SVM Classifier Research Articles

Malware Detection in Files and URL’s Using Machine Learning

The rampant extrusion of personal or sensitive data by malicious software has inflicted significant damage worldwide, posing a critical threat to individuals and various sectors of society. Cyber attackers engage in relentless warfare against computer systems, deploying malware to breach crucial data. Consequently, safeguarding this data has become a paramount concern for researchers. This paper aims to address this challenge by proposing the development of an application leveraging machine learning algorithms to discern between malicious and legitimate files effectively. In the contemporary era, marked by pervasive technological advancements, the widespread adoption of the internet has facilitated unprecedented connectivity but also heightened the risk of cyber threats perpetrated by malicious actors. These threats, often executed through the deployment of malware, have inflicted substantial financial losses, amounting to billions of dollars globally. Consequently, safeguarding against malicious attacks has become a paramount endeavor in the ongoing battle against cybercrime. To address this imperative, this paper endeavors to leverage machine learning algorithms to prognosticate the likelihood of malware infections in computer systems, utilizing a supervised learning approach alongside gradient boosting algorithms. Drawing upon a publicly available dataset, meticulously partitioned into training and testing sets, the study embarks on four distinct experiments employing the aforementioned algorithms. Notably, the findings underscore Light GBM as the most efficacious model, boasting an impressive AUC Score of 0.73926, thus signifying a pivotal advancement in fortifying cyber defenses through predictive analytics. Malware poses a significant threat in today's digital landscape, with various forms proliferating over the last decade, leading to substantial financial losses for organizations. Defined as malicious software, malware wreaks havoc on users' computers through various means. To address this challenge, a solution leveraging machine learning techniques has been proposed to detect malware in downloaded files. By analyzing features such as MD5 hash, Optional Header size, and Load Configuration Size, different machine learning algorithms are trained to distinguish between malicious and benign files effectively. Through rigorous training and comparison using Validation and Test datasets, the SVM Classifier emerged as the most accurate, achieving up to 99.99% accuracy in detecting malware types like Adware, Trojan, Backdoors, Unknown, Multidrop, Rbot, Spam, and Ransomware. This approach not only identifies malware but also mitigates potential risks to users' systems, safeguarding against detrimental impacts from infections.

Identification of rail cracks based on path graph features and SVM

In order to improve the accuracy of rail crack identification, a new method based on path graph feature and support vector machine is proposed. This method uses graph signal processing and graph theory to transform the magnetic flux leakage signal of rail crack, calculates the “time domain” and “frequency domain” statistics of the path graph signal, and effectively identifies rail cracks with different defect parameters by SVM classifier. The measured data verify the effectiveness of this method, which shows that the method of identifying rail cracks by using path graph features has higher accuracy and stability. The innovation of this method is that it draws on the idea of transform domain features to extract the graph domain features that can best represent the MFL signal. Compared with the 31 features used by the traditional method, this method only needs 22 features to achieve better recognition results and has shorter training time. For the recognition rate of 18 kinds of cracks, the average recognition rate of this method is more than 83.51%, and the highest recognition rate is 95.34%. Therefore, this study provides a new way for magnetic leakage analysis and treatment of rail crack detection, has important practical value, and provides beneficial enlightenment for further research in related fields.

Energy optimization for wireless sensor network using minimum redundancy maximum relevance feature selection and classification techniques

In wireless sensor networks (WSN), conserving energy is usually a basic issue, and several approaches are applied to optimize energy consumption. In this article, we adopt feature selection approaches by using minimum redundancy maximum relevance (MRMR) as a feature selection technique to minimize the number of sensors thereby conserving energy. MRMR ranks the sensors according to their significance. The selected features are then classified by different types of classifiers; SVM with linear kernel classifier, naïve Bayes classifier, and k-nearest neighbors classifier (KNN) to compare accuracy values. The simulation results illustrated an improvement in the lifetime extension factor of sensors and showed that the KNN classifier gives better results than the naïve Bayes and SVM classifier.

Blockchain Enabled Social Network for Detecting Fake Accounts

Front end technology with permanent accounts is employed in online social networks to help people get to know one another. In an effort to maintain connection with everyone, Facebook and Twitter are changing along with the users. OSNs are used by them for planning events, news exchange, interpersonal communication, and even running their own online enterprises. Because of OSNs' quick growth and the abundance of personal data that its users have shared, attackers and imposters have been lured to them with the intention of stealing personal information, spreading disruptive activities, and publishing false information. A further rationale for creating fictitious profiles for hateful accounts. In an attempt to harm the reputation of the person they detest, these users make profiles using their usernames and publish photographs and articles that aren't related to them. to use blockchain technology, Aadhar number verification, and SVM profile classification to create a reliable solution for detecting phoney social network profiles. By analysing several aspects of social media profiles, the SVM classification uses machine learning to discern between real and fraudulent accounts. By verifying Aadhar numbers through legitimate channels, Aadhar number verification is integrated into the registration process, guaranteeing the authenticity of user identities. A decentralised and secure identity management system is made possible by the use of blockchain technology, with smart contracts providing immutable storage for verified user data.

Enhancing Academic Success Prediction: An Ensemble Approach

The process of extracting meaning and knowledge from large volumes of data is termed as Data mining. It also refers to the way of inferring information from databases, which can be used in diverse fields including educational domain. Educational data mining plays a key role in finding ways to discover knowledge from data in the education sector. Educational Data Mining has evolved as a significant element in prediction of students’ academic performance. The most important goal of the paper is to analyze and evaluate the engineering students’ performance by applying stacking, an ensemble method in orange tool. Ensemble Stacking combines several base classifier models in order to create one optimal prediction model. Engineering students’ dataset was used to build predictive model using traditional classifiers SVM, Logistic Regression, Naïve Bayes and then stacking technique was implemented. The results showed that the proposed stacking technique obtains a high performance, which has a superior result compared to the other base classifiers techniques. Therefore, conclusion could be reached that the stacking performance is better than that of different algorithms.

Prognostic Evaluation, Prediction and Regimen of Diseases Using SVM, NB and RF Classifiers

Prognostic Evaluation, Prediction and Regimen of Diseases Using SVM, NB and RF Classifiers

Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns

Long extrachromosomal circular DNA (leccDNA) regulates several biological processes such as genomic instability, gene amplification, and oncogenesis. The identification of leccDNA holds significant importance to investigate its potential associations with cancer, autoimmune, cardiovascular, and neurological diseases. In addition, understanding these associations can provide valuable insights about disease mechanisms and potential therapeutic approaches. Conventionally, wet lab-based methods are utilized to identify leccDNA, which are hindered by the need for prior knowledge, and resource-intensive processes, potentially limiting their broader applicability. To empower the process of leccDNA identification across multiple species, the paper in hand presents the very first computational predictor. The proposed iLEC-DNA predictor makes use of SVM classifier along with sequence-derived nucleotide distribution patterns and physicochemical properties-based features. In addition, the study introduces a set of 12 benchmark leccDNA datasets related to three species, namely Homo sapiens (HM), Arabidopsis Thaliana (AT), and Saccharomyces cerevisiae (SC/YS). It performs large-scale experimentation across 12 benchmark datasets under different experimental settings using the proposed predictor, more than 140 baseline predictors, and 858 encoder ensembles. The proposed predictor outperforms baseline predictors and encoder ensembles across diverse leccDNA datasets by producing average performance values of 81.09%, 62.2% and 81.08% in terms of ACC, MCC and AUC-ROC across all the datasets. The source code of the proposed and baseline predictors is available at https://github.com/FAhtisham/Extrachrosmosomal-DNA-Prediction. To facilitate the scientific community, a web application for leccDNA identification is available at https://sds_genetic_analysis.opendfki.de/iLEC_DNA/.

Vehicle Steering Gear Sleeve Defect Detection Method Based on Machine Vision

This project takes the steering gear sleeve as the research object, and uses computer vision technology to detect it. By using this method, the Canny operator is adaptive filtered and integrated with the watershed method, and many features of the image are extracted. SVM classifier was established for three representative defects, such as lack of material, burr and coating. It is used to identify the steering gear sleeve. A test platform is set up to automatically classify and remove defective parts of automobile steering gear sleeve based on UR5 robot. The accuracy and stability of detection are improved by an improved edge segmentation algorithm. The experimental results show that the system can diagnose the fault of the axle sleeve of the automobile steering gear and cooperate with the operating arm to make it possible to automatically detect and remove the fault products.

EEG based automated detection of seizure using machine learning approach and traditional features

The detection of epileptic seizures is key for neurologists to initiate the right treatment at the earliest. However, the traditional methods are dependent on manual diagnosis which are error-prone. Hence, the current article presents an automated machine learning-based approach with competing results to detect seizures and its types using traditional features. Towards this goal, the present paper explores the performance of two fractal parameters namely Higuchi and, Kat’z fractal dimension, along with power spectral density and spectral entropy to detect EEG signals with seizure and non-seizure characteristics. To validate the performance via cross-validation technique, the features are extracted and experimented from three publically available databases such as Bern-Barcelona (focal, non-focal), Khas (preictal, interictal, ictal), and Bonn. Thereafter evaluated with different machine learning algorithms like Random Forest classifier, Ada Boost classifier, Gradient Boosting classifier, Extra Tree classifier, SVM classifier, and Naive Bayes classifier. The experiments showed 100% F1 score and accuracy in classifying focal and non-focal signals for random forest and extra tree classifier methods. To the best of our knowledge, this is the first paper reporting this score for the Bern-Barcelona dataset. The same features are also experimented for the detection of interictal, ictal and preictal scenarios. In the process, 100% accuracy is obtained in classifying interictal and ictal EEG signals, and an accuracy of 94% is attained in classifying interictal-preictal signals. This score is 14% higher than the current state-of-the-art methods using the same database. The features are also used in the Bonn database, where the proposed approach gave an accuracy of 100% same as the state-of-the-art methods.

COVID-19 Diagnosis by Extracting New Features from Lung CT Images Using Fractional Fourier Transform

COVID-19 is a lung disease caused by a coronavirus family virus. Due to its extraordinary prevalence and associated death rates, it has spread quickly to every country in the world. Thus, achieving peaks and outlines and curing different types of relapses is extremely important. Given the worldwide prevalence of coronavirus and the participation of physicians in all countries, information has been gathered regarding the properties of the virus, its diverse types, and the means of analyzing it. Numerous approaches have been used to identify this evolving virus. It is generally considered the most accurate and acceptable method of examining the patient’s lungs and chest through a CT scan. As part of the feature extraction process, a method known as fractional Fourier transform (FrFT) has been applied as one of the time-frequency domain transformations. The proposed method was applied to a database consisting of 2481 CT images. Following the transformation of all images into equal sizes and the removal of non-lung areas, multiple combination windows are used to reduce the number of features extracted from the images. In this paper, the results obtained for KNN and SVM classification have been obtained with accuracy values of 99.84% and 99.90%, respectively.

An adaptive learning paradigm: event detection through a novel dynamic arithmetic optimization-based ensemble SVM for data stream classification

An adaptive learning paradigm: event detection through a novel dynamic arithmetic optimization-based ensemble SVM for data stream classification

Tunable Q-factor wavelet transform based identification of diabetic patients using ECG signals

Diabetes is a chronic health condition that is characterized by increased levels of glucose (sugar) in the blood. It can have harmful effects on different parts of the body, such as the retina of the eyes, skin, nervous system, kidneys, and heart. Diabetes affects the structure of electrocardiogram (ECG) impulses by causing cardiovascular autonomic dysfunction. Multi-resolution analysis of the input ECG signal is utilized in this paper to develop a machine learning-based system for the automated detection of diabetic patients. In the first step, the input ECG signal is decomposed into sub-bands utilizing the tunable Q-factor wavelet transform (TQWT) technique. In the second step, four entropy-based characteristics are evaluated from each SB and elected using the K-W test method. To develop an automatic diabetes detection system, selected features are given as input with 10-fold validation to a SVM classifier using various kernel functions. The 3 rd sub-band of TQWT with the Coarse Gaussian kernel function kernel of the SVM classifier yields a classification accuracy of 91.5%. In the same dataset, the comparative analysis demonstrates that the proposed method outperforms other existing methods.

The Emotion Analysis of Indian Political Tweets using Machine Learning

In this day and age web-based entertainment is a major region for information examination and exploration work. For Feeling Examination, I select Tweeter handle. I use Tweepy for getting to tweeter information. I perform opinion examination on Indian Political information. I got 117545 tweets of 2019 Indian Political race. I use SVM (Backing Vector Machine) Classifier for feeling Examination. Feeling assessment oversees recognizing and portraying evaluations or sentiments conveyed in source message. Electronic diversion is creating an enormous proportion of feeling rich data as tweets, sees, blog sections, etc. Feeling examination of this client made data is especially useful in knowing the appraisal of the gathering. Twitter feeling assessment is problematic stood out from general assessment examination on account of the presence of work related conversation words and erroneous spellings. The most outrageous limitation of characters that are allowed in Twitter is 140. Data base philosophy and AI approach are the two frameworks used for separating suppositions from the text. In this paper, we endeavor to analyze the twitter posts about electronic things like mobiles, workstations, etc using AI approach.

A Robust Approach for Ulcer Classification/Detection in WCE Images

Wireless Capsule Endoscopy (WCE) is a medical diagnostic technique recognized for its minimally invasive and painless nature for the patients. It uses remote imaging techniques to explore various segments of the gastrointestinal (GI) tract, particularly the hard-to-reach small intestine, making it an effective alternative to traditional endoscopic techniques. However, physicians face a significant challenge when it comes to analyzing a large number of endoscopic images due to the effort and time required. It is therefore imperative to implement aided-diagnostic systems capable of automatically detecting suspicious areas for subsequent medical assessment. In this paper, we present a novel approach to identify gastrointestinal tract abnormalities from WCE images, with a particular focus on ulcerated areas. Our approach involves the use of the Median Robust Extended Local Binary Pattern (MRELBP) descriptor, which effectively overcomes the challenges faced when WCE image acquisition, such as variations in illumination and contrast, rotation, and noise. Using machine learning algorithms, we conducted experiments on the extensive Kvasir-Capsule dataset, and subsequently compared our results with recent relevant studies. Noteworthy is the fact that our approach achieved an accuracy of 97.04% with the SVM (RBF) classifier and 96.77% with the RF classifier.

Drones Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks.

The use of drones has recently gained popularity in a diverse range of applications, such as aerial photography, agriculture, search and rescue operations, the entertainment industry, and more. However, misuse of drone technology can potentially lead to military threats, terrorist acts, as well as privacy and safety breaches. This emphasizes the need for effective and fast remote detection of potentially threatening drones. In this study, we propose a novel approach for automatic drone detection utilizing the usage of both radio frequency communication signals and acoustic signals derived from UAV rotor sounds. In particular, we propose the use of classical and deep machine-learning techniques and the fusion of RF and acoustic features for efficient and accurate drone classification. Distinct types of ML-based classifiers have been examined, including CNN- and RNN-based networks and the classical SVM method. The proposed approach has been evaluated with both frequency and audio features using common drone datasets, demonstrating better accuracy than existing state-of-the-art methods, especially in low SNR scenarios. The results presented in this paper show a classification accuracy of approximately 91% at an SNR ratio of -10 dB using the LSTM network and fused features.

Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction.

Seizure prediction remains a challenge, with approximately 30% of patients unresponsive to conventional treatments. Addressing this issue is crucial for improving patients' quality of life, as timely intervention can mitigate the impact of seizures. In this research field, it is critical to identify the preictal interval, the transition from regular brain activity to a seizure. While previous studies have explored various Electroencephalogram (EEG) based methodologies for prediction, few have been clinically applicable. Recent studies have underlined the dynamic nature of EEG data, characterised by data changes with time, known as concept drifts, highlighting the need for automated methods to detect and adapt to these changes. In this study, we investigate the effectiveness of automatic concept drift adaptation methods in seizure prediction. Three patient-specific seizure prediction approaches with a 10-minute prediction horizon are compared: a seizure prediction algorithm incorporating a window adjustment method by optimising performance with Support Vector Machines (Backwards-Landmark Window), a seizure prediction algorithm incorporating a data-batch (seizures) selection method using a logistic regression (Seizure-batch Regression), and a seizure prediction algorithm with a dynamic integration of classifiers (Dynamic Weighted Ensemble). These methods incorporate a retraining process after each seizure and use a combination of univariate linear features and SVM classifiers. The Firing Power was used as a post-processing technique to generate alarms before seizures. These methodologies were compared with a control approach based on the typical machine learning pipeline, considering a group of 37 patients with Temporal Lobe Epilepsy from the EPILEPSIAE database. The best-performing approach (Backwards-Landmark Window) achieved results of 0.75 ± 0.33 for sensitivity and 1.03 ± 1.00 for false positive rate per hour. This new strategy performed above chance for 89% of patients with the surrogate predictor, whereas the control approach only validated 46%.

Investigating Role of Supervised Machine Learning Approach in Classification of Diabetic Patient

Objectives: Healthcare analytics requires classifying diabetic patient datasets for quicker diagnosis and personalized treatment. This study used SVM, Decision Trees, KNN, ANN, and Logistic Regression to predict type 1 and 2 diabetes. Our detailed performance research shows these algorithms' utility in handling diabetic patient data's complexity. Methods: We compare SVM, Decision Trees, KNN, ANN, and Logistic Regression for diabetes patient dataset classification. While each approach has pros and cons, ANN and logistic regression are promising clinical possibilities. Diagnoses, proactive therapies, and diabetes patient outcomes increase with these breakthroughs. Results: SVM has 84.3% accuracy in type 1 and type 2 diabetics. SVM recognized complicated dataset patterns with great accuracy and recall. Decision trees were more interpretable and could record diverse choice limits, with accuracy rates of 86.15% for both types. With 90.6% accuracy, KNN predicted type 1 diabetes well. KNN was ideal for complex datasets because to its greater accuracy and recall using data point similarities. ANN and Logistic Regression had the highest accuracy for type 1 and type 2 diabetes patients at 96.1% and 97%, respectively. Novelty: Layered ANN and logistic regression identified complicated dataset relationships with accuracy, recall, and F1 scores exceeding 0.94. ANN with logistic regression may change diabetes patient classification with unequaled prediction power and accuracy.

Support vector machine with discriminative low‐rank embedding

AbstractSupport vector machine (SVM) is a binary classifier widely used in machine learning. However, neglecting the latent data structure in previous SVM can limit the performance of SVM and its extensions. To address this issue, the authors propose a novel SVM with discriminative low‐rank embedding (LRSVM) that finds a discriminative latent low‐rank subspace more suitable for SVM classification. The extension models of LRSVM are introduced by imposing different orthogonality constraints to prevent computational inaccuracies. A detailed derivation of the authors’ iterative algorithms are given that is essentially for solving the SVM on the low‐rank subspace. Additionally, some theorems and properties of the proposed models are presented by the authors. It is worth mentioning that the subproblems of the proposed algorithms are equivalent to the standard or the weighted linear discriminant analysis (LDA) problems. This indicates that the projection subspaces obtained by the authors’ algorithms are more suitable for SVM classification compared to those from the LDA method. The convergence analysis for the authors proposed algorithms are also provided. Furthermore, the authors conduct experiments on various machine learning data sets to evaluate the algorithms. The experiment results show that the authors’ algorithms perform significantly better than other algorithms, which indicates their superior abilities on classification tasks.

Analysis of TF-IDF and TF-RF Feature Extraction on Product Review Sentiment

Sentiment analysis of product reviews is critical in understanding customer views and satisfaction, especially in the context of e-commerce applications. A marketplace provides channels where users can submit reviews of the products they purchase. However, due to the large number of reviews in a marketplace, analyzing them is no longer feasible to be performed manually. This research proposes a machine learning implementation to perform sentiment analysis on product reviews. In this research, the product review dataset on Shopee marketplace is used for sentiment analysis by comparing TF-IDF and TF-RF feature extraction using the SVM algorithm with stages of dataset, labeling, feature extraction and accuracy results. The importance of the comparison between TF-IDF and TF-RF feature extraction in this research is related to the need to evaluate and determine which feature extraction method is most effective in increasing the accuracy of sentiment analysis. TF-IDF and TF-RF are two methods commonly used in text analysis, and a comparison of their performance can provide deep insight into the effectiveness of each in the context of product sentiment analysis.Thus, through this comparison, this research aims to determine the best approach that can provide the highest accuracy results, so that the results can serve as a guide for further research. Based on the evaluation, the highest accuracy value is achieved at 92.87% by using TF-IDF and SVM classifiers which outperformed previous research.

Evaluating the Performance of Machine Learning Classifier Algorithms for Software Estimation in Software Development Projects

The major aim of this research is to rank the best performing features in order to classify the Software estimation dataset using SVM, Naïve Bayes, Random forest, Decision tree, and KNN classifiers and evaluate their accuracy. Two steps are involved in the classification process: first, the dataset with all attributes is analyzed; second, the information gain methodology is used to rank the attributes, and only the highly rated ones are used to generate the model of classification. Using several folds of cross-validation, we assess the accuracy rank of SVM, Naïve Bayes, Decision tree, Random forest, and KNN classifier