SVM Machine Learning Algorithm Research Articles

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.

Using a Logistic Regression Model to Examine the Variables Influencing Changes in Northern Thailand’s Forest Cover and Comparing Machine Learning Algorithms

Protecting biodiversity and keeping the Earth’s temperature stable are both very important jobs performed by tropical forests. In the last few decades, remote sensing has given us new tools and ways to track changes in land cover. To understand what causes changes in forest cover, it is important to look at the things that affect those changes. However, there is not enough research that uses a logistic regression model (LRM) and compares the results with machine learning (ML) techniques to investigate the specific factors that cause forest cover change in remote mountainous areas like Thailand’s Mae Hong Son and Chiang Mai Provinces. Following a comparison of an LRM, a random forest, and an SVM, this study of the causes of changes in forest cover in Mae Hong Son found six important factors: soil series, rock types, slope, the NDVI, the NDWI, and the distances to city areas. Compared to the LRM, both the RF and SVM machine learning algorithms had higher values for the kappa coefficient, sensitivity, specificity, accuracy, positive and negative predictions, and sensitivity, especially the RF. Following what was found in Mae Hong Son, when the important factors were examined in Chiang Mai, the RF came out on top. It is believed that these results can be used in more situations to help make plans for restoring ecosystems and to promote long-lasting methods of managing land use.

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.

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.

Significance of m6A in subtype identification, immunological evolution, and therapeutic sensitivity of RA

N6-methyladenosine (m6A) is one kind of important epigenetic modification pattern which is extensively involved in immune regulation. The development and progression of autoimmune diseases are closely related to immune dysregulation. Considering that rheumatoid arthritis (RA) is a typical autoimmune disease, the m6A process might be one of the important regulatory mechanisms in the pathogenesis of RA. In this study, we identified five differentially expressed m6A regulators in normal and RA samples from the GEO database. With these five regulators, we constructed the nomogram, and it could accurately identify the risk of RA morbidity. Next, we identified 121 differentially expressed genes (DEGs) between normal and RA samples, of which 36 DEGs were co-expressed with these five m6A regulators. We noted that these DEGs were highly enriched in multiple immunoregulatory signaling pathways, such as cytokine-mediated immune cell chemotaxis, adhesion, and activation. To further characterize the heterogeneity of immunological features, we clustered the RA samples into two subtypes. The C2 subtype has higher infiltration levels of pro-inflammatory cells and activity of pro-inflammatory signaling pathways. Thus, the inflammatory response might be more vigorous in the C2 subtype. Next, we constructed the m6Asig system with the SVM machine learning algorithms and least absolute shrinkage and selection operator (LASSO) regression. The m6Asig could accurately distinguish the C1 and C2 subtypes, which indicated that the m6Asig could be a potential biomarker for the inflammatory activity of RA. Finally, by comparing the information from the CellMiner, TTD, and DrugBank databases, we determined 25 drugs. The targets of these drugs were positively correlated with m6Asig. To be clarified, the above findings were derived from bioinformatics and statistical analyses, and further experimental validation still requires. In summary, this study further revealed the m6A and immunoregulation mechanisms in RA pathogenesis. Also, the m6Asig could be a novel biomarker with potential applicability in the clinical management of RA.

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.

Increasing the Effectiveness of Higher Education Academic Services Through the Implementation of the Chatbot Platform Using the SVM Machine Learning Algorithm

The education sector has adopted technology and digitization. To create appropriate technology that can increase the effectiveness and efficiency of existing processes in education, especially higher education, innovations are needed that can provide value to tertiary institutions. The right support is needed to achieve value in academic services as the heart of higher education. However, many universities still have not been able to provide maximum service. This research aims to create a chatbot model to support effective academic services for tertiary institutions. This research belongs to the type of research design science research (DSR). The research procedures were carried out by collecting data, categorizing data, creating chatbot models, model evaluation, and model implementation. Data was collected by inviting resource persons through focus group discussions (FGD), with the criteria being prospective students, university students, and the public interested in academic services in tertiary institutions. The resource persons were asked questions about the academic services needed. Based on the data obtained, there were 257 questions related to academic services. Service categorization is the process of classifying questions based on the functions of divisions or departments in tertiary institutions. Based on the data collection and service categorization results, a chatbot model is created, followed by model evaluation and implementation. The research analysis results show that the academic management chatbot model that uses the SVM algorithm can classify questions asked through chatbots with an accuracy of 57%, performing to support higher education academic services.

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.

Wind Power Plant Site Selection using Integrated Machine Learning and Multiple-Criteria Decision Making Technique

The growing demand for clean and sustainable energy sources has driven countries around the world to explore renewable energy options, including wind power. This research focuses on the use of machine learning techniques to optimize the site selection process for wind power plants in the Philippines. The study aims to address the challenge of identifying suitable locations for wind power plant development, which requires the assessment of various environmental and socio-economic factors. The research utilizes various datasets, including wind speed and direction, topography, land use, population density, and infrastructure availability. Additionally factors on The datasets was acquired to the Maps that contains road network, urban areas, protection areas, slope, wind speed, water courses, natural disasters and transmissions lines. These datasets are processed and analysed using SVM machine learning algorithms to identify the most suitable sites for wind power plant development. The study results indicate that machine learning techniques can provide a more accurate and efficient approach to wind power plant site selection compared to traditional methods. The model can identify areas with high potential for wind energy generation, taking into account various factors that influence the feasibility and profitability of wind power plant development. The research findings are expected to provide valuable insights for policymakers, investors, and other stakeholders involved in the renewable energy sector in the Philippines. The use of machine learning techniques can facilitate the identification of optimal locations for wind power plants, leading to more efficient and effective renewable energy development in the country.

Classification and Prediction by Pigment Content in Lettuce (Lactuca sativa L.) Varieties Using Machine Learning and ATR-FTIR Spectroscopy.

Green or purple lettuce varieties produce many secondary metabolites, such as chlorophylls, carotenoids, anthocyanins, flavonoids, and phenolic compounds, which is an emergent search in the field of biomolecule research. The main objective of this study was to use multivariate and machine learning algorithms on Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR)-based spectra to classify, predict, and categorize chemometric attributes. The cluster heatmap showed the highest efficiency in grouping similar lettuce varieties based on pigment profiles. The relationship among pigments was more significant than the absolute contents. Other results allow classification based on ATR-FTIR fingerprints of inflections associated with structural and chemical components present in lettuce, obtaining high accuracy and precision (>97%) by using principal component analysis and discriminant analysis (PCA-LDA)-associated linear LDA and SVM machine learning algorithms. In addition, PLSR models were capable of predicting Chla, Chlb, Chla+b, Car, AnC, Flv, and Phe contents, with R2P and RPDP values considered very good (0.81−0.88) for Car, Anc, and Flv and excellent (0.91−0.93) for Phe. According to the RPDP metric, the models were considered excellent (>2.10) for all variables estimated. Thus, this research shows the potential of machine learning solutions for ATR-FTIR spectroscopy analysis to classify, estimate, and characterize the biomolecules associated with secondary metabolites in lettuce.

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.

Feature selection and classification improvement of Kinnow using SVM classifier

An approach to enhance the classification of the kinnow is proposed in this paper. The fruit images are captured in the proposed approach, and texture, color, shape, and size features are extracted and merged to generate a dataset. To cope with outliers and dominant features, the Pareto normalization method is used. A hybrid feature selection approach that combines neighborhood component analysis and ReliefF methods to select the optimal features is proposed to eliminate redundant and irrelevant features. The dataset is classified using the SVM machine learning algorithm following the feature selection. Utilizing the SVM classifier, the proposed approach chooses 54.20% of the features with an accuracy of 94.67%. This proves that the proposed approach is efficient and can be used for the classification of the other fruits.

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

Overcoming the error of optical power measurement caused by the curvature radius.

In traditional focimeter measurements, the lens cannot completely coincide with the diaphragm owing to the change of radius, resulting in an increase in the power measurement error with an increase in the lens power. We proposed a method, using the SVM machine learning algorithm, to restore the measurement of the focimeter, using a lens power data set obtained from lens features, obtained through an automatic acquisition system. Total up to 83 groups of single focus lenses with refractive indices of 1.56 and 1.60, ranging from -10 m-1 to + 8 m-1 every 0.25 m-1 and -10.5 m-1 to -15 m-1 every 0.5 m-1, were used for lens image acquisition. The experimental results show that the ten-fold average F1 score of the classification under this method is 100%. The test lens power can be accurately identified and the measurement error can be overcome.