataset we considered includes 768 patients’ data with eight major features and a target column with result “Positive” or “Negative”. Experiment is done with Python and the outcome of our demonstration shows that the deep Learning model gives more efficiency for diabetes prediction.