SVM Learning Algorithm Research Articles

Human-Robot Interaction (HRI) using Machine Learning (ML): a Survey and Taxonomy

Human-robot interaction (HRI) which has become the fundamental need of the hour is born out of the necessity for studying the relation between humans and robots. This cutting-edge discipline is a multidisciplinary field that draws from computer science, robotics along with human-computer interaction and psychology. It focuses mainly on designing and programming machines, best known as automated machines or robots, which are used by humans to perform specific tasks in a timely manner and with higher quality. The key problem in HRI is to realize, shape, tune, and modelling the humanrobot interaction in a flexible manner. For the sake of reflecting and shaping the interactions between humans and robots, HRI is based on the fusion of the two areas: the people's behaviour and attitudes towards using these robots, as well as the physical, technological, and interactive features of the robots. As the robot has tightly integrated from a set of sensors that collect the data from the environment and send them to the processor which in turn translates the collected data into information that can be used in the robot itself, machine learning (ML) is a well-known research area that focuses on the building of well-stocked knowledge systems by using supervised and unsupervised algorithms. From a conceptual standpoint, this research survey and taxonomy pursue to present an in-depth evaluation and review of the most current state-of-the-art papers that have already been published so far and encompass the use of ML algorithms in the HRI field. Thus, a total of 30 research papers devoted to HRI were examined and analysed to give the most ML algorithms implemented in the field of HRI. Evidently, this study shows that the Neural and Reinforcement learning machine algorithms that are used mostly in the recent studies that have an interest in HRI use a machine learning algorithm with a supervised technique in a physical application. There are many challenges facing HRI using ML algorithms, which reduce the use of other ML algorithms such as deep and SVM learning algorithm. Unfortunately, these challenges limit use in social and mobile applications.

Rank Matrix Approach for Endometriosis: Integrating Data and Constructing Diagnostic Models

Background: Endometriosis is a debilitating gynecological disorder characterized by chronic pain, infertility, and the growth of endometrial tissue outside the uterus. Accurate and early detection of this condition is crucial for effective management and treatment. Methods: We developed a gene rank matrix-based model to integrate endometriosis cohorts across multiple platforms. After removing batch effects, we identified 83 genes associated with endometriosis and further refined a diagnostic model using 11 of these genes. The model was trained on two platforms and validated on two others using SVM, Random Forest, Logistic Regression, and gradient-boosting machine learning algorithms. Results: The integration via the gene rank matrix effectively mitigated batch effects. Utilizing a gradient boosting classifier with a subset of 11 genes, the model demonstrated commendable diagnostic efficacy, achieving an Area Under the Curve (AUC) of 0.77, an accuracy of 0.72, and an F1 score of 0.72 for the training dataset. When subjected to validation, the model maintained its performance, yielding an AUC of 0.769, an accuracy of 0.719, and an F1 score of 0.732. These 11 genes were found to be associated with immunosuppression. Conclusion: Our approach to integrating gene rank matrices effectively consolidates endometriosis data across diverse platforms. The diagnostic model, harnessing the predictive power of 11 specific genes, surpasses alternative models, thereby offering promising prospects for aiding clinical diagnosis of endometriosis. Further validation is imperative to elucidate the functional significance of these 11 genes. Our study underscores the potential of data integration coupled with machine learning techniques in advancing the diagnosis of intricate diseases, such as endometriosis.

Leveraging ML for Anomaly Detection in Healthcare Data Warehouses

The rapid emergence of digitalisation leads to unprecedented growth in the generation of the healthcare sector-particularly EHRs and medical equipment data. This extended the way for challenges for integrity in managing data and anomaly detection, including fraudulent transactions, medication errors, and many more system failures. Modern healthcare data poses a challenge to traditional methods of anomaly detection due to high and complex dimensionality. Machine learning provides a strong solution, using algorithms such as Gaussian Mixture Models, One-Class SVM and deep learning algorithms such as Autoencoders, and Recurrent Neural Networks in the detection of anomalies in healthcare data warehouse settings [1]. This study reports how ML can help advance care for patients, enable the validity of the data and reduce costs through real-time monitoring, fraud detection, and early detection of diseases. Applying anomaly detection through ML would most likely bring better operational performance, patient safety, and decision-making in health care for organizations as issues of poor data quality, lack of interpretability of models, and real-time detection would be addressed [2].

Contextually Propagated Term Weight based Approach for Author Profiling: Gender, Age and Language Variety Prediction

Author profiling creates characterization of an author based on attributes such as age, gender, language, dialect region variety, personality and so on. In recent years it has garnered significant attention for its varies applications across forensic linguistics, marketing, cybersecurity and social media analytics. Most of the research focused on stylistic, content based and term weight measures based feature representations. We observed that context semantics is not considered in the feature representation. In this propose contextually propagated term weight measures for feature representation. We implemented SVM, Random Forest and XG Boost machine learning algorithms on those feature representations. The results demonstrated that the proposed contextually propagated term weight with inverse category frequency outperformed the existing methods.

Identification of signature gene set as highly accurate determination of metabolic dysfunction-associated steatotic liver disease progression.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression. Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD. After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort. We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.

A novel brain EEG clustering based on Minkowski distance to improve intelligent epilepsy diagnosis

This paper introduces a novel clustering approach based on Minkowski's mathematical similarity to improve EEG feature selection for classification and have efficient Particle Swarm Optimization (PSO) in the context of machine learning. Given the intricacy of high-dimensional medical datasets, feature selection plays a critical role in preventing disease and promoting public health. By employing Minkowski clustering, the objective is to group dataset records into two clusters exhibiting high feature coherence, thereby improving accuracy by applying optimization techniques like PSO to select the most optimal features. Furthermore, the proposed model can be extended to intelligent datasets, including EEG and others. As fewer features are needed for precise categorization, intelligent feature selection is an advanced step of machine learning. This paper investigates the key factors influencing feature selection in the EEG Bonn University dataset. The proposed system is compared against various optimization and feature selection methods, demonstrating superior performance in analyzing and classifying EEG signals based on accuracy measures. The experimental results have confirmed the effectiveness of the suggested model as a valuable tool for EEG data classification, achieving up to 100% accuracy. The outcomes of this research have the potential to benefit medical experts in related specialties by streamlining the process of identifying and diagnosing brain disorders. Technically, the machine learning algorithms RF, KNN, SVM, NB, and DT are employed to classify the selected features.

Demographical Based Sentiment Analysis for Detection of Hate Speech Tweets for Low Resource Language

Advancement in IT and communication technology provides the opportunity for social media users to communicate their ideas and thoughts across the globe within no time as well big data promulgated in a result of the communication process itself has immense challenges. Recently, the provision of freedom of speech has witnessed immense promulgation of offensive and hate speech content on the internet aimed the basic human rights violation. The detection of abusive content on social media for rich resource language has become a hot area for researchers in the recent past. However, low-resource languages are underprivileged due to the non-availability of large corpus and its complexity to understand. The proposed methodology mainly has two parts. One is to detect abusive content and the other is to have a demographical analysis of the Indigenously developed dataset. The process starts with the development of a unique unlabeled Urdu dataset of 0.2 M from Twitter through a web scrapper tool named snscraper. The dataset is collected against the 36 districts of Punjab from Pakistan and from the duration 2018- Apr 2022. The dataset is labeled into three target classes Neutral, Offensive, and Hate Speech. After data cleaning, the feature extraction process is achieved with the help of traditional techniques such as Bow and tf-idf with the combination of word and char n-gram and word embedding word2Vec. The dataset is trained on both machine learning algorithms SVM and Logistic regression and deep learning techniques Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). The best F score achieved through LSTM on this dataset is 64 and accuracy is 93 through CNN algorithms. A Choropleth map is used for visualization of the dataset distributed among 36 districts of Punjab and a time series plot for time analysis covers five years duration from 2018-Apr to 22.

Hyperspectral reflectance and machine learning to monitor legume biomass and nitrogen accumulation

Hyperspectral remote sensing provides opportunity for a nondestructive tool for estimating biochemical or biophysical characteristics of agricultural crops. Importantly, among rotational legume production, timely identification of the extent of nitrogen accumulation (N accum) by the legumes is vital for optimization of N fertilizer application and simultaneous reduction of environmental impacts (i.e. excess runoff). We developed in-situ hyperspectral data-based models to predict nitrogen concentration (N conc), biomass, and N accum for three legumes (soybean, tepary bean, mothbean) through application and testing of four methods of machine learning (k-Nearest Neighbors [KNN], partial least squares regression [PLS], support vector machine [SVM], and random forest [RF]). An exploration of potential applications of a future hyperspectral satellite (i.e. CHIME) was also conducted by convolving hyperspectral wavelengths to fit hyperspectral bands of the satellite to conduct a similar analysis to that of the in-situ hyperspectral data. Other analytics such as legume type incorporation and direct versus derived N accum from hyperspectral data were explored. Results suggest hyperspectral remote sensing has great promise across both the in-situ and convolved satellite (i.e. CHIME) bands. Moreover, models based on SVM and RF machine learning algorithms had the greatest outcomes across the tested algorithms, with SVM being less computationally expensive than RF. Moreover, legume type was not significantly important to model development, and N accum is best modeled directly rather than being derived independently from the modeling of N concentrations and biomass. These findings enable the modeling of biophysical and biochemical characteristics of legumes using combinations of hyperspectral remote sensing and machine learning methodologies.

COVID-19 Hakkındaki Türkçe Tweetlerde LSTM Ağı Kullanılarak Duygu Sınıflandırması

As Covid-19 pandemic affected everyone in various aspects, people have been expressing their opinions on these aspects mostly on social media platforms because of the pandemic. These opinions play a crucial role in understanding the sentiments towards the pandemic. In this study, Turkish tweets on Covid-19 topic were collected from March 2020 to January 2021 and labelled as positive, negative, or neutral in terms of sentiment using BERT which is a pre-trained text classifier model. Using this labelled dataset, a set of experiments were carried out with SVM, Naive Bayes, K-Nearest Neighbors, and CNN-LSTM model machine learning algorithms for binary and multi-class classification tasks. Results of these experiments have shown that CNN-LSTM model outperforms other machine learning algorithms which are used in this study in both binary classification and multi-class classification tasks.

Android Malware Detection Based on Program Genes

The security issues with mobile devices have received more attention as a result of the development of mobile Internet technology and the adoption of mobile intelligent terminal devices. It is becoming more crucial to quickly and effectively identify and remove harmful applications from systems in order to protect user data and personal devices. The Dalvik bytecode, permission applications, and system calls of Android apps are the main targets of the current Android malware analysis approaches. However, in recent years, an increasing amount of Android malware conceals harmful code in native code. The method for using program gene technology to identify malware on the Android platform is presented in this research. This method extracts executable library files from the Native layer binary executable files of Android programs and disassembles the library files to obtain program genes. Then, the programs’ genes perform feature screening by the information gain method and next use Word2Vec to express the semantic abstraction of the screened features. Finally, the screened features were used in deep neural network models for training and detection. The experimental results demonstrate that compared with KNN, SVM, and other machine learning algorithms, the deep neural network model is more effective and the detection accuracy reaches up to 97.51%. Thus, it confirmed the feasibility of the Android malicious program detection method based on program genes in this paper.

ConvNeXt-Haze: A Fog Image Classification Algorithm for Small and Imbalanced Sample Dataset Based on Convolutional Neural Network

Aiming at the different abilities of the defogging algorithms in different fog concentrations, this paper proposes a fog image classification algorithm for a small and imbalanced sample dataset based on a convolution neural network, which can classify the fog images in advance, so as to improve the effect and adaptive ability of image defogging algorithm in fog and haze weather. In order to solve the problems of environmental interference, camera depth of field interference and uneven feature distribution in fog images, the CutBlur-Gauss data augmentation method and focal loss and label smoothing strategies are used to improve the accuracy of classification. It is compared with the machine learning algorithm SVM and classical convolution neural network classification algorithms alexnet, resnet34, resnet50 and resnet101. This algorithm achieves 94.5% classification accuracy on the dataset in this paper, which exceeds other excellent comparison algorithms at present, and achieves the best accuracy. It is proved that the improved algorithm has better classification accuracy.

IoT-based Emulated Performance Evaluation NLP Model for Advanced Learners in Academia 4.0 and Industries 4.0

In recent years, most of the research exhibits in the field of Education 4.0 Training Systems (ETS) and Industry 4.0 Training Systems (ITS) that has the ability to learn the behavior of the learners, interns, or trainees. Understanding the feelings and emotions of learners toward learning is essential for creating a practical and exciting learning experience. Patience-emotions detection and sentiments analysis have emerged as an integral part of the understanding of the behaviors of learners, thus there is a need to expand the overall educational or training process in academics and industries. This model enables teachers, trainers and instruction designers to obtain valuable information, which can be used to optimize teaching strategies to improve learning outcomes. To achieve this goal with IoT-enabled objects, an Academician can create a more personalized and effective learning environment for students, trainees and interns. A novel emulated framework is designed and implemented with IoT and machine learning techniques to analyze the performance of learners. The model receives feedback from 1000 learners using IoT devices and analysis the missing information in the learning systems, that missing information lacked effective learning. This emulated framework analyzes the performance of the model. A novel and innovative early warning system is also created to send the warning on WhatsApp or email to several users in a single shot, when achieving certain goals such as file’s size limits and so on. In this research SVM, MCC, NLP and CNN machine learning algorithms are applied to detect students’ feelings and emotions to track the feedback via IoT enabled system.

Monthly Agricultural Reservoir Storage Forecasting Using Machine Learning

Storage rate forecasting for the agricultural reservoir is helpful for preemptive responses to disasters such as agricultural drought and planning so as to maintain a stable agricultural water supply. In this study, SVM, RF, and ANN machine learning algorithms were tested to forecast the monthly storage rate of agricultural reservoirs. The storage rate observed over 30 years (1991–2022) was set as a label, and nine datasets for a one- to three-month storage rate forecast were constructed using precipitation and evapotranspiration as features. In all, 70% of the total data was used for training and validation, and the remaining 30% was used as a test. The one-month storage rate forecasting showed that all SVM, RF, and ANN algorithms were highly reliable, with R2 values ≥ 0.8. As a result of the storage rate forecast for two and three months, the ANN and SVM algorithms showed relatively reasonable explanatory power with an average R2 of 0.64 to 0.69, but the RF algorithm showed a large generalization error. The results of comparing the learning time showed that the learning speed was the fastest in the order of SVM, RF, and ANN algorithms in all of the one to three months. Overall, the learning performance of SVM and ANN algorithms was better than RF. The SVM algorithm is the most credible, with the lowest error rates and the shortest training time. The results of this study are expected to provide the scientific information necessary for the decision-making regarding on-site water managers, which is expected to be possible through the connection with weather forecast data.

Various Deep Learning Techniques Involved In Breast Cancer Mammogram Classification – A Survey

The most common and rapidly spreading disease in the world is breast cancer. Most cases of breast cancer are observed in females. Breast cancer can be controlled with early detection. Early discovery helps to manage a lot of cases and lower the death rate. On breast cancer, numerous studies have been conducted. Machine learning is the method that is utilized in research the most frequently. There have been a lot of earlier machine learning-based studies. Decision trees, KNN, SVM, naive bays, and other machine learning algorithms perform better in their respective fields. However, a newly created method is now being utilized to categorize breast cancer. Deep learning is a recently developed method. The limitations of machine learning are solved through deep learning. Convolution neural networks, recurrent neural networks, deep belief networks, and other deep learning techniques are frequently utilized in data science. Deep learning algorithms perform better than machine learning algorithms. The best aspects of the images are extracted. CNN is employed in our study to categorize the photos. Basically, CNN is the most widely used technique to categorize images, on which our research is based.

Fault Diagnosis of Rotary Machines Using Machine Learning

Classification of faults in rotary machines using machine learning is gaining attention in the field of science and engineering. In rotating machinery, misalignment is a common fault. This type of fault has been extensively studied in the literature using the vibration signals produced by rotary machines. This study proposes an approach based on machine learning techniques to diagnose misalignments in rotary machines under various conditions. A personalized diagnostic fault approach is proposed to detect misalignment faults. The approach includes three steps. First, the data acquisition model is developed to obtain signals (fault samples). The rotor vibration signals in stationary rotation conditions were obtained by two inductive proximity sensors with analog output, and the data were collected by a data acquisition device. Then, to generate the faulty training samples, each data acquisition signal is transformed to the frequency domain using Fast Fourier Transform (FFT). Finally, using the samples obtained through the feature selection techniques, machine learning algorithms Random Forest, Naïve Bayes and SVM were evaluated, resulting in classifications with different efficiencies. The results show that the SVM algorithm outperforms the Naïve Bayes and Random Forest algorithms when the same number of features is used. The proposed personalized diagnostic approach was applied to detect faults of rotating electrical machines misalignment with success.

CRICKET ANALYSIS USING MACHINE LEARNING

This project uses machine learning algorithms to set the matter of predicting match results supported historical matches information. ML is used to predict the match result variable by developing classification models based on certain freelance variables like player’s position, their batting and bowling performances, weather, location, etc. However, we have a tendency to 1st analyze the info and train the models then it’s straightforward to predict the match results. We as a team utilize SVM and Random Forest machine learning algorithms to research and predict the result of the sport, and it'll facilitate the coaches of the team acquire knowledge and analyze, wherever really the team goes wrong and also the space of the development. the method involves preparing the model supported previous matches history, then the developed model gets evaluated on associate degree separate future match to calculate its effectiveness

Diabetes Induced Factors Prediction Based on Various Improved Machine Learning Methods

Background: With the increasing quality of life of people, people have begun to have more time and energy to pay attention to their own health problems. Among them, diabetes, as one of the most common and fastest-growing diseases, has attracted widespread attention from experts in bioinformatics. People of different ages all over the world suffer from diabetes, which can shorten the life span of patients. Diabetes has a significant impact on human health, so that the accuracy of the initial diagnosis becomes essential. Diabetes can bring some serious complications, especially in the elderly, such as cardiovascular and cerebrovascular diseases, stroke, and multiple organ damage. The initial diagnosis of diabetes can reduce the possibility of deterioration. Identifying and analyzing potential risk factors for different physical attributes can help diagnose the prevalence of diabetes. The more accurate the prevalence, the more likely it is to reduce the incidence of complications. Methods: In this paper, we use the open source NHANES data set to analyze and determine potential risk factors relevant to diabetes by an improved version of Logistic Regression, SVM, and other improved machine learning algorithms. Results: Experimental results show that the improved version of Random Forest has the best effect, with a classification accuracy of 92%, and it can be found that age, blood-related diabetes, high blood pressure, cholesterol and BMI are the most important risk factors related to diabetes. Conclusion: Through the proposed method of machine learning, we can cope with class imbalance and outlier detection problems.

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.

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.

Classification of Amyloidosis by Model-Assisted Mass Spectrometry-Based Proteomics.

Amyloidosis is a rare disease caused by the misfolding and extracellular aggregation of proteins as insoluble fibrillary deposits localized either in specific organs or systemically throughout the body. The organ targeted and the disease progression and outcome is highly dependent on the specific fibril-forming protein, and its accurate identification is essential to the choice of treatment. Mass spectrometry-based proteomics has become the method of choice for the identification of the amyloidogenic protein. Regrettably, this identification relies on manual and subjective interpretation of mass spectrometry data by an expert, which is undesirable and may bias diagnosis. To circumvent this, we developed a statistical model-assisted method for the unbiased identification of amyloid-containing biopsies and amyloidosis subtyping. Based on data from mass spectrometric analysis of amyloid-containing biopsies and corresponding controls. A Boruta method applied on a random forest classifier was applied to proteomics data obtained from the mass spectrometric analysis of 75 laser dissected Congo Red positive amyloid-containing biopsies and 78 Congo Red negative biopsies to identify novel “amyloid signature” proteins that included clusterin, fibulin-1, vitronectin complement component C9 and also three collagen proteins, as well as the well-known amyloid signature proteins apolipoprotein E, apolipoprotein A4, and serum amyloid P. A SVM learning algorithm were trained on the mass spectrometry data from the analysis of the 75 amyloid-containing biopsies and 78 amyloid-negative control biopsies. The trained algorithm performed superior in the discrimination of amyloid-containing biopsies from controls, with an accuracy of 1.0 when applied to a blinded mass spectrometry validation data set of 103 prospectively collected amyloid-containing biopsies. Moreover, our method successfully classified amyloidosis patients according to the subtype in 102 out of 103 blinded cases. Collectively, our model-assisted approach identified novel amyloid-associated proteins and demonstrated the use of mass spectrometry-based data in clinical diagnostics of disease by the unbiased and reliable model-assisted classification of amyloid deposits and of the specific amyloid subtype.