SVM Approach Research Articles

Selecting an optimal real-time fMRI neurofeedback method for alcohol craving control training.

Real-time fMRI neurofeedback (rt-fMRI-NF) is a technique in which information about an individual's neural state is given back to them, typically to enable and reinforce neuromodulation. Its clinical potential has been demonstrated in several applications, but lack of evidence on optimal parameters limits clinical utility of the technique. This study aimed to identify optimal parameters for rt-fMRI-NF-aided craving regulation training in alcohol use disorder (AUD). Adults with AUD (n = 30) participated in a single-session study of four runs of rt-fMRI-NF where they downregulated "craving-related" brain activity. They received one of three types of neurofeedback: multi-region of interest (ROI), support vector machine with continuous feedback (cSVM), and support vector machine with intermittent feedback (iSVM). Performance was assessed on the success rate, change in neural downregulation, and change in self-reported craving for alcohol. Participants had more successful trials in run 4 versus 1, as well as improved downregulation of the insula, anterior cingulate, and dorsolateral prefrontal cortex (dlPFC). Greater downregulation of the latter two regions predicted greater reduction in craving. iSVM performed significantly worse than the other two methods. Downregulation of the striatum and dlPFC, enabled by ROI but not cSVM neurofeedback, was correlated with a greater reduction in craving. rt-fMRI-NF training for downregulation of alcohol craving in individuals with AUD shows potential for clinical use, though this pilot study should be followed with a larger randomized-control trial before clinical meaningfulness can be established. Preliminary results suggest an advantage of multi-ROI over SVM and intermittent feedback approaches.

Improved SVM-Based Soil-Moisture-Content Prediction Model for Tea Plantation.

Accurate prediction of soil moisture content in tea plantations plays a crucial role in optimizing irrigation practices and improving crop productivity. Traditional methods for SMC prediction are difficult to implement due to high costs and labor requirements. While machine learning models have been applied, their performance is often limited by the lack of sufficient data. To address the challenges of inaccurate and inefficient soil moisture prediction in tea plantations and enhance predictive performance, an improved support-vector-machine- (SVM) based model was developed to predict the SMC in a tea plantation. The proposed model addresses several limitations of existing approaches by incorporating novel features and enhancing the SVM algorithm's performance, which was improved with the Bald Eagle Search algorithm (BES) method for hyper-parameter optimization. The study utilized a comprehensive dataset comprising soil moisture measurements and relevant environmental variables collected from a tea plantation. Feature selection techniques were applied to identify the most informative variables, including rainfall, temperature, humidity, and soil type. The selected features were then used to train and optimize the SVM model. The proposed model was applied to prediction of soil water moisture in a tea plantation in Guangxi State-owned Fuhu Overseas Chinese Farm. Experimental results demonstrated the superior performance of the improved SVM model in predicting soil moisture content compared to traditional SVM approaches and other machine-learning algorithms. The model exhibited high accuracy, robustness, and generalization capabilities across different time periods and geographical locations with R2, MSE, and RMSE of 0.9435, 0.0194 and 0.1392, respectively, which helps to enhance the prediction performance, especially when limited real data are available. The proposed SVM-based model offers several advantages for tea plantation management. It provides timely and accurate soil moisture predictions, enabling farmers to make informed decisions regarding irrigation scheduling and water resource management. By optimizing irrigation practices, the model helps enhance tea crop yield, minimize water usage, and reduce environmental impact.

The semiproximal SVM approach for multiple instance learning: a kernel-based computational study

The semiproximal Support Vector Machine technique is a recent approach for Multiple Instance Learning (MIL) problems. It exploits the benefits exhibited in the supervised learning by the Support Vector Machine technique, in terms of generalization capability, and by the Proximal Support Vector Machine approach in terms of efficiency. We investigate the possibility of embedding the kernel transformations into the semiproximal framework to further improve the testing accuracy. Numerical results on benchmark MIL data sets show the effectiveness of our proposal.

Bug severity classification in software using ant colony optimization based feature weighting technique

At the present, the delivery of the software should be time-bound without affecting the quality of the software. However, bug severity can affect the timely delivery of software. It is a crucial component of the software engineering, including maintenance and testing. Both phases are essential for bug severity classification but require much time. Generally, bug triage is responsible for classifying the bugs based on criticality/severeness. The manual execution of this process is error-prone. Consequently, a model for automatic bug classification is required to help the bug triage. In this work, the ant colony optimization (ACO) based feature extraction technique is proposed to extract more relevant features for bug severity classification. Furthermore, the ACO technique is integrated with NB, SVM, DeepFM and F-SVM techniques for predicting bug severity and classifying bugs into multi-severity classes. Several benchmark projects such as Eclipse, Mozilla, OpenFOAM, JBoss, and Firefox, are considered to evaluate the efficacy of the techniques above. The simulation outcomes are expressed in terms of Accuracy, Precision, Recall, and F1-measure. It is noted that the outcomes of the SVM, NB, DeepFM and F-SVM approaches are improved by the ACO-based feature weighting technique. The accuracy rate of ACO-F-SVM, ACO-NB, ACO-SVM, ACO-DeepFM, NB, SVM, F-SVM, DeepFM techniques are ranging in between 85.73 and 89.38%, 78% to 80%, 73% to 76%, 92.67% to 97.27 %, 71% to 77%,65% to 74%, 78.21% to 81.28% and 90.02% to 95.24% respectively for five benchmark projects. Further, proposed techniques are also produced better simulation results as compared with state-of –the-art techniques. Friedman and post hoc statistical tests are also conducted on proposed techniques.

Prediction of flow discharge in Mahanadi River Basin, India, based on novel hybrid SVM approaches

Prediction of flow discharge in Mahanadi River Basin, India, based on novel hybrid SVM approaches

Hybrid dual-channel convolution neural network (DCCNN) with spider monkey optimization (SMO) for cyber security threats detection in internet of things

Applications for the Internet of Things (IoT) have been employed in a broad range of industries, including smart homes, healthcare, smart energy, and Industrial 4.0. IoT offers many advantages, such as ease and efficiency, but it also adds a lot of new risks. The problem is often made worse by the potential number of linked IoT devices and the ad hoc nature of such networks. IoT management now faces substantial difficulties related to safety and confidentiality. Current research has shown that deep learning algorithms are quite effective and have numerous benefits over previous approaches for doing security assessments of IoT devices. An integrated deep learning approach is created in this work to identify files containing malware and pirated software through the Internet of Things network The Hybrid Dual-Channel Convolution Neural Network (DCCNN) with Spider Monkey Optimization (SMO) is called DCCNN-SMO advocated leveraging stolen reference code to detect software piracy. For the purpose of examining software piracy, the dataset was gathered via Google Code Jam (GCJ). To research the suggested strategy, collected 100 input data for internet users from GCJ. In addition, the DCCNN-SMO is utilized to visualize colored images to identify harmful intrusions in IoT networks. From the Leopard Mobile database, malware samples were taken for testing. The actual results demonstrate that the recommended method for calculating the cyber security risks in IoT performs categorization more over current approaches. The proposed DCCNN-SMO + SVM approach provides better resultant value of 98.55% whereas the other existing approaches like GIST + SVM is 86.1%, CLGM + SVM is 92.06% and DNN + SVM is 97.46%, LBP + SVM is 78.05%.

Identification of Fake Printed Medicine Packaging from a SVM Approach and Dots Shape Features

Identification of Fake Printed Medicine Packaging from a SVM Approach and Dots Shape Features

Design of Smart Wearable System for Sleep Tracking Using SVM and Multi-Sensor Approach

Design of Smart Wearable System for Sleep Tracking Using SVM and Multi-Sensor Approach

ASTHMA PREDICTION USING CLASSIFIER DATA FROM MACHINE LEARNING

Shortness of breath, wheezing, and frequently fatal attacks are symptoms of asthma, a lung condition brought on by airway blockage and inflammation. Asthma sufferers must be ready to anticipate severe exacerbations brought on by uncontrolled asthma. As a result, a framework that can precisely predict whether a patient will develop asthma is needed. In this study, an efficient mechanism for predicting asthma disease was devised by mining the data included in patients' prior health records. We make use of machine learning algorithms to illustrate the results. Four machine learning classification algorithms, KNN, ANN, SVM, and linear regression, were used in these trials to predict asthma disease at an early stage. Many measures are used to evaluate the effectiveness of all four models. The proportion of cases that are correctly and incorrectly classified serves as a gauge of a classification system's accuracy. The outcomes of the tests show that SVM approaches have the greatest accuracy. The experiment results are displayed using MATLAB software. KEYWORDS: Accuracy, KNN, ANN, SVM, Logistic Regression, MATLAB, Asthma Prediction, Machine Learning

Improved Facial Identification Using Adaptive Neuro-Fuzzy Logic Inference System

Objectives: To suggest an efficient pose invariant face recognition system (PCA-ANFIS) based on PCA and ANFIS. Methods: Using a dual-tree complex wavelet transform technique, face image improvement is obtained. ORL and YALE B data sets are used in this process. To train and test the network to recognize the person, two sets of photos were used. The system will return recognized if the test image matches one of the image’s training sets. The system will report not recognized if the test image does not match one of the image’s training sets. Accuracy, specificity, sensitivity, precision, and recall are the criteria taken into account. The suggested approach is then evaluated and compared to other known, gold standard facial recognition algorithms, including PCA, WP&LDA, GOBER WAVELET, SVM, and Bayesian Classifier methods. Findings: The study results indicated that the image accuracy, specificity, sensitivity, precision, and recall are enhanced toa level of 90, 85, 95, 86, and 95 per cent respectively when compared with the test results of other known gold standard facial recognition algorithms viz., PCA, WP &LDA, GOBET WAVELET, SVM and Bayesian Classifier approaches by which it can be concluded that it is an efficient approach for facial identification. Novelty : Both the feature extraction strategy and the classification approach play a strong emphasis on these factors. Principal component analysis is used for feature extraction, while an Adaptive Neuro Fuzzy Inference System is used for classification. The study proposed a novel neuro-fuzzy system-based face recognition method that is proposed in this study correctly that identifies the input face photos with a higher recognition rate Compared to the existing reports. Keywords: Facial Recognition; Image Processing; Biometric Technology; Facial Image Enhancement; Accuracy; Sensitivity

An Autonomous Marine Mucilage Monitoring System

Mucilage bloom is a current issue, especially for countries in the Mediterranean Basin, due to economic activities and ecological effects. The main causes are increased nutrient load due to organic and industrial pollution in the sea, global warming, and meteorological conditions at a level that can trigger mucilage bloom. It is important to take permanent measures to combat the increased nutrient load causing mucilage. However, there are various actions that can be performed during the mucilage bloom period, especially the collection of mucilage on the sea surface. Surface vehicles can be used to monitor and collect mucilage on the sea surface. The aim of this study is to design an autonomous marine mucilage monitoring system for systems such as unmanned surface vehicles (USV). We suggest monitoring the risky Marmara Sea continuously and recording some of the key parameters using a USV. The onboard solution proposed in this study has an architect based on a three-tier mucilage monitoring system. In the first tier, the sea surface is scanned with camera(s) in a certain radius in real time. When mucilage-candidate areas are determined, the vehicle is directed to this region autonomously. In the second tier, seawater in the region is measured in real time with some onboard sensors, pH level, conductivity, and dissolved oxygen level. The third tier is where real samples at three different depths are collected (if possible) for detailed posterior lab analysis. We have compared image processing, CNN (ResNet50), kNN, SVM, and FFNN approaches and have shown that the accuracy of our proposed mucilage classification method offers better and more promising performance.

A Novel Method for Anomaly Detection in the Internet of Things using Whale Optimization Algorithm

The Internet of Things (IoT) is integral to human life due to its pervasive applications in home appliances, surveillance, and environment monitoring. Resource-constrained IoT devices are easily accessible to attackers due to their direct connection to the unsafe Internet. Public access to the Internet makes IoT objects more susceptible to intrusion. As the name implies, anomaly detection systems are designed to identify anomalous traffic patterns that conventional firewalls fail to detect. Effective Intrusion Detection Systems (IDSs) design faces three major problems, including handling high dimensionality, selecting a learning algorithm, and comparing entered observations and traffic patterns using a distance or similarity measure. Considering the dynamic nature of the entities involved and the limited computing resources available, more than traditional anomaly detection approaches is required. This paper proposes a novel method based on Whale Optimization Algorithm (WOA) to detect anomalies in IoT-based networks that conventional firewall systems cannot detect. Experiments are conducted on the KDD dataset. The accuracy of the proposed method is compared for classifiers such as kNN, SVM, and DT approaches. The detection accuracy rate of the proposed method is significantly higher than that of other methods for DoS, probing, normal attacks, R2L attacks, and U2R attacks compared to other methods. This method shows an impressive increase in accuracy when detecting a wide range of malicious activities, from DoS, probing, and privilege escalation attacks, to remote-to-local and user-to-root attacks.

Retraction Note to: Optimal feature-based multi-kernel SVM approach for thyroid disease classification

Retraction Note to: Optimal feature-based multi-kernel SVM approach for thyroid disease classification

Identification of saline landscapes from an integrated SVM approach from a novel 3-D classification schema using Sentinel-1 dual-polarized SAR data

This study presented an integrated SVM classification method that investigated the relationship between intrinsic scattering attributes of the surface features and the conductivity characteristics of the soil in the newly proposed two- and three-dimensional classification (2D and 3D) schema to classify the saline landscape. The study was conducted in the Vellore district, Tamil Nadu, which is composed of heterogeneous saline landforms due to the active geological processes and anthropogenic activities. The intensity products of Sentinel-1 data (VV + VH) of C-band frequency were employed in the present study. The SVM with linear kernel has outperformed the other models, namely random forest (RF), naive Bayes (NB), and K-Nearest Neighbour (k−NN), in performing the broad level of classification from the 2D classification schema (SVMOA = 84.2%, RFOA = 80.4%, k-NNOA = 78.8%, NBOA = 68.4%). The soil EC values derived from the dielectric loss measurements (R2 = 0.79, ρ = 0.018, and α=95%) were used to introduce the integrated SVM approach in the 3D schema to further breakdown the mapped classes into non-saline (NS) (soil EC ≤ 2 ds/cm), slightly saline (SS) (soil EC = 2.1 to 4 ds/cm) and moderately saline (MS) (soil EC = 4.1 to 8 ds/cm) categories. The overall performance of the integrated SVM approach implemented for the 3D classification schema (F1 = 0.80) was found to be satisfactory, but with an associated uncertainty majorly from MS (Precision = 0.52, F1 = 0.69), SS (Recall = 0.09, F1 = 0.15), and NS waterbodies (Recall = 0.18, F1 = 0.29) as shown in the Precision-Recall graph (AUCPR3D = 0.62). However, with the promising performance level demonstrated for the other nine classes such as NS, SS, and MS wet soil (F1 = 0.92, 0.92, 0.96), healthy plants (F1 = 0.83), salt-tolerant plants under SS and MS conditions (F1 = 0.83, 0.88), and waterlogged vegetation under NS, SS, and MS conditions (F1 = 0.82, 0.83, 0.83), the proposed classification scheme becomes an effective method to map saline and non-saline features from dual-polarimetric SAR data.

Accurate Identification of ADHD among Adults Using Real-Time Activity Data.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopment disorder that affects millions of children and typically persists into adulthood. It must be diagnosed efficiently and consistently to receive adequate treatment, otherwise, it can have a detrimental impact on the patient’s professional performance, mental health, and relationships. In this work, motor activity data of adults suffering from ADHD and clinical controls has been preprocessed to obtain 788 activity-related statistical features. Afterwards, principal component analysis has been carried out to obtain significant features for accurate classification. These features are then fed into six different machine learning algorithms for classification, which include C4.5, kNN, Random Forest, LogitBoost, SVM, and Naive Bayes. The detailed evaluation of the results through 10-fold cross-validation reveals that SVM outperforms other classifiers with an accuracy of 98.43%, F-measure of 98.42%, sensitivity of 98.33%, specificity of 98.56% and AUC of 0.983. Thus, a PCA-based SVM approach appears to be an effective choice for accurate identification of ADHD patients among other clinical controls using real-time analysis of activity data.

WTD-PSD: Presentation of Novel Feature Extraction Method Based on Discrete Wavelet Transformation and Time-Dependent Power Spectrum Descriptors for Diagnosis of Alzheimer's Disease.

Alzheimer's disease (AD) is a type of dementia that affects the elderly population. A machine learning (ML) system has been trained to recognize particular patterns to diagnose AD using an algorithm in an ML system. As a result, developing a feature extraction approach is critical for reducing calculation time. The input image in this article is a Two-Dimensional Discrete Wavelet (2D-DWT). The Time-Dependent Power Spectrum Descriptors (TD-PSD) model is used to represent the subbanded wavelet coefficients. The principal property vector is made up of the characteristics of the TD-PSD model. Based on classification algorithms, the collected characteristics are applied independently to present AD classifications. The categorization is used to determine the kind of tumor. The TD-PSD method was used to extract wavelet subbands features from three sets of test samples: moderate cognitive impairment (MCI), AD, and healthy controls (HC). The outcomes of three modes of classic classification methods, including KNN, SVM, Decision Tree, and LDA approaches, are documented, as well as the final feature employed in each. Finally, we show the CNN architecture for AD patient classification. Output assessment is used to show the results. Other techniques are outperformed by the given CNN and DT.

A Deep Learning Model to Identify Twins and Look Alike Identification Using Convolutional Neural Network (CNN) and to Compare the Accuracy with SVM Approach

Aim: The main aim of this work is to identify twins and look alike using Innovative convolutional neural networks (CNN) and comparing them with SVM. Materials and Methods: Two groups such as Support Vector Machine and convolutional Neural Network were considered. Each algorithm took N=2 samples from the dataset collected and performed two iterations on each algorithm to identify twin faces. Result: The accuracy of identical twin-face detection for biometrics was calculated. It seems Innovative CNN got a significantly better accuracy of (94.01%) when compared with SVM, which has had an accuracy of (83.98%) and having the significance value of (p=0.029). Conclusion: In the work, it could have recognized identical twin faces and differentiate them with different facial features for easy detection to biometrics where Innovative CNN got better accuracy than SVM.

Towards Improvement of LSTM and SVM Approach for Multiclass Fall Detection System

Telemonitoring of human physiological data helps detect emergency occurrences for subsequent medical diagnosis in daily living environments. One of the fatal emergencies in falling incidents. The goal of this paper is to detect significant incidents such as falls. The fall detection system is essential for human body movement investigation for medical practitioners, researchers, and healthcare businesses. Accelerometers have been presented as a practical, low-cost, and dependable approach for detecting and predicting outpatient movements in the user. The accurate detection of body movements based on accelerometer data enables the creation of more dependable systems for incorporating long-term development in physiological remarks. This research describes an accelerometer-based platform for detecting users' body movement when they fall. The ADXL345, MMA8451q, and ITG3200 body sensors capture activity data, subsequently classified into 15 fall incident classes based on SisFall dataset. Falling incidents classification is performed using Long Short-Term Memory results in best AUC-ROC value of 97.7% and best calculation time of 6.16 seconds. Meanwhile, Support Vector Machines results in the best AUC-ROC value of 98.5% and best calculation times of 17.05 seconds.

Analysis of Software Sizing and Project Estimation prediction by Machine Learning Classification

In this study, the outcomes of trials with various projects are analyzed in detail. Estimators may decrease mistakes by combining several estimating strategies, which helps them maintain a close eye on the difference between their estimations and reality. An effort estimate is a method for estimating a model's correctness by calculating the total amount of effort needed. It's a major pain in the backside of software development. Several prediction methods have recently been created to find an appropriate estimate. The suggested SVM approach is utilized to reduce the estimation error for the project estimate to the lowest possible value. As a result, throughout the software sizing process, the ideal or exact forecast is achieved. Early in a model's development, the estimate is erroneous since the needs are not defined, but as the model evolves, it becomes more and more accurate. Because of this, it is critical to choose a precise estimate for each software model development. Observations and suggestions for further study of software sizing approaches are also included in the report.

Computer-assisted grading of follicular lymphoma: a classification based on SVM, machine learning, and transfer learning approaches

ABSTRACT This proposed work implements various classification approaches to classify the H&E-stained Follicular Lymphoma tissue sample. As part of the process, k-means clustering is used to isolate cytological components like the nucleus extracellular and cytoplasmic regions from images. Then feature space vector is constructed by combining global feature extraction techniques like LBP, LDP, GLCM, and other local feature-extraction techniques. To classify FL images into their respective grades, feature space vectors obtained from feature extraction algorithms are input into a multiclass SVM. Moreover, classification accuracies were explicitly tested with different classifiers like CNN and other pre-trained deep learning networks that can directly operate on raw images without any preprocessing to classify the FL images into their respective grades. The efficacy of different classifiers is presented. This preliminary work provides a proof of concept for incorporating automated FL tissue diagnostic systems into future pathology workflows to supplement the pathologists'.