Support Vector Machine Training Research Articles

Optimizing Parameter Efficiency in Machine Learning Models: A Focus on Reducing Memory Overhead with L-BFGS-Optimized Algorithms

This research addresses the critical challenge of detecting cotton leaf diseases through an advanced image processing and machine learning approach. The study primarily focuses on the extraction of significant features from cotton leaf images to facilitate accurate disease identification. The novelty of this work lies in the application of the Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm for optimizing Support Vector Machines (SVM), enhancing the precision of disease detection in cotton leaves. This research method involves a comprehensive process of acquiring high-resolution leaf images, followed by an effective feature extraction technique that isolates crucial characteristics indicative of various plant diseases. Subsequently, these features are employed to train an SVM model optimized by the L-BFGS algorithm, a decision process that marks a significant departure from traditional gradient descent methods in SVM training. A pivotal aspect of this study is the comparative analysis conducted between the proposed L-BFGS-optimized SVM model and prevalent machine learning models including Random Forest, Decision Tree, Regression Models, and K-Nearest Neighbors (KNN). This comparison is grounded on various performance metrics such as accuracy, precision, recall, and F1-score, offering a comprehensive evaluation of each model's effectiveness in disease detection. The results of this research are expected to demonstrate the superiority of the L-BFGS-optimized SVM in terms of accuracy and efficiency in detecting cotton leaf diseases. This advancement holds substantial promise for agricultural technology, potentially leading to more informed and timely decision-making in crop management and disease prevention. The findings of this study aim to contribute significantly to the field of agricultural informatics, particularly in the domain of plant disease detection and management, by providing a more robust, accurate, and efficient tool for farmers and agriculturalists.

A Comparison of Veterans with Problematic Opioid Use Identified through Natural Language Processing of Clinical Notes versus Using Diagnostic Codes.

Opioid use disorder is known to be under-coded as a diagnosis, yet problematic opioid use can be documented in clinical notes, which are included in electronic health records. We sought to identify problematic opioid use from a full range of clinical notes and compare the demographic and clinical characteristics of patients identified as having problematic opioid use exclusively in clinical notes to patients documented through ICD opioid use disorder diagnostic codes. We developed and applied a natural language processing (NLP) tool that combines rule-based pattern analysis and a trained support vector machine to the clinical notes of a patient cohort (n = 222,371) from two Veteran Affairs service regions to identify patients with problematic opioid use. We also used a set of ICD diagnostic codes to identify patients with opioid use disorder from the same cohort. The NLP tool achieved 96.6% specificity, 90.4% precision/PPV, 88.4% sensitivity/recall, and 94.4% accuracy on unseen test data. NLP exclusively identified 57,331 patients; 6997 patients had positive ICD code identifications. Patients exclusively identified through NLP were more likely to be women. Those identified through ICD codes were more likely to be male, younger, have concurrent benzodiazepine prescriptions, more comorbidities, and more care encounters, and were less likely to be married. Patients in both these groups had substantially elevated comorbidity levels compared with patients not documented through either method as experiencing problematic opioid use. Clinicians may be reluctant to code for opioid use disorder. It is therefore incumbent on the healthcare team to search for documentation of opioid concerns within clinical notes.

The performance of Logistic Regression, Decision Tree, KNN, Naive Bayes and SVM for identifying Automotive Cybersecurity Attack and Prevention: An Experimental Study

The automotive industry has witnessed a significant increase in cyber threats as vehicles become more connected and reliant on software-driven systems. To safeguard against these threats, effective cybersecurity measures must be implemented. This paper explores the use of Support Vector Machine (SVM) algorithms as a means of bolstering automotive cybersecurity attack prevention. SVM algorithms have demonstrated remarkable capabilities in various domains due to their ability to handle complex and high-dimensional data. By leveraging SVM algorithms, this research aims to enhance the detection and prevention of cybersecurity attacks targeting automotive systems.
 The proposed approach involves training SVM models using labeled datasets that include both normal and anomalous driving scenarios. By analyzing various features and patterns extracted from the data, the SVM models can learn to differentiate between normal and malicious behaviour. These models can then be used in real-time to identify and mitigate potential cyber threats.
 The results of this study is compared with other machine learning algorithm like Logistic Regression, Naive Bayes, KNN, Decision Tree, highlight the effectiveness of SVM algorithms in detecting and preventing automotive cybersecurity attacks. The models exhibit high accuracy rates in distinguishing between normal and anomalous behavior, providing a robust defense mechanism against potential threats.
 In conclusion, this research emphasizes the significance of leveraging SVM learning algorithms for automotive cybersecurity attack prevention. By harnessing the power of machine learning, automotive systems can be fortified against cyber threats, ensuring the safety and integrity of vehicles and their passengers.

Data reduction for SVM training using density-based border identification.

Numerous classification and regression problems have extensively used Support Vector Machines (SVMs). However, the SVM approach is less practical for large datasets because of its processing cost. This is primarily due to the requirement of optimizing a quadratic programming problem to determine the decision boundary during training. As a result, methods for selecting data instances that have a better likelihood of being chosen as support vectors by the SVM algorithm have been developed to help minimize the bulk of training data. This paper presents a density-based method, called Density-based Border Identification (DBI), in addition to four different variations of the method, for the lessening of the SVM training data through the extraction of a layer of border instances. For higher-dimensional datasets, the extraction is performed on lower-dimensional embeddings obtained by Uniform Manifold Approximation and Projection (UMAP), and the resulting subset can be repetitively used for SVM training in higher dimensions. Experimental findings on different datasets, such as Banana, USPS, and Adult9a, have shown that the best-performing variations of the proposed method effectively reduced the size of the training data and achieved acceptable training and prediction speedups while maintaining an adequate classification accuracy compared to training on the original dataset. These results, as well as comparisons to a selection of related state-of-the-art methods from the literature, such as Border Point extraction based on Locality-Sensitive Hashing (BPLSH), Clustering-Based Convex Hull (CBCH), and Shell Extraction (SE), suggest that our proposed methods are effective and potentially useful.

Performance evaluation of adaptive offloading model using hybrid machine learning and statistic prediction

We introduce fast sensor diagnosis and focuses on intelligent offloading skills to enhance the sensor data screening efficiency. This study proposed the adaptive offloading model based on statistics-based prediction feedback and sensor candidate filtering. For the statistics-based filtering, sliding sensor grids and compounded sensor context were devised. This study also proposed hybrid prediction model using support vector machine (SVM) and k-nearest neighbors (KNN) machine training for the adaptive offloading. Therefore, the sensor information that is highly likely to be the cause of the actual device faults can be selected and transmitted, resulting in improved offloading performance. The test results through Google Colab show that the fault prediction accuracy of proposed models is 95%.

Sensor-Based Quantification of MDS-UPDRS III Subitems in Parkinson's Disease Using Machine Learning.

Wearable sensors could be beneficial for the continuous quantification of upper limb motor symptoms in people with Parkinson's disease (PD). This work evaluates the use of two inertial measurement units combined with supervised machine learning models to classify and predict a subset of MDS-UPDRS III subitems in PD. We attached the two compact wearable sensors on the dorsal part of each hand of 33 people with PD and 12 controls. Each participant performed six clinical movement tasks in parallel with an assessment of the MDS-UPDRS III. Random forest (RF) models were trained on the sensor data and motor scores. An overall accuracy of 94% was achieved in classifying the movement tasks. When employed for classifying the motor scores, the averaged area under the receiver operating characteristic values ranged from 68% to 92%. Motor scores were additionally predicted using an RF regression model. In a comparative analysis, trained support vector machine models outperformed the RF models for specific tasks. Furthermore, our results surpass the literature in certain cases. The methods developed in this work serve as a base for future studies, where home-based assessments of pharmacological effects on motor function could complement regular clinical assessments.

Fast CU size decision and intra-prediction mode decision method for H.266/VVC

AbstractH.266/Versatile Video Coding (VVC) is the most recent video coding standard developed by the Joint Video Experts Team (JVET). The quad-tree with nested multi-type tree (QTMT) architecture that improves the compression performance of H.266/VVC is introduced. Moreover, H.266/VVC contains a greater number of intra-prediction modes than H.265/High Efficiency Video Coding (HEVC), totalling 67. However, these lead to extremely the coding computational complexity. To cope with the above issues, a fast intra-coding unit (CU) size decision method and a fast intra-prediction mode decision method are proposed in this paper. Specifically, the trained Support Vector Machine (SVM) classifier models are utilized for determining CU partition mode in a fast CU size decision scheme. Furthermore, the quantity of intra-prediction modes added to the RDO mode set decreases in a fast intra-prediction mode decision scheme based on the improved search step. Simulation results illustrate that the proposed overall algorithm can decrease 55.24% encoding runtime with negligible BDBR.

Word2Vec and SVM Fusion for Advanced Sentiment Analysis on Amazon Reviews

As a matter of fact, sentiment analysis plays a crucial role in different kinds of reviews for online shopping. With this in mind, this study explores the application of Word2Vec and Support Vector Machine (SVM) in sentiment analysis of Amazon reviews. To be specific, initially, this study conducted preprocessing and feature extraction on a large-scale review dataset, revealing deep semantic relationships between words through the Word2Vec model. Subsequently, this study utilized SVM for model training and optimization, achieving efficient and accurate sentiment classification. According to the analysis, preliminary experimental results indicate that this combined method can effectively capture complex patterns and relationships in the text, demonstrating significant advantages in enhancing the accuracy as well as efficiency of sentiment analysis compared to traditional methods. Overall, these results shed light on providing a powerful tool for e-commerce platforms to understand better and analyze consumer opinions and feelings as well as guiding further exploration.

Deep Texture Analysis—Enhancing CT Radiomics Features for Prediction of Head and Neck Cancer Treatment Outcomes: A Machine Learning Approach

(1) Background: Some cancer patients do not experience tumour shrinkage but are still at risk of experiencing unwanted treatment side effects. Radiomics refers to mining biomedical images to quantify textural characterization. When radiomics features are labelled with treatment response, retrospectively, they can train predictive machine learning (ML) models. (2) Methods: Radiomics features were determined from lymph node (LN) segmentations from treatment-planning CT scans of head and neck (H&N) cancer patients. Binary treatment outcomes (complete response versus partial or no response) and radiomics features for n = 71 patients were used to train support vector machine (SVM) and k-nearest neighbour (k-NN) classifier models with 1–7 features. A deep texture analysis (DTA) methodology was proposed and evaluated for second- and third-layer radiomics features, and models were evaluated based on common metrics (sensitivity (%Sn), specificity (%Sp), accuracy (%Acc), precision (%Prec), and balanced accuracy (%Bal Acc)). (3) Results: Models created with both classifiers were found to be able to predict treatment response, and the results suggest that the inclusion of deeper layer features enhanced model performance. The best model was a seven-feature multivariable k-NN model trained using features from three layers deep of texture features with %Sn = 74%, %Sp = 68%, %Acc = 72%, %Prec = 81%, %Bal Acc = 71% and with an area under the curve (AUC) the receiver operating characteristic (ROC) of 0.700. (4) Conclusions: H&N Cancer patient treatment-planning CT scans and LN segmentations contain phenotypic information regarding treatment response, and the proposed DTA methodology can improve model performance by enhancing feature sets and is worth consideration in future radiomics studies.

Predicting the coefficient of friction in a sliding contact by applying machine learning to acoustic emission data

It is increasingly important to monitor sliding interfaces within machines, since this is where both energy is lost, and failures occur. Acoustic emission (AE) techniques offer a way to monitor contacts remotely without requiring transparent or electrically conductive materials. However, acoustic data from sliding contacts is notoriously complex and difficult to interpret. Herein, we simultaneously measure coefficient of friction (with a conventional force transducer) and acoustic emission (with a piezoelectric sensor and high acquisition rate digitizer) produced by a steel–steel rubbing contact. Acquired data is then used to train machine learning (ML) algorithms (e.g., Gaussian process regression (GPR) and support vector machine (SVM)) to correlated acoustic emission with friction. ML training requires the dense AE data to first be reduced in size and a range of processing techniques are assessed for this (e.g., down-sampling, averaging, fast Fourier transforms (FFTs), histograms). Next, fresh, unseen AE data is given to the trained model and the resulting friction predictions are compared with the directly measured friction. There is excellent agreement between the measured and predicted friction when the GPR model is used on AE histogram data, with root mean square (RMS) errors as low as 0.03 and Pearson correlation coefficients reaching 0.8. Moreover, predictions remain accurate despite changes in test conditions such as normal load, reciprocating frequency, and stroke length. This paves the way for remote, acoustic measurements of friction in inaccessible locations within machinery to increase mechanical efficiency and avoid costly failure/needless maintenance.

Particle swarm optimization support vector machine-based coal and rock cutting tool load spectrum identification method

The goal of this research is to achieve safe and efficient excavation of coal and rock tunnels with complex geological structures, and to enhance the self-sensing ability of coal and rock cutting equipment and tools. Particle swarm optimization support vector machine is used to identify the cutting state of disc cutting tools. EDEM finite element analysis software is used to analyze cutting process characteristics of the disc cutting tool when used to cut through coal and rock with different compressive strengths. Empirical mode decomposition is used to decompose the load spectrum characteristics; for this purpose, the first-order and seventh-order intrinsic mode functions containing all the feature information of the original signal of the load spectrum are selected. The sample entropy is calculated as the feature input vector. The extracted feature vector is input into the trained support vector machine model and the particle swarm optimization support vector machine model. By extracting the sample entropy of the load spectrum of the disc cutter as the feature vector, the particle swarm optimization support vector model is used to identify the cutting state of the coal and rock. The recognition accuracy of the support vector machine model before and after the improvement is compared and analyzed. The results show that compared to the unoptimized support vector machine, the support vector machine optimized by particle swarm optimization can identify the load spectrum of the coal more quickly and accurately. The recognition accuracy is 96,82%, which verifies the effectiveness of the particle swarm optimization support vector machine model in identifying the load spectrum of the coal and rock disc cutter.

TSVM: Transfer Support Vector Machine for Predicting MPRA Validated Regulatory Variants.

Genome-wide association studies have shown that common genetic variants associated with complex diseases are mostly located in non-coding regions, which may not be causal. In addition, the limited number of validated non-coding functional variants makes it difficult to develop an effective supervised learning model. Therefore, improving the accuracy of predicting non-coding causal variants has become critical. This study aims to build a transfer learning-based machine learning method for predicting regulatory variants to overcome the problem of limited sample size. This paper presents a supervised learning method transfer support vector machine (TSVM) for massively parallel reporter assays (MPRA) validated regulatory variants prediction. First, uses a convolutional neural network to extract features with transfer learning. Second, the extracted features are selected by random forest method. Third, the selected features are used to train support vector machine for classification. We performed scale sensitivity experiments on the MPRA dataset and validated the effectiveness of transfer learning. The model achieves the Mcc of 0.326 and the AUC of 0.720, which are higher than the state-of-the-art method. The source code of our proposed TSVM can be found in https://github.com/biomg/TSVM.

Energy-efficient classification strategy for detecting interference and malicious sensor nodes in wireless body area Networks

Wireless Body Area Networks (WBANs) play a vital role in healthcare monitoring, using wireless sensors to track physiological parameters and predict illness onset. This study proposes a novel approach for detecting interference and malicious sensor nodes in WBANs, crucial for maintaining system integrity and performance. The method combines feature-based techniques with classification strategies to accurately identify anomalies. Features are taken from WBAN nodes and used to train Support Vector Machine (SVM) classifiers, which makes interference detection work well. A neurofuzzy inference system (ANFIS) classifier is also used to train the system on trusted and untrusted nodes at the start, which makes classification easier in real-world WBAN situations. Link failures due to rogue sensor nodes can severely impact WBAN performance, emphasizing the need for efficient detection and correction mechanisms. The proposed strategy introduces a weight metric to identify broken links, enhancing system reliability. Evaluation metrics, including LFD latency and packet delivery ratio, are analyzed to assess the efficacy of the approach. By improving interference detection and addressing link failures, this study contributes to enhancing the efficiency and reliability of WBAN networks, critical for advancing healthcare monitoring technologies.

Hardware fingerprint construction of optical transmitters based on permutation entropy spectrum of chaos for secure authentication

In this paper, an optical transmitter authentication method using hardware fingerprints based on the permutation entropy spectrum (PES) of electro-optic chaos is proposed. By means of permutation analysis of chaotic time series generated by the electro-optic feedback loop, PES is defined and used as the hardware fingerprint of optical transmitters for secure authentication. The time division multiplexing (TDM) module is used to combine the chaotic signal and the message, while the optical temporal encryption (OTE) module is introduced to ensure the security of the fingerprint. At the receiver, the time division demultiplexing (TDD) module and the optical time decryption (TDD) module are introduced to recover the original chaotic signal. The trained support vector machine (SVM) models will then recognize legal and illegal optical transmitters by judging whether the PES of the received chaotic signal is legitimate or not. The simulation and experimental results show that PES of electro-optic chaos has obvious fingerprint characteristics and is sensitive to the feedback delay T in the feedback loop. The authentication system based on PES can recognize both legal and illegal transmitters in optical networks, and the recognition accuracy can reach about 97%. Under the condition of a low signal-to-noise ratio, the authentication system based on the permutation entropy spectrum can still distinguish legal and illegal transmitters effectively. Meanwhile, our scheme has high controllability and flexibility.

Silicone/ broadleaf wood fiber /MWCNTS composite stretchable strain sensor for smart object identification

Stretchable strain sensors have been rapidly developing in recent years. Fabricating strain sensors with both large workable strain range and high sensitivity via a low-cost and straightforward process is still challenging. Here, this work created a multi-walled carbon nanotubes (MWCNTs) / broadleaf wood fiber (BWF) film, the highly micro-porous network of which allows a slow infiltration of liquid silicone in a vacuum environment to form an elastic composite, serving as a stretchable strain sensor. The sensor showed an ultra-wide strain of up to 440% and high sensitivity of GF (gage factor) of 3.3 at 0∼200% strain and of 7.8 at 200∼440% strain. In addition, the sensor is able to endure 5500 cyclic loading/unload at 100% strain with no damage in its mechanical structure and small variation in electrical resistance. To demonstrate its practicality, the sensor was incorporated into a soft pneumatic griper and utilized as a bending sensor to detect the griper deformation. The system demonstrated a high accuracy of 99.33% in the classification of lab beakers using a data-driven framework built with a trained Support Vector Machine (SVM) algorithm.

Multi-objective diesel engine emission management and control technology based on SVM and NSGA-II

To improve the emission control effect of diesel engines, a pre-emission model for diesel engines using biofuels is constructed using support vector machine. Considering that traditional support vector machine training relies on initial parameters and is prone to local convergence, a genetic algorithm model is adopted to construct an improved diesel engine pre-emission model. At the same time, with the optimization goal of diesel engine emissions of particles and gases, the improved diesel engine pre-emission model is used to obtain emission data as training parameters. The second generation non-dominated genetic algorithm is introduced to construct a multi-objective diesel engine emission control model, achieving optimal control of diesel engine emissions. In the experimental analysis of diesel engine pre emission models, the proposed pre emission model has a better prediction effect in nitrogen oxides, with an average prediction accuracy of 0.986, which is better than the Particle Swarm Optimization-Support Vector Machine model's 0.906, and is more accurate in detecting emissions. Simultaneously predicting particulate matter, the average prediction accuracy of the proposed model is 0.978, while the prediction accuracy of the Particle Swarm Optimization-Support Vector Machine model is 0.902, indicating that the overall prediction effect of the proposed method is better. In the experimental analysis of multi-objective optimization models, the proposed multi-objective optimization model performs best in diesel emission optimization. The proposed model can achieve convergence in the shortest time, and the minimum nitrogen oxide emissions are 210.7 ppm. Compared with Non-dominated Sorting Genetic Algorithm II and Multi-Objective Particle Swarm Optimization models, the particle emission optimization effect is improved by 23.5 % and 18.6 %. From this, it can be seen that the proposed method has excellent performance in diesel engine emission monitoring and pollutant optimization, surpassing related technologies in the same period. The research technology provides important technical references for the monitoring and emission control of harmful substances in diesel engines.

Operation and maintenance strategy of radio transmission equipment based on big data

AbstractIn recent years, the operation and maintenance of radio transmission equipment have faced challenges such as equipment aging, high maintenance costs, long response time, and technological updates. It is necessary to adapt to new technologies and improve maintenance efficiency. The operation and maintenance strategy of radio transmitting equipment based on big data (BD) aims to optimize the reliability and operating efficiency of equipment through data analysis and intelligent algorithms, and reduce failure risk and maintenance costs. By analyzing the operation data and maintenance records of a large number of radio transmission equipment, it revealed the patterns and trends of equipment failures, in order to timely identify and predict possible faults, and take corresponding maintenance measures. This article adopted methods such as data collection and preprocessing, fault prediction and diagnosis, and maintenance optimization to achieve accurate monitoring of equipment operation status, fault prediction and diagnosis, optimized maintenance, and decision support. This can improve the operational efficiency and reliability of radio transmission equipment, which is used for fault diagnosis of radio transmission equipment through SVM (Support Vector Machine) algorithms. By training SVM classifiers, this article associated different types of faults with their feature data, thereby quickly and accurately determining the type of equipment fault based on real‐time collected device data, providing a basis for maintenance decision‐making. The experimental results show that the failure frequency of the radio transmitting equipment in the control group was between 7% and 11%, while the failure frequency of the radio transmitting equipment in the experimental group was between 3% and 7%. Experiments show that BD technology can effectively reduce the failure frequency of radio transmitting equipment. The empirical results indicate that this strategy can improve the reliability of equipment and reduce operation and maintenance costs, which has important guiding significance for the operation and maintenance management of radio equipment. This would bring about more efficient and reliable operation and maintenance management of radio equipment, making important contributions to the smooth progress of broadcasting business and the improvement of user experience.

A Filter-SQP strategy for training Support Vector Machine models

This paper introduces a filtering strategy for addressing optimization problems arising in binary Support Vector Machine classification. The training optimization problem aims to solve the dual formulation which involves a quadratic objective function subjected to a linear and box constraints. Our approach employs a Filter algorithm with Sequential Quadratic Programming iterations that minimize the quadratic Lagrangian approximations. Notably, we utilize the exact Hessian matrix in our numerical experiments to seek the desired classification function. Moreover, we present a Filter algorithm combined with the Augmented Lagrangian method aiming to accelerate the algorithm convergence. To substantiate our method's effectiveness, we conduct numerical experiments through MATLAB, comparing outcomes with alternative methodologies detailed in existing literature. Numerical experiments shows that the Filter--SQP combined with Augmented Lagrangian method is competitive and efficient method compared with an interior-point based solver and LIBSVM software in relation of classification metrics and CPU-time.

A two-stage prediction filling method with support vector technologies optimized competitively in stages by grey wolf optimizer and particle swarm optimization for missing fasting blood glucose.

Missing values often affect the data utilization in epidemiological survey. In this study, according to the cut-off point value of the medical diagnostic standard of fasting blood glucose for diabetes, we divide fasting blood glucose test data from the China Health and Nutrition Survey (CHNS) of Shandong province in 2009 into two classes: the normal and the abnormal. Accordingly, for missing fasting blood glucose values, we propose a two-stage prediction filling method with optimized support vector technologies competitively by particle swarm optimization (PSO) or grey wolf optimizer (GWO), which is to first predict the class of the missing data with support vector machine (SVM) in the first stage and then predict the missing value with support vector regression (SVR) within the predicted class in the second stage. In addition, we use the LIBSVM as a gold standard to train both SVM and SVR in different stages. For two kinds of competitive optimizers in stages, in the first stage GWO has the highest classification accuracy (91.1%), and in the second stage PSO has the smallest in-class mean absolute error (0.48). So, GWO-SVM-PSO-SVR is determined as the optimal model and a predicted value with it serves as a fill value. The comparison results of the models in empirical analysis also show that it outdoes any of the other filling models in terms of mean absolute error and mean absolute percentage error. In addition, the sensitivity analysis shows that it presents high tolerance as the sample size changes and has a good stability.

Research on Automatic Recognition of New Words on Weibo

To effectively capture emerging vocabulary on Weibo, this article proposes a new Weibo new word recognition strategy that combines Weibo data and support vector machine. Firstly, select positive and negative example sentences from Weibo corpus and trained corpus with part of speech tagging. Then, the lexical features in these sentences are transformed into vectors, and then trained using support vector machines to obtain classification support vectors for Weibo new words. Finally, input the vectorized features into the already trained support vector machine classifier to identify new Weibo words. Based on the experimental results, the system found the optimal feature combination.