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Online social media platforms such as YouTube have grown in popularity in recent years, providing as critical routes for content creators, advertisers, and platform administrators to access a large worldwide audience. Accurate video popularity prediction is critical for improving content strategy, increasing user engagement, and increasing video visibility. This study intends to improve video popularity prediction accuracy by presenting a fresh methodology that goes beyond traditional analysis. To construct a more effective prediction model, the suggested method combines examining metadata, such as title, description, and channel title, with thumbnail analysis. The project aims to dramatically increase prediction accuracy by extracting relevant information from both metadata and thumbnails. To do this, multiple classification techniques, including Support Vector Machine, Gradient Boosting, Random Forest, Extreme Gradient Boosting, and K-Nearest Neighbors, were used to train and forecast the model. Notably, when it came to feature extraction, the Gradient Boosting and Random Forest algorithms achieved amazing accuracy of 97% when using all extracted and original data, and 82% and 92% when using only extracted features. In comparison, previous studies employing simply extracted features obtained only 74% accuracy. These findings demonstrate the potential benefit of integrating metadata and thumbnail data research to get insights into the elements that drive video popularity and lead to more accurate predictions. [ABSTRACT FROM AUTHOR]

Classification of a person's cognitive state is important in many applications. The aim of this work is to develop a reliable classifier for predicting the cognitive state of the brain while performing various tasks. This dataset was recorded using a Muse headset with 10-20 EEG electrode placements and four different electrodes. The cognitive state of ten students was recorded while performing various tasks, such as mental arithmetic, reading technical articles, listening to technical podcasts, surfing the Internet, and resting with eyes open or closed. The EEG dataset is preprocessed and feature sets of five signals alpha, beta, gamma, delta and theta are selected for prediction. This dataset is trained by any machine learning algorithm such as decision trees, naive Bayes, and support vector machines. All machine learning algorithms are then embedded into a Stacking Ensemble Classifier with 10-fold cross-validation to predict the cognitive state of brain signals and classify them as high and low. Compared with existing algorithms, the method achieves an overall accuracy rate of 96%. [ABSTRACT FROM AUTHOR]

Cybercrime has been on a massive increase for several years. As more individuals conduct business and live their lives online, more criminals are turning to the internet to steal. With the developments in cybersecurity, adoption of best practices, cybersecurity awareness initiatives, and increased regulations and partnerships between businesses and governments to resolve the problems, cybercriminals may be feeling the pressure. In this paper, we are going to predict operating system vulnerabilities using machine learning algorithms. And evaluate their accuracies through training them. The aim of the paper is to use Machine Learning algorithms to predict with accuracy operating systems vulnerabilities. Different ML algorithms have been analyzed, such as Logistic Regression, Support Vector Machine and Random Forest. And, out of all of these algorithms, Random Forest had the highest accuracy of 99.33%. [ABSTRACT FROM AUTHOR]

The study aims to evaluate the performance of the Music Genre Prediction System using a Novel Support Vector Machine and Decision Tree. The MARSYAS website provided the GTZAN dataset utilised in this investigation, which is comprised of one thousand.au-formatted music files and is intended for use in Music Information Retrieval. It is currently considered the gold standard dataset. Mel-frequency cepstralcoefficients (MFCC) are retrieved from music files and used to make predictions about the genre. All steps, from analysing data to training a model to testing it, take place within Jupyter. SPSS is used to analyse the mean accuracies of two algorithms side-by-side. Accuracy, precision, etc., numbers came out differently depending on the size of the input data set. SVM and Decision Tree are used for classification, with N equal to 20 for each of the two groups (proposed and comparative). The value of the pre-test achieved is 0.08. This research shows that compared to the Decision Tree method, Novel SVM is more effective. In the Research study, SVM with 81.10% accuracy is found more effective, efficient, and faster when compared to the Decision Tree with 59.88% accuracy. [ABSTRACT FROM AUTHOR]

The main goal of the research is to train machines to predict fully paid loan repayment with higher accuracy using Novel Support Vector Machine (SVM) than Logistic Regression (LR) classifier on lending club data. Materials and Methods: Novel Support Vector Machine and Logistic Regression Classifier techniques classify enhanced fully paid loan repayment with increased prediction accuracy. The fully paid loan repayment system prediction sample size was 3500 (Group 1=1750 and Group 2=1750). Results: The Novel Support Vector Machine accuracy rate is 94.19 percent, while Logistic Regression is 89.72 percent. Novel Support Vector Machine precision is 94.19 percent, while Logistic Regression precision is 89.23 percent. The Novel Support Vector Machine recall rate is 94.72 percent, while Logistic Regression is 89.33 percent. Novel Support Vector Machine has 95.64 percent specificity and Logistic Regression 90.01 percent. Accuracy rate significantly differs with P=0 (P<0.05) 2-tailed test. Conclusion: Novel Support Vector Machine Classifier outperforms Logistic Regression Classifier in accuracy, precision, recall, and specificity for fully paid loan repayment systems. [ABSTRACT FROM AUTHOR]

This research employs a novel approach to headcounting by comparing the performance of the ANN algorithm with that of the SVM algorithm. To determine how many individuals are moving under an overhead monitoring system, researchers have developed a revolutionary paper counting approach based on head recognition and tracking. Substances and Techniques: Twenty samples in all were taken from an electronic resource. We maintained a significance level of 0.05, a confidence interval of 95% (95% CI), an enrollment ratio of 1, and a significance level of 80% (G power). The precision value is calculated using the SPSS software. Result: The results of two separate t-tests show that the ANN algorithm outperformed the SVM method, although the difference is not statistically significant (p=0.721; p>0.05). In summary, it seems that the ANN algorithm outperforms the SVM method on the analysed datasets. [ABSTRACT FROM AUTHOR]

The primary purpose of this study is to use unique blood sample data to the analysis of the gender ratio for diabetes prediction. The Components and Procedures: Glucose level, diastolic bp, blood thickness, insulin levels, body mass index, age, and skin are some of the features included in the dataset utilised for training and testing the suggested prediction models. Patients' data were gathered from various online sources, and the framework was built using their novel blood sample data and the Voting Classifier over Support Vector Regression machine learning algorithm applied to a diabetes dataset that contains 1695 records with 10 attributes, the samples considered are (N=10) for both the algorithms with two groups and g-power value of 80%, and st. Voting Classifier (94 percent) outperformed Support Vector Regression in a statistical examination of blood-sample data for predicting diabetes prevalence by gender (80 percent). In summary, the findings of this study demonstrate that the Naive Bayes method is more successful than the SVM algorithm constructed in Python. [ABSTRACT FROM AUTHOR]

Technological advances have enabled skin cancer detection studies utilising machine learning algorithms. Machine learning is best for early, curable head and neck non-melanoma skin cancer (NMSC) detection and diagnosis. Techniques and Substances: This study examined real-world machine learning disease diagnosis algorithms. This research uses Novel Logistic Regression (LR) and Support Vector Machine classification algorithms, which use training and validation sets, respectively (SVM). SVM and LR algorithms were assigned to Groups 1 and 2 based on their g-power values of 80% and current study findings using images of squamous cell carcinoma from online sources (threshold: 0.05 percent, Confidence Interval: 95 percent mean and standard deviation). This study's findings will help machine learning systems predict such illnesses. The two-tailed significance value of 0.035 (p0.05) with a 95% confidence range shows that the SVM algorithm is 81.58 percent accurate and the LR algorithm is 85.47 percent accurate. This study recommends POC skin cancer prevention and early diagnosis based on a Machine Learning analysis of the data. [ABSTRACT FROM AUTHOR]

The purpose of the proposed framework is to use a variety of machine learning algorithms to assess and predict the trend in changes of the stock market price index for a subset of three stocks in the Stock Market. The Components and Procedures: Four groups of categorization techniques—Support Vector Machine (SVM), Linear Regression (LR), Logistics Regression (LR), and Long Short-Term Memory (LSM)—are compared in terms of their ability to forecast stock prices within the framework (LSTM). All of the models are evaluated based on their accuracy at forecasting the direction of change in the Bombay Stock Exchange (BSE) index. An estimated 30 equities were used to assess the novel framework's accuracy in making predictions for the proposed framework. The experimental findings showed that the SVM model (99.27%), the Linear Regression (98.18%), and the Logistics Regression (98.10%) all outperformed the LTSM model (72.04%) by a substantial margin (p0.05). The results of the experiments show that the Support Vector Machine outperforms LSTM, linear regression, and logistics regression in terms of accuracy and precision. [ABSTRACT FROM AUTHOR]

The primary goal of this study is to predict fetal heart disease detection using Magnetic Resonance Imaging (MRI) scan images and comparing with decision tree (DT) classifiers to improve accuracy, specificity, and disease type. Materials and Methods: In this research 20 Magnetic Resonance Imaging (MRI) scan images in a view of samples of SVM (N=10) and DT (N=10). The sample size is calculated for each group with 80% of G power, 95% confidence interval, and 0.05 error rate (Alpha). Results: The proposed method SVM classifier achieves a high accuracy of 93%, specificity of 89% and DT classifier 82% accuracy, 79% specificity. The significance rate of accuracy is (p=0.001) and specificity is (p=0.018). Conclusion: The Novel Support Vector Machine classifiers have achieved significantly better accuracy, specificity when compared with the decision tree (DT) classifier to predict fetal heart disease. [ABSTRACT FROM AUTHOR]

The purpose of this study was to assess the accuracy of cybercrime predictions provided using Novel Random Forest and Support Vector Machine. Material and Procedure: Cybercrime predictions were made using the new random forest (N=10) and the support vector machine (N=10), which were then analysed. Random forest trumps SVM in terms of precision (by a margin of 84 percent to 85 percent). 81 percent is the percentage. The advanced random forest classifier performed better than the conventional SVM classifier. There is a significant difference in accuracy between the two approaches (p>0.005). The development of the new cybercrime prediction system used machine learning. This novel random forest technique outperformed SVM. [ABSTRACT FROM AUTHOR]

This study compares the performance of the Support Vector Machine (SVM) classification method with that of the Self Organizing Map (SOM) algorithm for predicting agricultural relief rains over a given time period (SOM). The Components and Techniques: There were 88 participants in all (44 in Group 1 and 44 in Group 2) for the relief rainfall prediction study. Relief rainfall prediction for the selected time of agriculture is compared using the Self Organizing Map (SOM) and the Support Vector Machine (SVM) algorithms, both of which take into account a large number of samples (N=10). Findings show that the SOM has a 93.37 percent accuracy rate, while the SVM method only has an accuracy rating of 84.032 percent. The accuracy rate difference confidence range is 95%, and two-tailed test P is 0.0303. (p0.05). We conclude that the Self Organizing Map (SOM) method predicts relief rainfall for a specific agricultural period more accurately than the Support Vector Machine (SVM) technique. [ABSTRACT FROM AUTHOR]

The primary goal of this study is to perform leaf disease diagnosis using K-Nearest Neighbors classification (KNN) and compare its performance against that of Support Vector Machine (SVM) technique to increase accuracy. Materials and Techniques SVM was used in this study to detect leaf illness by sampling 10 samples (N=10), and K-Nearest Neighbors was used to classify the samples (N=10). With a sample size of 20 for each group and a pretest power of 80%, two groups are analyzed statistically. Both strategies' accuracy is evaluated. The Support Vector Machine algorithm outperforms the KNN approach in terms of accuracy (96.25% vs. 83.3%), and independent samples T-tests show a statistically significant difference between the two algorithms' accuracy at p=0.37 (p0.05). Conclusion: According to the results, the Support Vector Machine technique for identifying leaf diseases appears to be noticeably superior to K-Nearest Neighbors categorization. [ABSTRACT FROM AUTHOR]

The primary purpose of this research is to use the Support Vector Machine (SVM) classifier and the Naive Bayes (NB) classifier to make arrhythmia predictions from the MIT-BIH database. With an alpha of 0.05, 95% confidence interval (CI), 80% power, and an enrollment ratio of 1, the proposed research employed SVM and NB machine learning algorithms to predict arrhythmia using the MIT-BIH dataset of 65 normal and 65 abnormal ECG signals downloaded from IEEE dataport in.xlsx format. We used the data mining programme WEKA 3.8.5 to determine how to categorise people with and without arrhythmia. IBM SPSS version 21 was used for the statistical analysis. When comparing SVM and NB classifiers, a statistically significant difference (p0.010) was found. Using WEKA's 10-fold cross-validation for training and testing, the SVM classifier outperformed the NB classifier with an 88.50% accuracy rate in classification (80.39 percent). [ABSTRACT FROM AUTHOR]

This research aimed to evaluate Novel Ridge Regression in comparison to the SVM machine learning method for analysing Covid-19's effect on social and public media. Sample sizes for each group were 110 for Novel ridge regression and 110 for the SVM (Support Vector Machine) machine learning approach, both of which were used to classify a dataset of 65896. The experimental data shows that the innovative ridge regression model outperforms the SVM model in terms of RMSE, with a p-value of 0.032 (two-tailed sample test, p0.005). When comparing SVM and the innovative Ridge Regression model for analysing the effect of COVID-19 on social and mainstream media, the latter model comes out on top. [ABSTRACT FROM AUTHOR]

Using Novel Support Vector Machine (SVM) and Multiclass Support Vector Machine (SVM-MC) for genre-based classification of audio recordings by using low-level information in the frequency and temporal domain is the major purpose of this work (MSVM). In the current investigation, we make use of two case studies: one for the SVM technique, and the other for the MSVM methodology. To obtain the desired values of 80 percent G power, 0.05 percent threshold, and 95 percent CI, the device was sampled ten times, and with each iteration, the accuracy was improved by 5 percent. When it comes to predicting the classification of Audio files using the low-level characteristic of frequency, the SVM approach achieves a greater success rate of 91.25 percent as opposed to the MSVM method, which achieves a success rate of 84.78 percent. When comparing the two groups, a significance level of 0.008 (p0.05) implies that there is a statistically significant gap between them. When it comes to forecasting low-frequency audio data, the SVM approach performs much better than the CNN algorithm. [ABSTRACT FROM AUTHOR]

This study's objectives are to forecast cardiac disease using the support vector machine (SVM) approach, improve the accuracy of prediction using machine learning classifiers, and evaluate the usefulness of these various methods. The Support Vector Machine (SVM) and the Naive Bayes method are the two classes being compared here. The algorithms have been tested and evaluated using a dataset that contains 1,700 records. Clincalc.com suggests that the sample size should be 540, with an estimated 80 percent power for the pretest. The statistical analysis includes twenty different samples. Following the completion of the trial, the SVM algorithm obtained a mean accuracy of 89.38 percent in predicting heart disease, but the Naive Bayes technique only reached an accuracy of 80.66 percent. The results of independent sample t-tests indicate that there is a statistically significant difference in performance between the two approaches (p 0.05). The use of machine learning strategies is going to be investigated in this project in an effort to improve heart disease prediction. During the comparison, it was determined that SVM was better than Naive Bayes in terms of the output. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to use Support Vector Machine (SVM) instead of the traditional Naive Bayes technique to extract patient-specific information from a dataset of lung cancer patients in order to calculate diagnostic accuracy (NB). The methods of Naive Bayes and Support Vector Machine are used in this study. Using the G power programme, we arrive at a sample size of 10 individuals per group with a pretest power of 80%, a significance level of 0.05, and a confidence interval (CI) of 95%. The innovative data analysis provided by Support Vector Machine for determining the likelihood of cancer is 92.45 percent, which is greater than the 84.67 percent provided by the Naive Bayes method. With a significance score of 0.88 (p>0.05), there is no statistically significant difference between the two groups. By extracting patient-specific information, the Support Vector Machine method outperforms the Naive bayes algorithm in making accurate cancer predictions. [ABSTRACT FROM AUTHOR]

The core of this work presents an assembly method of EEG signals for the early detection of brain tumors using a novel support vector machine (SVM) algorithm compared with a decision tree algorithm Materials and methods: Collected from UCI repository 1472 samples contain 19 attributes. Early detection of brain tumors is performed using two groups, where group 1 is a SVM and group 2 is a decision tree. For the analysis of groups 1 and 2, 40 samples were used. Results: The accuracy of the SVM algorithm was 93%, while the accuracy of the decision tree was 82%. A significant value of 0.037 (P<0.05) was obtained and determined by independent samples test. Conclusion: The support vector machine algorithm has significantly higher accuracy than the decision tree algorithm. [ABSTRACT FROM AUTHOR]

The primary purpose of this investigation is to compare and contrast the performance of two machine learning algorithms, namely the Lasso regression Algorithm and the Support Vector Machine Algorithm, in the context of accident prediction. Over the course of 20 iterations, utilising N=10 samples, the Support Vector Machine approach, and Lasso regression, the accuracy of accident predicting was tested. A substantial gap (84.28 percent) separates the performance of the Support Vector Machine method from that of the Lasso regression methodology (94.56 percent). A significance level of 0.001 has been assigned to the new SVM classifier (p0.05 independent sample test). Comparisons of data showed that the Support Vector Machine Novel classifier was more accurate than the Lasso regression in predicting the occurrence of traffic accidents. [ABSTRACT FROM AUTHOR]

The purpose of this research is to use Machine Learning in Google COLAB to categorise human facial expressions. Images labelled as "happy," "sad," or "neutral" using a novel Support Vector Machine dataset. About 100 were selected for use in preparing the samples. As a training dataset, we used 25 pictures of people smiling broadly. The training dataset consists of 25 pictures of the expression "sad." The dataset used for testing included 25 images labelled with the "sad" emotion, and a similar number labelled with the "happy" emotion. The values of Gpower (80%) and alpha (0.5) were derived from SPSS. The mean difference for SVM in SPSS is 91.6667, while the mean difference for CNN is 87.3333. SVM has a variance of 2.16025 and CNN has a variance of 2.87228. When it comes to facial expression detection and classification, the proposed novel Support Vector Machine classifier has a 98 percent accuracy, while CNN only manages an 88 percent success rate. Regardless of the assumption of equal variances, a 2-tailed significance level of 0.002 is considered accurate. Mask-R Convolutional Neural Network has a 2-tailed significance of 0.840, which holds true even when assuming unequal variances. SPSS simulation yields the equal variances assumptions. The proposed novel Support Vector Machine outperforms CNN in Facial Expression Recognition, but only by a small margin. This demonstrates SVM's innovative potential in practical applications. [ABSTRACT FROM AUTHOR]

The aim of the study was to distinguish fingerprints using state-of-the-art artificial neural networks and support vector machines. Materials and methods: This study compared support vector machines and innovative artificial neural network techniques (N=10). The total sample size was calculated using the g-Power software with an alpha of 0.05, an enrollment rate of 0.1, a confidence interval of 95%, and a pretest power of 80%. Results: The ANN achieves 93% accuracy while the SVM classifier achieves 83% accuracy. In SPSS statistical analysis, the accuracy was found to be significant (p<0.05). Conclusion: The novel ANN classifier provides significantly better fingerprinting performance than the SVM classifier. [ABSTRACT FROM AUTHOR]

The purpose of this research is to evaluate the performance of four distinct approaches to prediction—namely, Logistic Regression (LR), Decision Tree (DT), K Means Algorithm (KMA), and Support Vector Machine (SVM) Classification—in order to determine which one yields the most accurate results. The Following are the Components and Methods: When testing the Classification technique, a dataset consisting of 778 items is employed. In the subject of education, the Logistic Regression (LR), K-Means, Decision Tree, and Support Vector Machine (SVM) are all viable frameworks that have been suggested and developed for the College Recommendation system. K-Means was the first of these frameworks to be built. Through the use of G power, we were able to establish that we need 55 people for each of the conditions. Both the precision and accuracy of the classifiers have been analysed and recognised. Through the use of G Power, we were able to establish that each of our groups required 55 individuals. The College dataset had a sample size of 303 students, 76 attributes, and a few blanks in addition to those numbers. A clinical study was conducted to establish the appropriate size of the sample, and the following were the findings: 80 percent predictive power on the pretest, 0.05 alpha, and a 1. When applied to the College Recommendation System data set, the results demonstrate that the K-Means classifier produces the same group that it predicts (50 percent). This is also the case for Logistic regression (86 percent), Decision Tree (88 percent), K-Means (50 percent), and Support Vector Machine (50 percent) (85 percent). The value assigned to significance is zero. Consequently, DT performs better than LR, SVM, and K-Means. The findings indicate that Decision Tree offers greater performance in terms of precision and accuracy when compared to SVM, LR, DT, and K-Means. This is shown by the fact that Decision Tree. [ABSTRACT FROM AUTHOR]

An IDS built using the Information Gain Algorithm and an unique One Class Support Vector Machine with Random Forest Feature Selection (Group 1) for detecting denial-of-service attacks in wireless ad hoc networks are compared for accuracy and detection rate (Group 2). Substances and Methods: An IDS model was created using CIC-IDS 2017. The IDSs were modelled using the above methods and analysed using SPSS with 19 samples per group. The innovative One Class SVM with Random Forest Feature Selection IDS had a detection rate of 90% and an accuracy of 99.0%, whereas the Information Gain Algorithm IDS had 82.0 percent and 80.0 percent. The significance threshold was 0.05. (Accuracy - 0.00; Detection rate - 0.00). Finally, this data shows that the innovative One Class SVM with Random Forest Feature Selection IDS beats the Information Gain Algorithm IDS by a large margin. [ABSTRACT FROM AUTHOR]

An evaluation of SVM and a novel K-Nearest Neighbor classifier for fingerprint verification improvements. Methods and Materials In this article, we compare and contrast the Novel K-Nearest Neighbor Method with a Support Vector Machine. The recommended procedure is applied to a total of 15 samples, 15 from each of the two groups, and the G power is set to 80%. Results: The innovative K-Nearest Neighbor Method method predicts biometrics with an accuracy of 87 percent, which is much higher than the 82 percent accuracy of Support Vector [1]. Here, we calculated a pretest power of 85%, with a sample size of 15 in one group and 30 in total. The mean accuracy for Novel K-Nearest Neighbor (87.12%) and the SVM method (81.85%) are quite similar, however the standard deviation for Novel K-Nearest Neighbor (1.29517) is lower (2.45449) While the Novel K-Nearest Neighbor Method and SVM algorithms achieve similar mean accuracy (87.12 and 81.85 percent, respectively), the standard deviation for the Novel K-Nearest Neighbor Method is somewhat less (1.29517 vs. 2.45449), which is statistically significant at the 0.001 level (p 0.05). The Novel K-Nearest Neighbor Method Algorithm is discovered to be much superior than the SVM algorithm in this research for predicting biometric recognition. [ABSTRACT FROM AUTHOR]

Using a unique logistic regression classifier to improve fingerprint verification over the current state-of-the-art support vector machine. Materials and Techniques A Support Vector Machine is compared to the New Logistic Regression Approach. We run the provided analyses on 15 samples each from the two groups with the G power set to 80%. Results: The Novel Logistic Regression Method outperforms the Support Vector Machine Method by 13 percentage points in biometric identification prediction accuracy. There are 30 people in the sample as a whole, 15 people in one group, and an 85% chance of success based on a power study done beforehand. While both the SVM and the Novel Logistic Regression algorithms achieve 88.04% accuracy on average, the Novel Logistic Regression Method has a substantially smaller standard deviation (1.56574) (1.56574). (1.13469) The Novel Logistic Regression Method has a lower standard deviation (1.56574) than the SVM, making it more suitable for use at the 0.01 level of significance (1.13469). (p0.05). With a mean accuracy of 88.04%, the Novel Logistic Regression Method outperforms the SVM methods (82.94 percent). The Novel Logistic Regression Method Algorithm was shown to be more effective than the SVM Algorithm in this study for predicting biometric identifiers. [ABSTRACT FROM AUTHOR]

The objective of this work is to increase the accuracy of credit card fraud prediction using the Support vector machine approach. Materials and Procedures: A G-power value of 89 percent was applied to iterations of a Support Vector Machine and a Convolutional Neural Network (CNN) with 100 samples each, in an innovative method for forecasting the total amount of credit card fraud that will be committed. Support Vector Machines are far more accurate than Convolutional Neural Networks, with a 98.2% accuracy rate (92.4). K-means clustering outperformed Convolutional neural network at the p=0.004 and p=0.005 levels (CNN). In conclusion, the support vector machine approach enables a state-of-the-art decision support system to reliably predict a broader spectrum of financial transactions. [ABSTRACT FROM AUTHOR]

The goal is to anticipate cardiac arrest in heart disease patients. Machine learning methods like SVM and decision tree categorise photos (DT). To effectively and reliably analyse labelled pictures with G power of 80%, threshold 0.05 percent, CI 95 percent mean and standard deviation, SVM and Decision Tree sample sizes of n=5 were iterated 10 times. The Support Vector Machine (SVM) outperformed the Decision Tree (DT) in predicting and categorising cardiac patients' data with a p-value of 0.05. Support Vector Machine predicts cardiomyopathy risk better than Decision Tree. [ABSTRACT FROM AUTHOR]

The purpose of this study is to apply machine learning methods to the problem of classifying musical genres. Here, signal processing is used to extract the rhythm pattern feature, which is then used in conjunction with machine learning algorithms to provide a multiclass categorization of musical genres. A preexisting internet music collection is used to generate two distinct datasets, one with undersampling and the other with oversampling to achieve class balance. Two types of models were compared for their efficacy in this research. The first model is an enhanced support vector machine that is fully trained to identify the musical genre of an input signal from its spectrogram. The Random Forest Classifier is the second model. G power for this research is 80%; there are two groups in total, and each has a sample size of 20. Experiments performed on the audio dataset show that an assembler classifier that takes use of both techniques achieves an AUC of 0.894. According to the findings, the support vector machine method provides a higher accuracy (69.24%) than the Random Forest Classifier (64.26%). Independent sample T-test significance level of P=0 (P 0.05, 2-tailed). The findings showed that the accuracy of the music genre classification using the enhanced support vector machine with an unique Rhythm pattern feature method was higher than using the Random Forest Classifier. [ABSTRACT FROM AUTHOR]

The study's goal is to create an efficient feedback system for automated and unmanned restaurants that makes use of customers' facial expressions by training the model with six distinct emotions. Substances and Techniques: To identify human emotions in cashierless eateries, the proposed system employs a unique gradient descent technique to the Improved support vector machine. The FER2013 dataset was utilised for this implementation. In this research, 392 people make up each of two groups. The outcomes showed that the proposed method was 82.14 percent accurate, which is a significant improvement over the current system's accuracy of 60.84 percent. Using a novel gradient descent technique, the results reveal that the upgraded support vector machine surpasses the current system in terms of accuracy when applied to the feedback system of unmanned and automated restaurants. This system can also be utilised as a resort feedback system to enhance the vacation experience for guests. [ABSTRACT FROM AUTHOR]

Metrics such as accuracy, recall, and f-score will be used to compare the results of a decision tree approach to detecting lung cancer with those achieved by a novel polynomial support vector machine (SVM). The researchers have access to 1339 lung cancer patient samples because to the Kaggle collection. In order to determine the G power for samples, we need clinical data, which consists of two groups: group 1, with (n1=670) and group 2, with (n2=670), both with (alpha=0.05) and (power=80%) values. The f-score, recall, and precision were all computed. The collected data is split into two collections, one including 937 samples (representing 75% of the total) and the other containing 402 samples (representing the remaining 25%). (representing 25 percent). When comparing the decision tree with the polynomial support vector machine (SVM), the decision tree performed better, with values of 78.36%, 80.66%, and 79.54% for accuracy, recall, and f-score, respectively, while the innovative polynomial SVM performed worse, with values of 63.63%, 70%, and 66.66%. P0.001 is determined to be the threshold of significance. Based on the calculated f-score, we may conclude that the decision tree outperforms the novel polynomial SVM. [ABSTRACT FROM AUTHOR]

There are a number of technologies that might help farmers increase yields and decrease losses, but two of the most important are precision farming and accurate weather forecasts. Despite this, these systems need precise, up-to-date information on a wide range of factors, including soil quality, weather forecasts, and crop health. When it comes to potential destruction, cyclones are among the most concerning, and they may also have a major effect on farming methods. We can get a deeper understanding of the correlation between cyclones and crop yields by building and testing a custom-made convolutional neural network (CNN) using historical cyclone data. Eventually, this data might be utilised to create more accurate weather forecasts and precision farming algorithms to benefit farmers. Using this data, we can develop these cutting-edge algorithms. To train and verify the proposed framework, we used an existing dataset that includes cyclone data from the past. Compared to state-of-the-art approaches, the suggested architecture outperformed them significantly in terms of accuracy and precision. [ABSTRACT FROM AUTHOR]

This research endeavours to evaluate the efficacy of the support vector machine (SVM) method against that of the K-Nearest neighbour Network (KNN) approach in removing thick fog from images. We gathered the Realistic Single Image Dehazing (RESIDE) dataset from the kaggle.com database. For each group's sample size of 22, the suggested SVM method is compared against the current KNN techniques. There are 44 samples altogether. Our suggested process include measures to actually accomplish the elimination of noise in photos. Metrics of performance, such precision and sensitivity, are computed. The suggested support vector machine (svm) has an accuracy and sensitivity of 94.9 and 38.5%, respectively, whereas k-nearest neighbour had 93.7 and 37.5%. Using the G power instrument and setting the minimum power to 0.8, we can determine how many samples are needed for this study. When comparing the SVM and KNN algorithms, the significance level reached was (p 0.05). Support vector machines provide far superior performance over the standard k-nearest neighbour, as shown both by experimental findings and Independent sample T-test statistical analysis using SPSS software. [ABSTRACT FROM AUTHOR]

In this study, we compare the performance of two different algorithms for recognising irises: the K-Nearest Neighbor (KNN) strategy and the Support Vector Machine (SVM) approach. The Components and Procedures: Training and test sets were created from the whole research sample. In order to enhance the accuracy rate of the KNN and SVM algorithms, samples were collected from 100 people, with 5 eye scans from each individual (Group 1=250 and Group 2=250). KNN has a 93.34% accuracy rate, whereas SVM achieves just an 89.735% success rate. KNN has a 94.876 percent Precision rate, whereas SVM only has an 89.032 percent Precision rate. Recall for KNN is 94.298 percent, while recall for SVM is 90.123 percent. G-Power is assumed to be 0.8 due to the statistically significant discrepancy in accuracy rates (P=0.0427). The results of the iris recognition system show that the KNN algorithm is superior than the SVM technique. [ABSTRACT FROM AUTHOR]

The primary objective of this research is to diagnose lung cancer more accurately utilizing the Support Vector Machine (SVM) method and K-Nearest Neighbors classification (KNN) technique. Materials and Methods - SVM was used in this study to detect lung cancer using 10 samples (N=10), while KNN was used to classify the samples using 10 samples (N=10). The accuracy of the two approaches is contrasted. Results: The SVM method outperforms the KNN approach in accuracy (96% vs. 68%). Conclusion - It is clear from the results of the experimental testing that SVM performs better on lung imaging datasets than existing classifiers. The findings indicate that, for the identification of lung cancer, the SVM algorithm appears to be significantly better (p<0.05). [ABSTRACT FROM AUTHOR]

In this investigation, we compare the performance of the state-of-the-art ANN algorithm for vehicle detection with the more conventional SVM algorithm. Twenty representatives were selected at random from the database of the website. The threshold was kept at 0.05, the confidence level at 95%, the enrollment ratio at 1, and the G power at 80%. Accuracy metrics are calculated using the SPSS software. Results from an independent t test showed that the ANN algorithm outperformed the SVM algorithm, with a mean accuracy of 95%. The difference between the two groups was not statistically significant (p=0.11, p>0.05). In this research, it appears that the ANN algorithms perform better than the SVM algorithms when it comes to detecting vehicles in the datasets used. [ABSTRACT FROM AUTHOR]

The goal of this study is to use Support Vector Machines (SVMs) to recognise handwritten characters and compare their predictive accuracy to that of Logistic Regression. In this paper, we explore the application of machine learning algorithms like Support Vector Machine and Logistic Regression to the problem of character recognition. For a 0.05 level of statistical significance, we need 15 samples out of a total of 30. The SVM algorithm achieves a higher recognition rate (96.58 percent) than the Logistic Regression algorithm (87.43 percent) when both are used for character recognition. A p-value of 0.005 indicates a statistically significant difference between the study groups. Support Vector Machine was found to have better prediction accuracy than the Logistic Regression algorithm in this study of character recognition. [ABSTRACT FROM AUTHOR]

False news is characterized as a made-up story with a goal to bamboozle or to delude. In this paper we present the answer for the errand of fake news discovery by using Deep Learning structures. Fake news identification is carried out utilizing two AI calculations, Natural Language Processing Algorithm(N=10) and Deep Learning algorithm(N=10) calculations. False and True these two sorts of dataset is utilized for Fake news recognition, and it is gathered from kaggle.com. Dataset comprises lines and 6 fundamental boundaries that are connected with the False news that information gathered from twitter. For each gathering more than 30 examples are taken, and it is separated into preparing and testing. Accuracy for Natural Language handling calculation is 91.300% and for Support Vector Machine calculation is 77.500%. There exists an insightful critical distinction between Natural Language Processing Technique and Support Vector Machine calculations with p<0.05. Fake news location utilizing Natural Language Processing calculation seems to acquire higher precision than the Support Vector Machine calculation. [ABSTRACT FROM AUTHOR]

The primary objective is to carry out the identification of phony news discovery around the web-based entertainment with the proposed Natural Language Processing contrasted and Support Vector machine Algorithm. Counterfeit news discovery is executed utilizing two AI calculations, Natural Language Processing Algorithm(N=10) and Support Vector Machine(N=10) calculations. Phony and True these two kinds of dataset is utilized for Fake news discovery, and it is gathered from kaggle.com. Dataset comprises of columns and 6 principal boundaries that are connected with the phony news that information gathered from twitter. For each gathering 20 examples are taken, and it is partitioned into preparing and testing. Exactness for Natural Language handling calculation is 91.300% and for Support Vector Machine calculation is 72.700%. There exists a logical huge distinction between Natural Language Processing Technique and Support Vector Machine calculations with p<0.05 Fake news recognition utilizing Natural Language Processing calculation seems to acquire higher precision than the Support Vector Machine calculation. [ABSTRACT FROM AUTHOR]

This research evaluates the performance of linear regression (LR) and support vector machines (SVM) with a custom kernel Trick for predicting the availability and demand of workers in the public sector industrial construction industry. Methods and materials: In this research, we evaluate Linear Regression against Support Vector Machine models. Timely public sector industry construction was verified by testing the regression models. There are 52 participants in each category. The laboratory experiments resulted in a two-method ensemble classifier with an average value of 2.97. Support Vector Machines (90.06%) significantly beat Linear Regression (64.02%) in accuracy, as determined by a two-tailed independent sample T test, with a significance level of P=0.001 (P 0.05). In conclusion, the suggested SVM version forecasts labour requirements for future generations of production. Employee happiness levels on a daily basis are crucial to the veracity of this version. [ABSTRACT FROM AUTHOR]

The aim of this research is to use the Decision Tree classifier to identify lung cancer in scanned pictures and compare its performance to that of the Support Vector Machine classifier. Methods and materials The lung cancer database contains information on 60 samples (patients). Clincalc's sample G power has two groups, with alpha (0.05), power (80%), and enrollment ratio (n1 = 30) for group 1 and (n2 = 30) for group 2. Decision Tree outperforms standard SVM classifiers in terms of accuracy, with a 95.13 percent success rate. According to the results of this study, the decision tree method is more accurate than the Support Vector Machine classifier. [ABSTRACT FROM AUTHOR]

The objective of this research is to predict the loan sanction by comparing accuracy using Support Vector Machine (SVM) and K-Nearest Neighbour algorithm (KNN). Materials and Methods: Total sample size of this study is 40. Group 1 has 20 samples for KNN and group 2 has 20 samples for SVM. These groups were analyzed by an independent sample T-test with a pretest power of 80%. Results: KNN algorithm achieves accuracy of 70.90% and SVM achieves accuracy of 75.17%. The obtained significance value is 1% which is less than 5%. Conclusion: SVM achieves significantly better accuracy when compared with KNN algorithm. [ABSTRACT FROM AUTHOR]

Emotion classification utilising Support Vector Machine (SVM), Hidden Markov Model (HMM), and Random Forest (RF) classifier extraction from EEG signals is the focus of this research. The Components and Techniques: Emotion detection in EEG signals was analysed and compared using the Support Vector Machine (SVM), Hidden Markov Model (HMM), and Random Forest (RF) classifier programmes. These numbers were calculated with a MATLAB script. The performance of HMM, SVM, and RF classifiers was compared using 10 samples from each group. The sample size was determined by keeping the following parameters constant: G-power 80%, =0.05, and a 95% confidence interval. The study's results show that the HMM classifier is superior to the RF and SVM classifiers in detecting emotions from EEG data and in terms of overall performance. SVM has an average value of 62.2%, HMM has an average value of 75.6%, and RF has an average value of 30.6%. When using data features, the HMM classifier has an accuracy rate of 35%. The results of this study reveal that the HMM classifier has a higher accuracy level (35%) in recognising emotions using the EEG signal than the RF classifier (10%) or the SVM classifier (29%). The HMM classifier was shown to be significantly more effective than the RF classifier and the SVM classifier, with a P value of.001. [ABSTRACT FROM AUTHOR]

The goal of this research is to catalogue all of the undesirable materials available on rival and popular social media platforms. Tools and methods: The dataset for training and testing the proposed prediction models was constructed using the messages from a number of popular social media messaging platforms, with a minimum of 5 attributes and 150 messages. Random Forest, a machine learning algorithm, was used to design the framework, and it was compared favourably to the Support Vector Machine algorithm. Discussion and Remarks: Accuracy in retrieval is 90.30 percent for the Random forest algorithm and 94.36 percent for the Support vector machine learning algorithm (countvectorizer). The significance level between the two algorithms is high at p=0.02 (p0.05). This study confirms that the SVM (countvectorizer) Machine Learning algorithm has a higher accuracy rate than the Random Forest algorithm and creates a novel framework to identify spam words. [ABSTRACT FROM AUTHOR]

To improve the license plate recognition accuracy rate using Inductive Learning (IL) classification using machine learning in comparison with Support Vector Machine (SVM) classifiers. Materials and Methods: The sample size of license plate recognition system with improved recognition rate was sample 2000 (Group 1 = 1500 and Group 2 = 1500). Comparative analysis of license plate recognition systems with improved recognition rate is performed by Support Vector Machine (SVM) whereas number of samples (N = 30) and Inductive Learning (IL) Classifier where number of samples (N = 30) techniques. Results: The accuracy rate of Support Vector Machine (SVM) is 96.34% whereas results of Inductive Learning (IL) accuracy rate are 91.87%. The Specificity rate is 96.43% for Support Vector Machine (SVM) whereas the results of Inductive Learning (IL) Specificity rate are 92.23%. The Sensitivity rate is 96.45% for Support Vector Machine (SVM) whereas results of Inductive Learning (IL) Sensitivity is 93.12%. There is a significant difference in accuracy rate with p value 0.0 in 2 tailed test (p<0.05). Conclusion: Support Vector Machine (SVM) Classifier predicts the better classification in finding the accuracy, Specificity, sensitivity for analysis of license plate recognition system with improved recognition rate when compared to Inductive Learning (IL) Classifier. [ABSTRACT FROM AUTHOR]

After lung cancer, prostate cancer is the most common form of cancer among men. World Cancer Research Fund International estimates that 1,414,259 people will be diagnosed with cancer in 2020, with 375,304 people losing their lives to the disease [1]Predicting the onset of Prostate Cancer at an early stage is, thus, essential. One of the major problems in Pca is diagnosing the disease as it does not show any early signs or symptoms. There are various diagnosing techniques for Pca. These techniques are very painful for the patients; one is DRE (Digital Rectal Examination). This is an ideal situation for using machine learning techniques to find an answer. Various supervised learning techniques (such as Random Forest, K-nearest Neighbor, SVM (Support Vector Machine), Logistic Regression, MLP (Multi-layer Perceptron), RNN (Recurrent Neural Network), and Naive Bayes) will be compared and contrasted in this study to make accurate predictions about prostate cancer. The primary goal of the research is to identify the most effective strategy for the prediction of prostate cancer for future use. Data for the study was used from the open-access online site of 100 patients. These Methods were evaluated basedon their precession, F1-Score, accuracy, recall and AUC. The results show that MLP, Logistic Regression, RNN performed well with 93% accuracy. Therefore, we may conclude that this method has huge benefits for the detection of prostate cancer. [ABSTRACT FROM AUTHOR]

Early Parkinson's disease (PD) diagnosis is essential for preventing the illness's development and providing patients with the opportunity to take disease-specific therapy. Speech signal processing is widely used and has gained attention recently. In this project, we carry out a comparative analysis for effective dysphonia (a speech disorder) and Parkinson's disease spotting using Machine Learning methods. To distinguish between PD patients and normal individuals. We used Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LREG), and K-Nearest Neighbor (KNN) algorithms to demonstrate a robust detection process. According to experimental findings, the KNN outperformed other ML algorithms. The results of the trial conducted at UCI contain data from 31 patients, 23 of whom were diagnosed with PD. After implementing these algorithms, we found that our accuracy rates for these algorithms are NB (86.95%), RF (85.77%), SVM (79.18%), LREG (91.56%), DT (87.16%), and KNN (97.67%). [ABSTRACT FROM AUTHOR]