Use Of Support Vector Machines Research Articles

Literature Survey on Revolutionizing Fake Currency Detection: CNN-Based Approach for Indian Rupee Notes

The proliferation of counterfeit currency poses a significant challenge in various economies, including India. To address this issue, several studies have proposed innovative image processing and machine learning techniques for detecting counterfeit coins and banknotes. Leveraging digital image processing, these studies aim to enhance the security measures against counterfeit currency through accurate and efficient recognition systems. Techniques such as preprocessing, segmentation, feature extraction, and clustering are employed to identify fraudulent currency. The use of Support Vector Machines (SVM), k-means clustering, and Convolutional Neural Networks (CNN) facilitates the recognition and classification of genuine and counterfeit currency. Moreover, these studies explore the application of spatial coding, component-based recognition, and deep learning algorithms to improve the accuracy and robustness of counterfeit detection systems. By developing real-time recognition systems capable of identifying counterfeit currency, these research efforts contribute to combating financial fraud and safeguarding economic integrity.

Analyzing Cancer Prognosis with Advanced Machine Learning Models

This study investigates the use of Support Vector Machines (SVM) and other machine learning algorithms for predicting the prognosis of lung, breast, and cervical cancer patients. The research evaluates the predictive accuracy, influential features, algorithm performance, and model generalization across different cancer types. Using publicly available datasets, the study highlights SVM's exceptional accuracy in prognosis prediction, with findings indicating its superiority over alternative algorithms. Notably, it identifies the key clinical, molecular, and pathological features that significantly impact predictive accuracy. The study also discusses the clinical applicability of these models, emphasizing their potential to aid healthcare professionals in making more informed treatment decisions. Acknowledging limitations, including data availability and computational resources, the study suggests future directions, encouraging the exploration of additional techniques, diverse datasets, and real-world clinical trials to validate the model’s effectiveness. Keywords Cancer, Medical Diagnosis, Markers, Learning, Patients, Machine Learning, Support Vector Machine

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.

Impact of Velocity of Detonation and Charge per Bank Cubic Meters on Flyrock Throw Prediction Using Support Vector Machine

One of the ambient effects of production blasting is flyrock. To effectively manage flyrock throw distance in mining, there is the necessity to successfully envisage blasting output without sacrificing the hazardous impact of flyrock which may result in fatality and operational shutdown. For flyrock throw distance prediction, velocity of detonation (VOD) and charge per bank cubic meter (CPBCM) are not usually included. This paper focuses on the use of support vector machine (SVM) regression to ascertain the impact of VOD and CPBCM on flyrock throw predictions. The machine learning models were linear support vector machine (LSVM), quadratic Gaussian support vector machine (QGSVM), fine Gaussian support vector machine (FGSVM), medium Gaussian support vector machine (MGSVM), and cubic Gaussian support vector machine (CGSVM). The outcome indicates that FGSVM was the most sensitive with a 4% improvement when VOD and CPBCM were included. As a result, the LSVM model provides a suitable AI competitive alternative tool for flyrock throw prediction in mining operations by incorporating VOD and CPBCM.

Deep Learning-based Glaucoma Detection Using CNN and Digital Fundus Images: A Promising Approach for Precise Diagnosis.

Glaucoma is a significant cause of irreversible blindness worldwide, with symptoms often going undetected until the patient's visual field starts shrinking. To develop an AI-based glaucoma detection method to reduce glaucoma-related blindness and offer more precise diagnosis. Discusses various methods and technologies, including Heidelberg Retinal Tomography (HRT), Optical Coherence Tomography (OCT), and Fundus Photography, for obtaining relevant information about the presence of glaucoma in a patient. Additionally, it mentions the use of Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) for glaucoma detection. There are many limitations for existing methods as; Asymptomatic Progression, reliance on subjective feedback, multiple tests required, late detection, limited availability of preventive tests, influence of external factors. Findings reveal promising outcomes in terms of glaucoma detection accuracy, particularly in the analysis of the RIM-ONE-r3 dataset. By scrutinizing 20 images from the Healthy, Glaucoma, and Suspects categories through fundus image recognition, our developed AI model consistently achieved high diagnostic accuracy rates. Conclusion Our study suggests that further enhancements in glaucoma detection accuracy are attainable by augmenting the dataset with additional labeled images. We emphasize the significance of considering various application parameters when discussing the integration of computer-aided decision/management systems into healthcare frameworks.

Malware Detection in Files and URL’s using Machine Learning

The use of Support Vector Machine (SVM) to machine learning-based malware detection is the main goal of this study. Since malware threats are always changing, more advanced detection techniques are required. With the help of SVM, a potent classification algorithm, our project is able to precisely identify malware based on a range of attributes. By developing a strong and effective solution that efficiently identifies and reduces malware threats, the aim is to improve cybersecurity and support continuous efforts to protect computer systems and networks. Malware has been posing a serious threat to embedded systems in recent times, and traditional software solutions like antivirus and patching haven't been able to keep up with the sophisticated and constantly changing bad programmes. In this work, we present guardol, a hardware-enhanced architecture designed to identify online malware. Guardol is a hybrid technique that combines FPGA and CPU. Our method seeks to capture malware's malevolent behaviour, or high-level semantics. In order to do this, we first suggest the frequency-centric model for building features out of benign samples and known malware's system call patterns. We next create a machine learning strategy in FPGA to train a classifier with these features, utilising a multilayer perceptron. The trained classifier is applied at runtime to categorise the unknown data as benign or malicious, with In this day and age of advanced technology, the internet has been embraced by most people. And with it has come an increased risk of malevolent cyberattacks by cybercriminals. The attacks are completed.

Use of Support Vector Machine(SVM) in Prediction of Success of Wart Treatment Methods

Abstract: Warts are common noncancerous benign tumors caused by the Human Papilloma Virus. They can be treated using various methods, including cryotherapy and immunotherapy. However, the success rates of these treatment methods are not consistent and can vary from patient to patient. To address this issue, we have developed a reliable machine learning model that can accurately predict the success of immunotherapy and cryotherapy for individual patients based on their demographic and clinical characteristics. By utilizing a dataset of 180 patients who received either immunotherapy or cryotherapy for their warts, we employed a support vector machine classifier with a radial basis function kernel for the immunotherapy treatment method (Nugroho et al., 2018) . This model takes into account factors such as sex, age, time, number of warts, type, area, and response to treatment to make predictions about the likelihood of treatment success. Through this model, healthcare professionals can have a better understanding of which treatment method is more likely to be effective for a specific patient. The accuracy of our machine learning model for predicting the success of immunotherapy and cryotherapy for warts is expected to be high, as previous studies using similar datasets have reported classification accuracies of up to 90 (Rahman et al., 2020). Our model builds upon previous research in the field, such as the fuzzy logic rule-based method developed by Khozeimeh et al and the AdaBoost with classification and regression tree and random forest algorithms employed by Putra et al. Using our machine learning model, healthcare professionals can make more informed decisions about the treatment approach for individual patients with warts, increasing the likelihood of successful outcomes and improving overall patient care. In conclusion, our machine learning model offers a promising approach to predict the success of immunotherapy and cryotherapy treatments for warts.

Hyperspectral discrimination of ginseng variety and age from Changbai Mountain area

BackgroundThe efficacy and market value of Panax ginseng Meyer are significantly influenced by its diversity and age. Traditional identification methods are prone to subjective biases and necessitate the use of destructive sample processing, leading to the loss and wastage of ginseng. Consequently, non-destructive in-situ identification has emerged as a crucial subject of interest for both researchers and the ginseng industry. The advancement of technology and the expansion of research have introduced spectral technology and image processing technology as novel approaches and concepts for non-destructive in-situ identification. MethodsHyperspectral imaging (HSI) is a methodology that combines conventional spectroscopy and imaging to acquire comprehensive spectral and spatial data from various samples. In this study, we investigated the use of Support Vector Machine (SVM) and Spectral Angle Mapper (SAM) classifier algorithms, in conjunction with HSI classification technology, for quasi-Artificial Intelligence (quasi-AI) ginseng identification. To enhance the hyperspectral images prior to SVM classification, we compared the efficacy of Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). ResultsThe classification of ginseng based on age was accomplished through the utilization of Radial Basis Function (RBF) kernel SVM and SAM algorithm, which was trained on feature enhanced images. The classification of WMG, MCG, and GG is primarily based on age, with the endmember spectrum serving as the foundation for SAM and SVM. ConclusionThe “endmember spectrum set” derived from the classification outcomes can serve as the “mutation point” for identifying ginseng of different ages.

Mobile Botnet Detection A Machine Learning Approach using SVM

The security and privacy of smartphone users are seriously threatened by mobile botnets, which allow malevolent actors to carry out a variety of illegal actions, such as DDoS attacks, data theft, and resource exploitation. These mobile botnets are becoming more and more sophisticated, making it difficult to detect them with conventional signature-based and heuristic methods. This study proposes a machine learning-based method for mobile botnet detection that makes use of Support Vector Machines (SVM).The study focuses on behavioural feature extraction from mobile device system-level data and network traffic. The SVM model is used for classification once feature selection techniques have been used to select the most pertinent and discriminative attributes. The SVM model, making use of its capacity to manage nonlinear classification and high-dimensional data.Tests carried out on a variety of network traffic and system behaviour datasets gathered from mobile devices show encouraging outcomes for the detection of botnets. Compared to conventional detection techniques, the SVM classifier outperforms them in identifying mobile botnet activities with a high degree of accuracy, precision, and recall.The suggested SVM-based method improves mobile device security by offering a flexible and successful mobile botnet detection solution. The results of this study open the door to the development of strong and durable mobile security systems by providing insights into the proactive identification and mitigation of mobile botnet threats through the use of machine learning techniques

Classification of Monofloral Honeys by Measuring a Low-Cost Electronic Nose Prototype Based on Resistive Metal Oxide Sensors

In this article a case study of characterisation of type of honey based on floral origin is presented. It is intended to discriminate Iberian honeys from local beekeepers located in the Community of Madrid (Spain), by means of a low-cost electronic nose prototype, composed of a matrix of nonspecific resistive sensors of MQ-type metal oxides. The measurements of the honeys made with an electronic nose prototype were contrasted with physicochemical analyzes and pollen content. The experiment was carried out in two trials. A first preliminary study in which six samples of honey from different sources were used (three Blueweed, one rapeseed, one lavender and one commercial honey) and in which eight repetitions were made for each of the six samples analyzed. Due to the small sample size, conclusive results were not obtained, although the sensors did show a clear response in those that presented a higher pollen content, above 57%, however, the honey samples that reflected pollen values lower than 50% they showed no perceptible reaction on the sensors. In the second study, in which the sample size was increased to a total of 16 samples (four lavender honeys, four oak honeys, four rosemary honeys, and four chestnut honeys), a total of 10 repetitions per sample were carried out with a total of repetitions out of 160. These last data were analyzed with the principal component technique (PCA), the results of which were inconclusive. However, when applying the data analysis through the use of Support Vector Machines (SVM), it is possible to obtain a model with 87.5% accuracy in the classification. In this case, the Lavender and Chestnut honeys were the ones that achieved a precision of 90% and 100% respectively.

Investigating the Prediction of Breast Cancer Diagnosis by Use of Support Vector Machines

This study examines the use of support vector machine (SVM) learning algorithms in predictive analytics models for the detection of breast cancer. This study uses the breast cancer Wisconsin dataset and evaluates the model's performance using measures including accuracy, F1-score, precision, and recall. Comparisons are made between the SVM model's performance and those of alternative classification techniques including logistic regression, decision trees, and random forests. The findings demonstrate the usefulness of utilising predictive analytics models, notably the SVM algorithm, for the detection of breast cancer. The SVM model demonstrated significant predictive effectiveness and accuracy, making it a viable choice of tool for clinicians in the early identification and diagnosis of breast cancer.

Optimization of high alumina slag practice in blast furnace ironmaking: an industrial approach. Part 2: Data-driven aspects

ABSTRACT The prediction of transport and thermodynamic properties of the blast furnace slag is an experimentally tedious job to accomplish as seen in part-1 of this work. Literature studies have shown that the use of machine learning in the determination of the properties as a function of composition is an effective technique for glassy slags. However, the application of machine learning and data science techniques in the prediction of high alumina slag properties has not been studied extensively so far. In this paper, the use of Support Vector Machine (SVM) and ExtraTrees Regressor have been done to predict the viscosity, liquidus temperature, and other thermodynamic properties of blast furnace type slag in the high alumina regime, i.e. Al2O3 varying from 18 to 22 wt-%. The minimization of detrimental effects of high alumina slag has been studied by varying the MgO content and CaO/SiO2 ratio in the range of 8–12 wt-% and 0.8–1.2, respectively. The accuracy of models has been tuned to be fairly high and the results of the prediction have been discussed with possible solutions to operate under the high alumina regime of blast furnace slag.

Automatic Classification of Music Genres Using the Deep Learning Approach

Abstract: Music is divided into arbitrary groups known as genres. Music genre classification is a challenging task due to the subjective and ambiguous nature of musical genres. The existing systems for music genre classification suffer from low accuracy and poor generalization of new data. Therefore, there is a need to develop a robust and accurate machine-learning model that can overcome these challenges and classify music audio files into different genres with high accuracy. The main aim of the Music genre classification project is to develop a user-friendly application that accepts audio files as input and classifies the audio file into a particular category of sound to which they belong (to predict its genre) using machine learning models. This application automates the process to reduce manual error and time. It will take an audio file as input and categorizes each file into a particular category like audio belonging to Disco, hip-hop, etc. The final classification is obtained from the collection of individual data. This machine learning model makes use of Support Vector Machine(SVM) and Logistic Regression models. Both models will be integrated into a website to make the project easily accessible.

Detection of Hate Posts and Tweets in the Social Network Society

Hate speech is a prevalent issue on social media platforms that has recently been a cause for concern due to its detrimental effects on individuals and society. The development of effective hate speech detection procedures and algorithms is crucial to address this issue. However, the existing natural language processing (NLP) algorithms and machine learning models face several challenges in accurately identifying and categorizing hate speech. These challenges include the ambiguity and variability of language use, the lack of standardized definitions and guidelines for hate speech, and the rapid evolution of new and creative forms of hate speech. In this paper, we propose a technique that leverages classic machine learning and deep learning methods to locate and categorize hate speech in social media. Our approach involves the use of Support Vector Machines (SVM) and Long ShortTerm Memory (LSTM) networks for classification. We evaluate the performance of our model on a hate speech dataset and compare it with a deep learning-based model. Our results show that the SVM model outperforms the deep learning-based model in accuracy and efficiency. Our approach offers a promising solution to the challenges posed by hate speech detection on social media and contributes towards building a safer and more welcoming online community. Keywords—Hate speech, Social Networks, NLP, LSTM, Transformers

Electric vehicle charging state predictions through hybrid deep learning: A review

This review paper discusses the application of hybrid deep learning techniques for predicting the charging state of electric vehicles. The paper highlights the importance of accurate predictions for the efficient management of electric vehicle charging stations. The review focuses on the use of recursive neural networks (RNNs) and the gated recurrent unit (GRU) framework in hybrid deep learning models, which have shown promising results in previous studies. In addition to hybrid deep learning, the paper also examines the use of support vector machines (SVMs) and artificial neural networks (ANNs) in charging state prediction. The strengths and weaknesses of these different approaches are analyzed and compared. The paper concludes that hybrid deep learning models, particularly those using RNNs and GRUs, are a promising approach for accurately predicting electric vehicle charging states. The paper also suggests potential areas for future research to further improve the accuracy and efficiency of charging state predictions.

Security Enhancement in Surveillance Cloud Using Machine Learning Techniques

Most industries are now switching from traditional modes to cloud environments and cloud-based services. It is essential to create a secure environment for the cloud space in order to provide consumers with a safe and protected environment for cloud-based transactions. Here, we discuss the suggested approaches for creating a reliable and safe environment for a surveillance cloud. When assessing the security of vital locations, surveillance data is crucial. We are implementing machine learning methods to improve cloud security to more precisely classify image pixels, we make use of Support Vector Machines (SVM) and Fuzzy C-means Clustering (FCM). We also extend the conventional two-tiered design by adding a third level, the CloudSec module, to lower the risk of potential disclosure of surveillance data.In our work we evaluates how well our proposed model (FCM-SVM) performed against contemporary models like ANN, KNN, SVD, and Naive Bayes. Comparing our model to other cutting-edge models, we found that it performed better, with an average accuracy of 94.4%.

Effective Handling of Missing Values in Datasets for Classification Using Machine Learning Methods

The existence of missing values reduces the amount of knowledge learned by the machine learning models in the training stage thus affecting the classification accuracy negatively. To address this challenge, we introduce the use of Support Vector Machine (SVM) regression for imputing the missing values. Additionally, we propose a two-level classification process to reduce the number of false classifications. Our evaluation of the proposed method was conducted using the PIMA Indian dataset for diabetes classification. We compared the performance of five different machine learning models: Naive Bayes (NB), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), Random Forest (RF), and Linear Regression (LR). The results of our experiments show that the SVM classifier achieved the highest accuracy of 94.89%. The RF classifier had the highest precision (98.80%) and the SVM classifier had the highest recall (85.48%). The NB model had the highest F1-Score (95.59%). Our proposed method provides a promising solution for detecting diabetes at an early stage by addressing the issue of missing values in the dataset. Our results show that the use of SVM regression and a two-level classification process can notably improve the performance of machine learning models for diabetes classification. This work provides a valuable contribution to the field of diabetes research and highlights the importance of addressing missing values in machine learning applications.

Comprehensibility and Automation: Plain Language in the Era of Digitalization

Abstract The current article briefly presents a pilot machine-learning experiment on the classification of official texts addressed to lay readers with the use of support vector machine as a baseline and fastText models. For this purpose, a hand-crafted corpus was used, created by the experts of the National Tax and Customs Administration of Hungary under the office’s Public Accessibility Programme. The corpus contained sentences that were paraphrased or completely rewritten by the experts to make them more readable for lay people, as well their original counter pairs. The aim was to automatically distinguish between these two classes by using supervised machine-learning algorithms. If successful, such a machine-learning-based model could be used to draw the attention of experts involved in making the texts of official bodies more comprehensible to the average reader to the potentially problematic points of a text. Therefore, the process of rephrasing such texts could be sped up drastically. Such a rephrasing (considering, above all, the needs of the average reader) can improve the overall comprehensibility of official (mostly legal) texts, and therefore supports access to justice, the transparency of governmental organizations and, most importantly, improves the rule of law in a given country.

Use of support vector machine and cellular automata methods to evaluate impact of irrigation project on LULC.

Land use and land cover (LULC) both define the earth's surface both anthropogenically and naturally. It helps maintain global balance but changes in land use create inequality. The LULC modification adversely affects physical parameters such as infiltration, groundwater recharge, surface runoff, ground temperature, and air quality. It is high time to monitor land use changes globally. Remote sensing and GIS techniques help to monitor these changes with a low budget and time. Various types of LULC classifiers have been invented to classify the LULC types. Maximum likelihood is a popular LULC classifier, but nowadays, support vector machine (SVM) classifier is gaining popularity because it provides a more accurate LULC than the maximum likelihood classifier. Therefore, in this study, the SVM classification technique has been applied to produce good accuracy LULC maps. Using the SVM classifier, six LULC maps are produced from 1995 to 2020 for the Shali reservoir area in India which is a medium irrigation project irrigating ~ 3211 hectares of land per year. It plays an important role in the agricultural production of the region by providing irrigation water in monsoon and post-monsoon seasons. The impact of LULC change on the environment is also studied. The LULC forecast maps are also created using the cellular automata (CA) model and MOLUSCE plugin. Kappa coefficient and validation methods are used to validate the LULC and simulated maps. Both maps produce high accuracy with a kappa coefficient of 0.9. Secondary data, collected from the governmental gazette, such as population, crop production, and water level is also used to justify the results. The simulated map shows that 4% of agricultural land and built-up area may increase from 2020 to 2030. Overall, it has been proven that the SVM and CA models can produce accurate classified results.

Application of support vector machine algorithm for early differential diagnosis of prostate cancer

Prostate cancer (PCa) symptoms are commonly confused with benign prostate hyperplasia (BPH), particularly in the early stages due to similarities between symptoms, and in some instances, underdiagnoses. Clinical methods have been utilized to diagnose PCa; however, at the full-blown stage, clinical methods usually present high risks of complicated side effects. Therefore, we proposed the use of support vector machine for early differential diagnosis of PCa (SVM-PCa-EDD). SVM was used to classify persons with and without PCa. We used the PCa dataset from the Kaggle Healthcare repository to develop and validate SVM model for classification. The PCa dataset consisted of 250 features and one class of features. Attributes considered in this study were age, body mass index (BMI), race, family history, obesity, trouble urinating, urine stream force, blood in semen, bone pain, and erectile dysfunction. The SVM-PCa-EDD was used for preprocessing the PCa dataset, specifically dealing with class imbalance, and for dimensionality reduction. After eliminating class imbalance, the area under the receiver operating characteristic (ROC) curve (AUC) of the logistic regression (LR) model trained with the downsampled dataset was 58.4%, whereas that of the AUC-ROC of LR trained with the class imbalance dataset was 54.3%. The SVM-PCa-EDD achieved 90% accuracy, 80% sensitivity, and 80% specificity. The validation of SVM-PCa-EDD using random forest and LR showed that SVM-PCa-EDD performed better in early differential diagnosis of PCa. The proposed model can assist medical experts in early diagnosis of PCa, particularly in resource-constrained healthcare settings and making further recommendations for PCa testing and treatment.