SVM Machine Learning Algorithm Research Articles

Ensure the Proper Wearing of Face Masks Using Machine Learning to Fight Covid-19 Virus

In this pandemic time, wearing face masks is mandatory to all because of the possibility that a person can get COVID-19 virus through their mouth, nose or eyes, which could possibly happen when a person has a direct or close contact to a person with that virus. But, despite the strict implementation, some people disregard the proper wearing of face masks and unaware the risks of possible virus transmission for such negligence. In this paper, it will demonstrate how a Convolutional Neural Network (CNN) can detect if a person is wearing a face mask or not and the additional parameter to support to detect if the face mask is properly worn by a person by considering the facial landmarks thru face recognition using Histogram of Oriented Gradients (HOG) feature descriptor with a linear SVM machine learning algorithm. Two (2) processes are involved in proper wearing of face masks detection. It needs to pass in Face Mask Detection to proceed to the next process which is the Face detection wherein the result of checking should return false to confirm the proper wearing of the face mask of a person.

Ensemble Residual Network Features and Cubic-SVM Based Tomato Leaves Disease Classification System

The need for automatic disease detection applications that can help farmers in the detection of agricultural product diseases is increasing day by day. Convolutional Neural Network (CNN) is a very popular field in image processing, recognition, and classification. It is seen that CNN architectures are used in the determination of agricultural products. In this study, 3 different ResNet architectures of the features automatically are used in the detection of tomato diseases. The most efficient features obtained from these architectures have been obtained by the NCA algorithm again. The features obtained have been trained with the Cubic SVM machine learning algorithm. Tomato leaves belonging to a total of 10 classes have been trained at 80% and a test performance rate of 98.2% has been achieved.

COVID-19 diagnosis on CT images with Bayes optimization-based deep neural networks and machine learning algorithms.

Early diagnosis of COVID-19, the new coronavirus disease, is considered important for the treatment and control of this disease. The diagnosis of COVID-19 is based on two basic approaches of laboratory and chest radiography, and there has been a significant increase in studies performed in recent months by using chest computed tomography (CT) scans and artificial intelligence techniques. Classification of patient CT scans results in a serious loss of radiology professionals' valuable time. Considering the rapid increase in COVID-19 infections, in order to automate the analysis of CT scans and minimize this loss of time, in this paper a new method is proposed using BO (BO)-based MobilNetv2, ResNet-50 models, SVM and kNN machine learning algorithms. In this method, an accuracy of 99.37% was achieved with an average precision of 99.38%, 99.36% recall and 99.37% F-score on datasets containing COVID and non-COVID classes. When we examine the performance results of the proposed method, it is predicted that it can be used as a decision support mechanism with high classification success for the diagnosis of COVID-19 with CT scans.

Development of a computer-aided tool for detection of COVID-19 pneumonia from CXR images using machine learning algorithm

The novel coronavirus (SARS-CoV-2) is spreading rapidly worldwide, and it has become a greater risk for human beings. To curb the community transmission of this virus, rapid detection and identification of the affected people via a quick diagnostic process are necessary. Media studies have shown that most COVID-19 victims endure lung disease. For rapid identification of the affected patient, chest CT scans and X-ray images have been reported to be suitable techniques. However, chest X-ray (CXR) shows more convenience than the CT imaging techniques because it has faster imaging times than CT and is also simple and cost-effective. Literature shows that transfer learning is one of the most successful techniques to analyze chest X-ray images and correctly identify various types of pneumonia. Since SVM has a remarkable aspect that tremendously provides good results using a small data set thus in this study we have used SVM machine learning algorithm to diagnose COVID-19 from chest X-ray images. The image processing tool called RGB and SqueezeNet models were used to get more images to diagnose the available data set. Our adopted model shows an accuracy of 98.8% to detect the COVID-19 affected patient from CXR images. It is expected that our proposed computer-aided detection tool (CAT) will play a key role in reducing the spread of infectious diseases in society through a faster patient screening process.

Breast Cancer Prediction Using Classification Techniques of Machine Learning

Abstract: Data analytics play vital roles in diagnosis and treatment in the health care sector. To enable practitioner decisionmaking, huge volumes of data should be processed with machine learning techniques to produce tools for prediction and classification Breast Cancer reports 1 million cases per year. We have proposed a prediction model, which is specifically designed for prediction of Breast Cancer using Machine learning algorithms Decision tree classifier, Naïve Bayes, SVM and KNearest Neighbour algorithms. The model predicts the type of tumour, the tumour can be benign (noncancerous) or malignant (cancerous) . The model uses supervised learning which is a machine learning concept where we provide dependent and independent columns to machine. It uses classification technique which predicts the type of tumour. Keywords: Cancer, Machine learning, Prediction, Data Visualization, SVM, Naïve Bayes, Classification.

Wavelet Tree ensembles with Machine Learning and its classification

Wavelet trees are compact data structures in computational geometry. In the past, it was used as an essential tool for handling the size of data, data compression, indexing and for many more applications. Machine learning algorithms are used for classification of data and its analysis. In this article, we discuss the scope of machine learning with wavelet trees, wavelet entropy, wavelet matrix and wavelet packets. The study concludes that machine learning applications with wavelet tree is a better choice in terms storage and classification of data. The proposed methodology consists of three techniques for making the data more efficient. It consists of LZW Compression techniques, Wavelet tree, and machine learning algorithm SVM. In this methodology compression with classification process is done for datasets. This proposed methodology performs with machine learningalgorithms in terms of classification of data. In future this method can be used for efficient searching and indexing of large data sets. The classified and compressed dataset perform the indexing with wavelet tree takes less searching time.

Critical Healthcare Assessment using WBAN and SVM

Today good healthcare facilities and awareness of need of good healthcare is increasing in India. But as awareness increases it also strains the current healthcare infrastructure as patient expects more secured treatment round the clock. So there arises a need of remote assessment of patient health all the time using IoT devices. But these devices also need to be monitored by health worker in a hospital. Due to human interaction with theses IoT devices it may give rise to errors as human decisions can be late as a human health worker cannot look at the devices 24X7. So, to remove dependence of human decision-making technologies such as WBAN, cloud and machine learning has to be utilized together to make heath decision of a patient with less human interaction. So, we are designing a project where healthcare of a patient can be monitored extensively using WBAN. In first part of our project, we design a IoT device using Arduino and ESP8266 Wi-Fi module. The sensors connected to the Arduino will be pulse sensor, temperature sensor etc. The sensors will transfer data from patient to a server using ESP8266 and Wi-Fi called as WBAN network. The server will then apply SVM machine learning algorithm on the sensor readings and classify in two categories safe and unsafe. Custom made training dataset will be used to train the SVM. If unsafe readings are found the sensor will send a message to concerned doctor and upload readings to the cloud. The doctor on receiving alert can see the readings on the android app designed for the project and take a decision on the condition of the patient. For the project we are using Google Cloud Platform as our cloud provider which is free for use. Thus, by using our project a doctor can monitor his patient remotely from anywhere and the system will help in making decisions on the behalf of the doctor.

Detecting Malicious URLs using R-CNN and Cloud

In today’s information age as use of websites, mobile apps and all forms of information sharing forms have increased which gave rise to malicious URL forms. These malicious URLs are forwarded and users attention is diverted from the main course for what he is searching to other non-necessary and harmful content, thus wasting a lot of time and money. Theses malicious URLs have given rise to authentication thefts, money thefts and bullying of a user who falls in to a trap set by hackers by accessing these URLs. To resolve and find a solution to this kind of menace there is need to detect and prevent users from accessing these URLs. So, while studying various techniques put forward by various authors in different research papers, we found a few techniques quite interesting and useful. The first is detecting malicious URLs using CNN and GRU. The second is where a text mining technique is proposed using Natural Language Processing (NLP) which can be used for classification. The third is a combination of CNN and NLP. By studying them we came to understand that there should be a combination of both NLP and CNN together to implement a successful malicious URL detection system. So, in our paper we are proposing a fusion of R-CNN, NLP and Cloud together. The main work in our paper is to collect malicious and healthy URL which will be done using internet and multiple sources and combined as one dataset. Thus, we will use Google cloud to create a blacklisted URL database of our own and not depend upon multiple sources internet for them. In our system first we will create a blacklist database on cloud and then apply classification on it using NLP and machine learning algorithm SVM. The second step will be to use same URL dataset to train a R-CNN AI algorithm and get an output in form of malicious identified URLs. Then in the final phase we will compare the final results from SVM and R-CNN and analyse which one is efficient and highs and lows of the technique.

Movie Review System using Sentiment Analysis and Social Networking Platforms (SNPs)

Today online social networking platforms SNPs have become an integral part of or life where we share a lot of information of all the things, we do in life from shopping to movie watching. With ever growing use of SNPs recommendation systems have emerged as a hot trend for applications in e-commerce and digital media. These recommendation systems are useful as well as misguiding. Today digital media use has increased tremendously with increase in internet speeds. But users do not get proper review of a movie and a user is lured to watch a substandard movie which he does not intended to do, thus costing a user time and money. So, there is a need of developing a movie review which will give correct reviews of a digital content like movies so he can only movies which he intends to do. So, we are studying various techniques authored by various authors and create a good movie review system of our own. The first technique we studied and intends to use is movie recommendation system using tweets. The second study is movie recommendation using similarity measures. The third study does find a public shamming using SNP. These techniques are useful and we propose to use some part of each in our new movie review framework by improving the techniques drawbacks. The new framework will be a combination of data from more than one SNP and using natural language processing and machine learning on the data. We are going to use two machine learning algorithms SVM and Naïve Bayes for this purpose. For natural language processing of SNP data, we are going to use OPEN-NLP. We intend to use SNPs such as Twitter and any other movie database like IMDB etc. for data on the movie. The movie will be classified in three classes bad, good and excellent. The results from each algorithm SVM and Naïve bayes will be analyzed for each SNP and try to give user a more accurate movie review by combining all the reviews together and classes accuracy and show overall prediction results with a rating. To get more accurate results for each movie we are going to create a dataset for each movie for demonstration and will not depend on a single combined dataset as keywords for each movie may be different. We are going to combine datasets for each movie from multiple SNPs.

Transaction Fraud Detection (Anomaly detection) using Machine Learning

Now a day’s online shopping crazy thing for peoples. As well as many people uses Online transaction for many purposes. Transaction fraud is growing seriously. Therefore, the study on fraud detection is interesting and significant and we can say necessary. An important way of detecting fraud is to extract the behavior profiles (BPs) of users based on their historical transaction records, and then to verify if an incoming transaction is a fraud or not in view of Their BPs. Also we apply SVM, Adaboost, and Neural Network machine learning algorithm to see which one is giving the best result.

Student Result Prediction in Covid-19 Lockdown using Machine Learning Techniques

Covid-19 virus has moved the world to a static state. This has confused the education field in conducting the end examinations. The school education has declared the results of a few classes as pass without conducting the end examinations. But, this may not be possible for all the education system, especially for final year college student results. For this reason, the prediction of result based on their previous performance is essential. Prediction can be effectively achieved by using machine learning algorithms. Machine learning automatically learns and improves from example and experience. Supervised machine learning algorithms Logistic regression and SVM are used for this work. The data set of 1460 students result of a college is considered for the study. Finally, the trained machine predicts accurately whether the student is eligible to acquire the degree or not and the same is viewed in the college portal.

The Diagnosis of Chronic Liver Disease using Machine Learning Techniques

In the 21st-century, the issue of liver disease has been increasing all over the world. As per the latest survey report, liver disease death toll has been rise approximately 2 million per year worldwide. The overall percentage of death by liver disease is 3.5% worldwide. Chronic Liver disease is also considered to be one of the deadly diseases, so early detection and treatment can recover the disease easily. Due to rapid advancement in Artificial intelligence (AI), like various machine learning algorithms SVM, K-mean clustering, KNN, Random forest, Logistic regression, etc., This will improve the life span of a patient suffering from Chronic Liver Disease (CLD) in early stages. The data can be obtained in a large volume due to the broad exploitation of bar codes for supreme marketable products, the mechanization of various business and government dealings, and the development in the data collection tools. This research work is based on liver disease prediction using machine learning algorithms. Liver disease prediction has various levels of steps involved, pre-processing, feature extraction, and classification. In this s research work, a hybrid classification method is proposed for liver disease prediction. And Datasets are collected from the Kaggle database of Indian liver patient records. The proposed model achieved an accuracy of 77.58%. The proposed technique is implemented in Python with the Spyder tool and results are analyzed in terms of accuracy, precision, and recall.

Detection of Brain Tumor Using Unsupervised Enhanced K-Means, PCA and Supervised SVM Machine Learning Algorithms

The brain tumor is an abnormal cell growth in the human body. To know which type of brain tumor it is and where is the exact location of it. We are using the MR image is a tomographic imaging technique. MRI is based on Nuclear Magnetic Resonance signals. A brain tumor is of two types 1. Benignant 2. malignant. Benignant belongs to I and II grade; this type of tumor is not active cells and have a low-grade tumor. It has a uniform structure. Malignant belongs to III and IV grades, this type of tumor are active cells and have a high grade. It has a non-uniformity structure. The initial phase Input MR image is transformed into a binary image by the Otsu threshold technique. The second step k-means segmentation process is used on binary images. Third step Discrete Wavelet Transform is used on segmented image for extracting the image and it reduces the large dimensionality by using PCA. It identifies the tumor by using Support Vector Machine classification it gives the final output of a brain tumor that normal or abnormal. The proposed paper experimented on the detection of brain tumors using classification algorithms dataset about BraTS dataset and compared with existing methodologies, and it is then proved that superior to existed.

Survey of Tools and Techniques for Sentiment Analysis of Social Networking Data

Social media has rapidly expanded over a period of time and generated a huge repository of content. Sentiment analysis of this data has a vast scope in decision support and attracted many researchers to explore various possibilities for technique enhancement and accuracy improvement. Twitter is one of the social media platforms that are widely explored in the area of sentiment analysis. This paper presents a systematic survey related to Social Networking Sites Sentiment Analysis and mainly focus on Twitter sentiment analysis. The paper explores and identifies the techniques and tools used in a well-structured approach to find out the research gaps and identify future scope in this area of research. The techniques evolved over time to improve the efficiency of classification. Total 55 research papers are included in this survey. The result reflects that Twitter is the most explored social networking site for opinion mining. Naïve Bayes and SVM machine learning algorithms are implemented in maximum researches. As the latest advancements, Stack based ensemble, fuzzy based and neural network based classifiers are also implemented to enhance the efficiency of classification. WEKA, R Studio, Python are mostly used tools by research scholars for implementation. The overall evolution of the research goes through various changes in terms of technologies, tools, social media platforms and data corpus targeted.

Ship Radiated Noise Recognition Technology Based on ML-DS Decision Fusion.

Ship radiated noise is an important information source of underwater acoustic targets, and it is of great significance to the identification and classification of ship targets. However, there are a lot of interference noises in the water, which leads to the reduction of the model recognition rate. Therefore, the recognition results of radiated noise targets are severely affected. This paper proposes a machine learning Dempster–Shafer (ML-DS) decision fusion method. The algorithm combines the recognition results of machine learning and deep learning. It uses evidence-based decision-making theory to realize feature fusion under different neural network classifiers and improve the accuracy of judgment. First, deep learning algorithms are used to classify two-dimensional spectrogram features and one-dimensional amplitude features extracted from CNN and LSTM networks. The machine learning algorithm SVM is used to classify the chromaticity characteristics of radiated noise. Then, according to the classification results of different classifiers, a basic probability assignment model (BPA) was designed to fuse the recognition results of the classifiers. Finally, according to the classification characteristics of machine learning and deep learning, combined with the decision-making of D-S evidence theory of different times, the decision-making fusion of radiated noise is realized. The results of the experiment show that the two fusions of deep learning combined with one fusion of machine learning can significantly improve the recognition results of low signal-to-noise ratio (SNR) datasets. The lowest fusion recognition result can reach 76.01%, and the average fusion recognition rate can reach 94.92%. Compared with the traditional single feature recognition algorithm, the recognition accuracy is greatly improved. Compared with the traditional one-step fusion algorithm, it can effectively integrate the recognition results of heterogeneous data and heterogeneous networks. The identification method based on ML-DS proposed in this paper can be applied in the field of ship radiated noise identification.

FPGA-based Hardware Acceleration for SVM Machine Learning Algorithm

object recognition algorithms are both large consumers of computing power and memory which affects the quality and performance especially when it comes to large image datasets, in this paper we propose an algorithm for fruit/plant recognition that we will accelerate it using the PYNQ Board to evaluate the execution time and the accuracy of the classifier.

Machine learning approaches for sex estimation using cranial measurements.

The aim of the present study is to apply support vector machines (SVM) and artificial neural network (ANN) as sex classifiers and to generate useful classification models for sex estimation based on cranial measurements. Besides, the performance of the generated sub-symbolic machine learning models is compared with models developed through logistic regression (LR). The study was carried out on computed tomography images of 393 Bulgarian adults (169 males and 224 females). The three-dimensional coordinates of 47 landmarks were acquired and used for calculation of the cranial measurements. A total of 64 measurements (linear distances, angles, triangle areas and heights) and 22 indices were calculated. Two datasets were assembled including the linear measurements only and all measurements and index, respectively. An additional third dataset comprising all possible interlandmark distances between the landmarks was constructed. Two machine learning algorithms-SVM and ANN and a traditional statistical analysis LR-were applied to generate models for sex estimation. In addition, two advanced attribute selection techniques (Weka BestFirst and Weka GeneticSearch) were used. The classification accuracy of the models was evaluated by means of 10 × 10-fold cross-validation procedure. All three methods achieved accuracy results higher than 95%. The best accuracy (96.1 ± 0.5%) was obtained by SVM and it was statistically significantly higher than the best results achieved by ANN and LR. SVM and ANN reached higher accuracy by training on the full datasets than the selection datasets, except for the sample described by the interlandmark distances, where the reduction of attributes by the GeneticSearch algorithm improved the accuracy.

A comparison study of SVM and NB machine learning algorithms performance on training dataset

Support Vector Machine and Naive Bayes are popular classification algorithms in PDF malware detection, Spam filtering and scientific community training datasets. These algorithms incorporated classifications into the training datasets which they affected with the type of causative and evasion attack. The adversaries are insect the training dataset by injecting malicious sample data. This infected training datasets are used in the ML algorithms without knowing that they are infected for research purpose. Intelligent attackers mislead the SVM and NB learning algorithms functional task by modifying the training dataset. This may cause the security problems in the training dataset. To develop security mechanism, use to cope the attack on training dataset and avoid to decreases ML algorithms performance. This paper shows that the SVM and NB accuracy reduces dramatically when they used infected training dataset. The proposed defence method Rand Check used to prevent the trusted training dataset from causative and evasion attacks.

A Hybrid Machine Learning Model with Cost-function Based Outlier Removal and Its Application on Credit Rating

This paper establishes a methodology to build hybrid machine learning models, aiming to combine the power of different machine learning algorithms on different types of features and hypothesis. A generic cost-based outlier removal algorithm is introduced as a step of pre-process of training data. Using this methodology, an experimental hybrid machine learning model is implemented for a crediting problem, which combines three types of machine learning algorithms SVM, DT and LR. Different metrics of training time, recall, precision, F1 score, and AUC are evaluated and compared with the results from single model of single SVM, DT, and LR. The new hybrid models in general shows improvement in performance on multiple metrics. this study further demonstrated that one learning model may work well on certain type of variables but not as effectively on other variables. Well defined procedures can be followed to create hybrid models to combine the power of different learning models, which are superior in dealing with dataset with complicate feature selection. This methodology can be explored with other algorithms and applications.

Incorporating Research Reports and Market Sentiment for Stock Excess Return Prediction: A Case of Mainland China

The prediction of stock excess returns is an important research topic for quantitative trading, and stock price prediction based on machine learning is receiving more and more attention. This article takes the data of Chinese A-shares from July 2014 to September 2017 as the research object, and proposes a method of stock excess return forecasting that combines research reports and investor sentiment. The proposed method measures individual stocks released by analysts, separates the two indicators of research report attention and rating sentiment, calculates investor sentiment based on external market factors, and uses the LSTM model to represent the time series characteristics of stocks. The results show that (1) the accuracy and F1 evaluation indicators are used, and the proposed algorithm is better than the benchmark algorithm. (2) The performance of deep learning LSTM algorithm is better than traditional machine learning algorithm SVM. (3) Investor sentiment as the initial hidden state of the model can improve the accuracy of the algorithm. (4) The attention of the split research report takes the two indicators of investor sentiment and price as the input of the model, which can effectively improve the performance of the model.