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During the era of web 4.0, social media has become a popular trend among internet users. People use social media platforms to share their thoughts and emotions, seeking support from their peers. However, this also exposes them to privacy threats from certain individuals. Cyberbullying is a significant issue in this context, referring to attacks that occur on social media platforms through comments or tweets. The consequences of cyberbullying can be severe, leading to depression, anxiety, and other forms of self-harm. To gain a deeper understanding of this problem, a comprehensive survey was conducted, focusing on the contributions of various authors. This survey primarily examined different machine learning techniques and pre-processing techniques, utilizing data from popular sources between 2015 and 2023. The findings of this survey indicate that the most commonly employed methods in cyberbullying research are tokenization processing and support vector machines. [ABSTRACT FROM AUTHOR]

A study on texture-based classification of ultrasound breast cancer images using machine learning demonstrates a promising approach to automate breast cancer diagnosis. This research introduces a computer-aided diagnostic (CAD) system for categorizing breast images into benign and malignant tumors. Machine learning techniques are utilized to extract texture features and classify images based on these features. Evaluation metrics such as accuracy, precision, sensitivity, specificity, and F1-Score are employed to assess performance, with confusion matrices created for each metric. Support Vector Machine (SVM) classifiers with various kernels are employed due to the complexities of medical images, achieving an average accuracy of 95%. The findings suggest that the proposed CAD system could effectively aid radiologists in distinguishing between benign and malignant tumors. [ABSTRACT FROM AUTHOR]

This research focuses on assessing and comparing different machine learning algorithms to identify the most effective one for pneumonia detection. The dataset utilized consisted of 5840 images. Several classification algorithms, including K-nearest neighbor (KNN), Logistic regression, Random Forest, Decision tree, and Support Vector Machines (SVM), wereemployed to evaluate their performance. The resultsindicated that the KNN algorithm exhibited the highest performance, achieving an accuracy rate of 83%. [ABSTRACT FROM AUTHOR]

Polycystic ovarian syndrome is one of the major diseases hampering the fertility of women between the age of 15 to 50. The PCOS prone women are affected both mentally and physically, which claims difficulty to conceive. There are several ways to detect this disease, but untimely detection can reduce the effect of it. we are presenting an established model for the early detection of PCOS applying CNN. The previous models built utilizing machine learning produces an accuracy of 89%, Fuzzy TOPSIS provided an accuracy of 98% and SVM (Support Vector Machine) algorithm provided an accuracy of 94%. But the newly established model utilizes the ultrasound images of the ovary to detect the cysts that are formed and classifies into infected and non-infected. The conducted research extracts the feature from the ultrasound images which are taken out from the Kaggle repository and the outcome is measured in terms of accuracy since classification is the problem basis. This emphasized model provides the best accuracy among all the previously developed models. The research is done with the motto of enhancing the sexual health of women and to provide better methods for detection in the region of biomedical technology. [ABSTRACT FROM AUTHOR]

The main objective of the research is to evaluate the effectiveness of two methods for human behavior recognition forecasting: one uses an artificial neural network technology, and the other uses support vector machines. To determine the sample amount for the expected result of human behavior recognition using the artificial neural network approach, we utilize G-power 0. 8 using an alpha of 0.8 and a confidence limit of 95%. Group 1 has 25 participants and Group 2 has 25. While Support Vector Machine achieves a precision of 91.8469, New Artificial Neural Network achieves 97.3989. A newly developed artificial neural network significantly surpasses support vector machines from incorporating the top aspects of both approaches. [ABSTRACT FROM AUTHOR]

A study that pits a novel recurrent neural network-based cyberbullying inference approach against a support vector machine classifier reveals that the new method outperforms the latter. Approach and Substances Employed: This strategy is used by two firms. Twenty instances were assigned to each group so that they could test their cyberbullying deduction skills. While Group 1 made use of recurrent neural networks, Group 2 opted to employ support vector machines. We estimated the sample size using a fixed pretest power of 80% and a G power of 80%. In terms of accuracy, the data shows that RNNs are 94.43% better than support vector machines. There is a statistically significant difference, as shown by the p-value of 0.18 (p>0.05). The results show that recurrent neural network techniques are much superior than support vector machine classifiers. [ABSTRACT FROM AUTHOR]

The main objective of this study is to create a unique Naive Bayes (NB) classifier that can predict ischemic strokes better than cutting-edge Support Vector Machine (SVM) techniques. Twenty Support Vector Machines (SVMs) and twenty naïve Bayes classifiers are compared in this study. The G power technique was used to calculate the total sample size, with an allocation ratio of 1 and an alpha of 0.05. While the recommended method achieves 98% success rate, the support vector machine achieves 90%. The statistical analysis indicated that the specificity p-value was 0.010 and the accuracy p-value was 0.042. When it comes to brain stroke identification, the novel Naive Bayes classifier performs better than support vector machine classifiers. [ABSTRACT FROM AUTHOR]

The purpose of this study was to compare the effectiveness of two distinct neural network architectures—a support vector machine (SVM) and a customized convolution neural network (CNN)—in brain MRI imaging and stroke scan categorization. The results showed that accuracy and precision improved when the CNN was used. In this study, two groups are compared: one using a support vector machine (N = 20) and the other a convolution neural network (N = 20). We used power software to compute the total sample size using the following parameters: 98% pre-test power, 0.05 alpha, 0.1 enrollment ratio, and 95% confidence interval. The recommended solution outperforms the alternative, according to assessments utilizing the most advanced Convolution Neural Network (98%) and Support Vector Machine (89%). After using SPSS to analyze the data, we discovered that the specificity (p = 0.010) and accuracy rate (p = 0.044) had significant statistical significance. The novel Convolution Neural Network classifier significantly outperforms Support Vector Machine classifiers in the diagnosis of brain strokes. [ABSTRACT FROM AUTHOR]

In this study, design of an Innovative Intrusion Detection System using Naive Bayes (Group 2) as compared to the efficiency of SVM, (Group 1). Naive Bayes and Support Vector Machines were the two intrusion detection systems (IDS) used by the researchers. The research uses a total of 38 samples for evaluation, including 19 samples in each group. We used the SPSS program for statistical analysis. A sample size of 80% was determined by combining G power with the pretest power. Using SVM to improve IDS accuracy by a certain percentage is a statistically significant improvement (p = 0.007). The typical level of precision of an Innovative Intrusion Detection System using Naive Bayes (Group 2) is 62.26% and with SVM (Group 1) is 98.73%. The Innovative Intrusion Detection System is more effective than the Naive Bayes-based system since it uses Support Vector Machine (SVM). In any case, it's crucial. [ABSTRACT FROM AUTHOR]

The study's goal is to use Decision Tree (DT) and Making the Most of Structural Vector Machines on Accuracy haze removal under diverse weather circumstances (SVM). Materials and procedures: This experiment's data set consists of 30 photos for various techniques of haze reduction in remote sensing photographs. The sample size determined by Gpower is 20 per group. The classifier's accuracy and precision are tested and reported for both Decision Tree and SVM. Results: When the independent sample the T-test's two-tailed p-value of 0.023 (p0.005) demonstrated statistical significance. When compared to the Support Vector Machine method, the Decision Tree method fared better. technique in terms of accuracy (80% vs. 80%). 85.5%. haze removal analysis under different weather situations. [ABSTRACT FROM AUTHOR]

Group 2 will use one class of support vector machines (SVMs) to build an intrusion detection system, and Group 1 will use support vector machines (SVMs) to compare the two groups' performance. Two types of intrusion detection systems (IDS) are covered in the study; one uses a support vector machine (SVM) method, while the other is an innovative IDS. There are a total of 38 samples used to assess the research, with 19 samples in each group. The statistical analysis was conducted using the SPSS utility. Using G power and a pretest power of 80%, we determined the sample size. The high degree of significance (p<0.05) demonstrates the outstanding functionality of the IDSs. The accuracy of One class SVM IDS has been significantly enhanced, with a value of 0.0 (p<0.05), as shown by the percentage. Accuracy mean for Group 2 using one-class support vector machines (SVMs) is 0.9946, whereas Group 1's mean accuracy with an innovative intrusion detection system employing SVMs is 0.9853. The results show that when comparing the two types of intrusion detection systems, the one based on one class support vector machines (SVMs) performs far better. [ABSTRACT FROM AUTHOR]

To be more specific, we are interested in comparing the accuracy of Support Vector Machine with a novel artificial neural network classifier when it comes to fingerprint verification. We compare the new method of artificial neural networks to an SVM. Following evaluating the suggested procedure with a combined total of fifteen samples in both groups, the G power value stands at eighty percent. When it comes to identifying biometric identity, the Support Vector Machine technique only manages 83% accuracy; in contrast, the Novel Artificial Neural Network technique approach achieves an impressive 87% accuracy. With a protest power analysis completed at 85% and test sizes of 15 for one group and 30 for the overall set, the Novel Artificial Neural Network approach outperformed SVM marginally in terms of mean accuracy and standard deviation (1.25369 vs. 1.30700). The Novel Artificial Neural Network Method (1.25369) outperforms the SVM method (1.30700) with a significant value of 0.01 (p<0.05) in terms of mean exactness and standard deviation, although the SVM algorithm (83.10%) ranks somewhat lower. The results demonstrated that when it came to biometric expectancies, the Novel Artificial Neural Network Method Algorithm outperformed the SVM algorithm. [ABSTRACT FROM AUTHOR]

To determine how much certain cardiovascular events would cost over various time periods for specific patient subsets and clinical quality indices, we developed cost expectation criteria specifically for this research. Novel Linear Regression and Support Vector Machine may be used to correctly predict the occurrence of cardiovascular illnesses. For every classifier, we have an N=10 sample size. Novel Linear-Regression achieves an accuracy rating that is 94.6% more than Support Vector Machine's 90.7%. The two groups are statistically distinct from each other, according to the findings of an independent sample T test (P=0.00, p < 0.05). When compared to a support vector machine, Novel Linear Regression is determined to be the more accurate method in this article. [ABSTRACT FROM AUTHOR]

This study evaluates two machine learning methods for improved fruit disease diagnosis: one that uses innovative convolutional neural networks and the other that prioritizes accuracy and sensitivity improvement. From the many fruit disease photographs on Kaggle, three thousand samples were chosen. For the sake of testing, two datasets will be utilized: one containing 800 photos (or 30% of the total) and another including 1800 shots (or 70% of the total). Honesty, precision, and reliability by doing numerical calculations, we may assess how well the convolution neural network approach works. The novel CNN produced 97.5% accuracy, 5.1% sensitivity, and 99.4% precision, as opposed to the SVM method's 83.3% accuracy, 87.9% sensitivity, and 88.3% precision. For both groups, the p-value is 0.000. It seems that the Novel CNN approach outperforms the SVM method when it comes to detecting fruit quality. [ABSTRACT FROM AUTHOR]

This research intends to learn how road design affects the national highways in Tamil Nadu by using a support vector machine strategy and an innovative K-Nearest Neighbor ensemble technique. Using a support vector machine, the study is carried out using a sample size of 10. Run a small set of 10 items through the Hive Algorithm. The G-power calculation takes into consideration and tests two groups: the power group (80% of the total) and the environment ratio. The value of alpha is 0.05. The results: The K-Nearest Neighbor Algorithm achieved an impressive success rate of 92.7%. There seems to be a statistically significant difference between the support vector machine method and the new K-Nearest Neighbor ensemble technique (p=0.000). According to our findings, the accuracy with which we can ascertain the impact of road design in Tamil Nadu on the frequency of traffic-related injuries and accidents is substantially affected by various K-Nearest Neighbor Algorithm techniques using varying seed values. [ABSTRACT FROM AUTHOR]

For innovative categorization of water quality monitoring, supervised machine learning approaches were applied. The aim of this research work is to evaluate the performance of SVM in novel classification of normal water and polluted water by comparing with the k mean. A Dataset of 800 water samples were downloaded from Kaggle website. The water samples are divided into two folders, normal and polluted water each containing 300 samples and a variety of different resolutions. 300 water samples used for SVM (Group 1) and it is compared with k mean (Group 2). The dataset is split into two sections: training (70%) and testing (20%). Accuracy, precision, and sensitivity values are calculated using a confusion matrix to quantify the classification potential of the SVM algorithm. 0.8 is the G power. SVM algorithm has achieved accuracy, precision and sensitivity of 89.60%, 87.53% and 94.62% respectively in comparison to K Means algorithm of 84.26%, 81.38% and 90.17%. SVM has a significantly higher classification performance (p = 0.018) compared with k mean. The Support Vector Machine method outperforms the k mean approach in the categorization of water samples, according to this study. [ABSTRACT FROM AUTHOR]

The primary objective of this research is to compare and contrast the two methods used for flood detection, namely the logistic regression method with the support vector machine. The 118-sample data set used in this investigation includes 70 samples for training & 48 samples for testing. A total of 80% G-power and 0.05 alpha were used to establish the sample size. At a significance threshold of 0.000039 (p < 0.05), the support vector machine acquires an accuracy of 75.48% with logistic regression an accuracy of 88.58%. In terms of detecting areas that have been flooded, the results show that Novel Logistic Regression is much more effective than Support Vector Machine. [ABSTRACT FROM AUTHOR]

The goal of this study is to evaluate the performance of K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) in facial picture recognition. Forty people make up the whole sample for this research. For KNN, there are 20 samples in Group 1, while for SVM, there are 20 samples in Group 2. A pre-test power of 80% and an alpha value of 0.05 were used to analyse these groups using an independent sample T-test. A sensitivity level of 89.65% and an accuracy of 84.16% are achieved using the novel K-Nearest Neighbour algorithm. The sensitivity level is 87.53% and the accuracy is 83.32% while using Support Vector Machine. According to this research, the significance values for accuracy were 0.01 and sensitivity was 0.03 (p<0.05). When pitted against SVM, KNN produces much superior results in terms of sensitivity and accuracy. [ABSTRACT FROM AUTHOR]

The major goal of this work is to propose a system that can efficiently read robot diners' facial expressions for feedback. The suggested research built its system using the FER2013 dataset. An innovative kernel trick technique is used to improve the classifier's performance in this research, which is based on a support vector machine approach. This proposed support vector machine technique calculates the rating based on the customer's facial expression. This study considers two sample groups, each having a sample size of 392. The proposed support vector machine method outperformed the previous system by achieving a greater accuracy of 82.14% than the deep reinforcement learning technique with 60.41%. The results clearly suggest that the Improved support vector machine outperforms the Deep reinforcement algorithm in terms of accuracy. [ABSTRACT FROM AUTHOR]

The Fully Convolutional Network (FCN) and the Support Vector Machine (SVM) are two well-known classifiers that are used for MRI-based novelty liver segmentation classification. This study aims to compare and contrast these two classifiers. To train classifiers using SVM and FCN, twenty samples of liver MRI images were used. Two groups, one using support vector machines (SVMs) and the other using FCNs, were given ten samples each. This study's pretest power is 80%. In MATLAB, FCN has a recognition rate of 97% compared to 86% for SVM. Our statistical study yielded a satisfactory accuracy ratio (P<0.05). Compared to the SVM method, the FCN method achieved superior results on the datasets tested for unique liver segmentation categorization. [ABSTRACT FROM AUTHOR]

To detect accuracy in Fake reviews using Novel A study using Research using Support Vector Machine and Random Forest. What you are research incorporates both new SVMs and RFs, or Random Forests. Our research parameters are as follows: beta=0.2, power=0.8, and alpha= 0.05. 10 participants in each group. Furthermore, we assess their accuracy using various sample sizes. Whereas Random Forest only had a 78.24% success rate, the Novel Support Vector Machine managed a whopping 98.47%. Both the performance and loss metrics have a at the 0.376 level of significance (p>0.05), it is statistically significant. Compared to the RF, the SVM model does far better in detecting fraudulent reviews. For the purpose of identifying disingenuous news reports, it may be your best bet. [ABSTRACT FROM AUTHOR]

The main objective of the research is to evaluate the efficacy, specificity, and sensitivity of two approaches—a support vector machine (SVM) and an artificial neural network (ANN)—in differentiating between benign and malignant lung tumours. For this investigation, data sets were acquired from Github. For the ANN and SVM, a total of 40 lung CT scans were employed. With a review course power of 0.8 and a maximum acceptability error of 0.5, the sample size in each group is examined. When used to identify cancers in lung CT images, Artificial Neural Networks perform with an accuracy of 92%, whereas Support Vector Machines do better with an accuracy of 84% and a significant level of p 0.05. When it comes to distinguishing between benign and malignant lung tumours, the artificial neural network technology outperforms the Support Vector Machine method in terms of overall accuracy, sensitivity, and specificity. [ABSTRACT FROM AUTHOR]

To forecast the categorization of digital handwriting images, this study employs two classification algorithms: K-Nearest Neighbors (KNN) and Nova Support Vector Machine (SVM). Using a Digit dataset consisting of 935 items, the classification method is evaluated. Our suggested method makes use of support vector machines (SVMs) and kernel neural networks (KNNs) to predict numerical values typed by hand. Using G power, we split the staff into two 32-person teams. The clinical trials sample size was determined using the following criteria: 90 percent confidence, 0.5 beta, alpha of 0.05, pre-test G power of 80%, enrollment ratio of 1, and beta of 0.5. Compared to KNN, the Novel Support Vector Machine classifier has a success rate of 94.01% when predicting the Handwritten Digit classification on the dataset. There is a notable difference between the two groups, as shown by a p-value of 0.002 (p<0.05) on the 95% confidence interval. This is why SVM classifiers outperform its kernel neural network counterparts. The findings demonstrate that compared to KNN, SVM provides superior accuracy. [ABSTRACT FROM AUTHOR]

Building two intrusion detection systems (IDSs) is the goal of this project so that wireless ad hoc networks may be protected against denial-of-service assaults. A Bayesian network and a novel one-class support vector machine (SVM) will be used by one system for feature selection, while the other will depend on information gain. What You'll Need and How to Do It: In order to create an IDS model, the 2017 CIC-IDS dataset was used. We utilize SPSS to examine the results after modeling the IDSs using the aforementioned methods, with 19 instances in each group. Finally, a combination of an Information Gain Algorithm-based intrusion detection system with a one-class support vector machine (SVM) using Bayesian network feature selection led to a significant increase in discovery rates and accuracy. The latter was the product of original thought, whereas the former attained 82% and 88%, respectively. Since the significance level is less than 0.05 and the detection rate and accuracy are both 0.00, it may be deduced that IDS is successful. The findings from the comparison between the two types of IDS point to the Information Gain Algorithm-based and the Bayesian network feature selection-based systems as being the better choices. [ABSTRACT FROM AUTHOR]

The main goal of this research is to increase the accuracy of the loan prediction over the Random Forest approach. The most recent results used data collected from several sources, a 0.05 percent threshold, and a 95% confidence range that includes the mean and standard deviation. Using an 80% G-power estimate, two distinct sets of algorithms are combined to get the classification for n = 10 samples. The Random Forest approach only scores 75.60%, whereas the Novel Support Vector Machine achieves a high accuracy score of 79.00%, according to the study's findings. Between the two groups, there is a little difference of 0.001, or less than 0.05. Compared to Random Forest, Support Vector Machine generates estimates of consumer loans that are more accurate. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to compare the Adaboost method with the Conventional SVM technique in order to provide an accurate prediction about whether or not the fluid is fit for usage by humans. Two sets of twenty samples each were used for this study, totaling forty data. For group 1, we have SVM, while for group 2, we have Adaboost. With a prior test efficiency of 80% and an alpha value of 0.05, the sampling is computed using the results of current research as well as the system clincalc.com. According to the workflow to implement the support vector machine (SVM), the code is implemented and compiled in the Anaconda software with the Jupyter Notebook installed and launched successfully. The proposed work support vector machine (SVM) has got a higher accuracy of 90.75% whereas the existing Adaboost classifier has got an accuracy of 75.14%. The significance 0.000 obtained is less than 0.05 value. Therefore, the suggested study concludes that, when comparing the precision of water quality, SVM outperforms the current classifier, the Adaboost classifier. [ABSTRACT FROM AUTHOR]

The classification of data is a crucial machine learning activity. The No Free Lunch Theorems assert that no single algorithm can provide the best results for all classification issues. The persistent homology classification algorithm, or Persistent Homology Classifier (PHC), is a new classification technique based on persistent homology (PH) developed to perform classification tasks involving various datasets, including image datasets. It is a supervised method that utilizes the lifespan of the topological features formed through the filtration process of each point cloud of observations with the same classes. Due to PH's robustness, the insertion of a new observation from the same class will not significantly alter the lifespans of the formed topological features. This was established as the criterion for determining the new observation's class. PHC's performance in categorizing the fashion MNIST image dataset has been compared with that of other well-known classifiers. The feature of each image was extracted using Histogram of Oriented Gradients (HOG) and was reduced using Principal Component Analysis (PCA). The research demonstrates that PHC outperformed classification and regression trees (CART), was comparable to k-nearest neighbors (KNN) and random forests (RF) but underperformed to support vector machines (SVM) and linear discriminant analysis (LDA). Additionally, PHC outperforms RF and CART in terms of accuracy. Moreover, an increase in dataset size indicates a significant improvement in the accuracy of PHC. In general, the study shows that PHC can satisfactorily perform image classification tasks. [ABSTRACT FROM AUTHOR]

Traditional bearing sensory diagnostic include touching and hearing rely on personal experience, and for more complex system are unable to meet the needs of equipment fault diagnosis. The research on bearing fault diagnosis is developing significantly. Bearings are used in rotating machinery and most machinery failures are caused by bearing failures. The fault diagnosis of bearings is an important research area. The core of bearing fault diagnosis is the pattern recognition of fault features. The key of pattern recognition is to develop a reasonable classifier. Intelligent pattern recognition has been developed such as principal components, support vector machine, neural network. In this study, a bearing fault diagnosis based on exploring pattern recognition is proposed. The key to pattern recognition is to design a significant classifier. A number of features from bearing vibration of normal and fault bearing are extracted and processed using principal components of correlation matrix. Plot of principal components shows the visualization of normal and fault bearing and the classifier is chosen subjectively. The principal components exploration will be confirmed using least squares support vector machine. The parameter of support vector machine estimated using heuristic optimization particle swarm optimization. The proposed method can be applied in the detection of faults of bearing. [ABSTRACT FROM AUTHOR]

One of the leading causes of death is cardiovascular disease (CVD). This disease is the cause of 31% of deaths worldwide in 2016, and 85% of them are heart attacks. The traditional way to detect CVD is based on medical records and clinical analysis of the patient. Electrocardiogram (ECG) analysis is one way to determine irregular heartbeat or arrhythmia. Computer assistance with implementing specific machine learning algorithms can help recognize irregular heartbeats automatically. However, raw ECG data may contain noise that affects the accuracy of irregular heartbeat detection. In this study, the ECG data used was from the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) database. The data has four categories: Normal, Atrial Fibrillation, PVC Bigeminy, and Ventricular Tachycardia. ECG raw data processing using multilevel discrete wavelet transforms (DWT) based on Haar and Daubechies wavelet. The process uses various values of mode (i.e. db1 until db10), level (i.e. level 1 and level 2), and filter (low and high pass filter), and the result is 20 data processed. Each data is used to create a model with several classification algorithms, i.e. K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, and Deep Forest. The validation process uses 10-fold cross-validation. The results of this study indicate that Multilevel Discrete Wavelet Transforms improve irregular heartbeat detection accuracy when compared to raw ECG and processed data using a single DWT. While the best detection accuracy is the Deep Forest model, with an accuracy value of 63.57% using processed data with db1 mode values, level 2 and combining high and low pass filters. [ABSTRACT FROM AUTHOR]

The objective of this study is to improve the accurateness of the skin disease prediction using ML algorithm by comparing SVM and Extreme Gradient Boosting (XGBOOST) models. Skin disease prediction models developed using ML method have improved the accuracy of diagnosis. The methods currently available for building accurate skin disease prediction models require extensive data collection. This study compared the enactment of two popular ML algorithms, SVM and XGBOOST, in predicting skin disease using a dataset obtained from Kaggle consisting of clinical and demographic variables. The dataset was preprocessed, and both algorithms trained and tested both algorithms and found that XGBOOST outperformed SVM. The findings of this study indicate that machine learning algorithms have the capability to provide precise predictions of Skin disease. The outcomes of the autonomous samples t-test indicated that the mean accurateness of XGBoost was expressively higher than the mean accuracy of SVM (p < 0.001, p < 0.05), a statistically noteworthy variation between the algorithms related to classification accuracy. By this learning accurateness of skin disease prediction is improved while analyzing the results of two ML algorithms SVM and XGBoost. The study compares the accuracy of both algorithms using a dataset. The conclusion is that the proposed model of XGBOOST 94% outperformed the SVM 59.6% within this dataset. [ABSTRACT FROM AUTHOR]

The goal of this research is to improve the accuracy of semiconductor device health prediction by comparing the efficacy of Enhanced CNN with the Support Vector Machine technique. Support vector machines and Novel Enhanced CNN were the two groups that were used. The parameters were a G-Power of 80%, an alpha of 0.05, and a beta of 0.2. Two iterations were conducted for each group, for a total of 40 iterations and a sample size of 1641. Independent T-tests confirmed the significance of the hypothesis at a level of p = 0.003 (p < 0.05). Based on statistical study with SPSS, the Novel Enhanced CNN and the Support Vector Machine Algorithm had accuracy of 97.10% and 95.85%, respectively. These noteworthy accuracy numbers imply that the Enhanced CNN strategy (97.10% accuracy) works better than the Support Vector Machine methodology (95.85% accuracy) for estimating the health of semiconductor devices. [ABSTRACT FROM AUTHOR]

To detect forgeries in signature images using a state-of-the-art deep learning Support Vector Machine (SVM) algorithm based on parameters extracted from the data set. 44 samples total are used in the study, which is split into two groups of 22. Group 1 utilizes CNN-xg, whereas Group 2 employs SVM. Colab software specialized for machine learning is used to run the code. According to simulation findings, the CNN-xg Algorithm obtains a greater reliability of 96.82%, while the SVM achieves reliability of 84.80%; both algorithms have the same significance values of 0.0004 (p < 0.05). CNN-xg identifies forged signatures in the provided dataset more correctly than SVM, demonstrating superior performance. [ABSTRACT FROM AUTHOR]

Water quality is an essential aspect of environmental management, and accurate prediction of water quality parameters can aid in the protection and sustainable use of water resources. In this study, an investigation is conceded out for the prediction of water quality using a assessment drawn between Support Vector Machine (SVM) and Decision Tree (DT). This learning intended to relate the effectiveness of two methods Decision Tree and SVM in predicting water quality. The evaluation was carried out using a dataset of 3277 samples, with performance metrics such as accuracy used to compare the two algorithms. The trial size for each group is 10. The result shows that SVM outperformed XGBoost in terms of all the enactment metrics, suggesting that XGBoost is a more effective method for detecting fake news. The significance charge is p=0.001 which is less than 0.05. Hence, statistical significant difference between the two groups. Our learning suggests that SVM can be a useful tool for real-time monitoring and management of water resources, especially in situations where data are limited or where the relationships between the variables and the water quality constraints are complex. The outcomes suggest that XGBoost with 63.2% accuracy is a more effective algorithm for predicting water quality. [ABSTRACT FROM AUTHOR]

In this study of this research is to improve in accuracy for blast disease in rice crops using K-Nearest Neighbour Algorithm and compared with Random Forest Algorithm. To predict the Rice Blast illness, K-Nearest Neighbour Algorithm and Random Forest in both used, with different training and testing splits. In this case, 169 samples were employed in each of the two groups for an analysis that involved 10 iterations in total. The ClinCalc programme is used as a tool to calculate the setup's correctness for supervised learning. This test's average Gpower is about 80% using G power setting values of 0.05 and 0.80. Novel Supporting Vector Machine and K-Nearest Neighbour's performance at identifying Blast Disease in rice crops will depend on the quality of chosen dataset. The K-Closest Neighbour approach outperforms Random Forest (88.5950%) in terms of both Accuracy and loss, with an Independent Sample T-test of p=0.001 which is considered to be statistically significant. The K-Nearest Neighbour method (88.595%) is more accurate than Random Forest (78.100%) Algorithm. K-Nearest Neighbour can be more effective than Random Forest when dealing with high-dimensional data and when there is a complex relationship between the features and the label for the selected dataset. [ABSTRACT FROM AUTHOR]

Determining the research goal is to categorize unsolicited electronic messages via the use of a comparison of the RF Algorithm and the SVM Algorithm for purifying junk messages. To determine accuracy percentage, Random Forest and Support Vector Machine algorithms with sample sizes of 10 were analyzed repeatedly with G power values of 0.8 and 0.8, respectively. When comparing the RF Algorithm's accuracy (88.890%) to the SVM Algorithm's accuracy (77.990%), the results reveal that the former produces better outcomes. The significance value of the two-tailed test is 0.001, and p-value of 0.001 (p<0.05) indicates that the difference between the two algorithms is statistically significant. [ABSTRACT FROM AUTHOR]

The proposed system aims to increase the accuracy of coronary artery disease prediction through the Novel K- Nearest Neighbor Classifier and compare it with the Support Vector Machine algorithm. This project consists of two groups as SVM Classifier and KNN algorithm. Each group is having a sample size of 20 and G-power pretest of 80% with a Confidence Interval of 95% was used to determine the sample size. The novel K-Nearest Neighbors with Support Vector Machine predicted heart disease with accuracy of 90.8830% and 87.6640%, respectively. The statistical significance value between KNN and SVM based on an independent sample t-test is p=0.001 (p<0.05), which indicates that there is a significance from the two groups. In light of the acquired outcomes the K-Nearest Neighbour Classifier gives better accuracy of 90.8830% when compared with Support Vector Machine 87.6640%. [ABSTRACT FROM AUTHOR]

This study compares seed quality analysis using the Innovative Support Vector Machine with the J48 Decision Tree Algorithm. Two groups were evaluated: Group 1 utilized the Innovative Support Vector Machine, while Group 2 employed the J48 Decision Tree Algorithm. Each group consisted of 20 samples, totaling 40 samples overall. The accuracy assessment revealed that the Innovative Support Vector Machine model outperformed the J48 Decision Tree Algorithm, achieving a higher accuracy of 90.44% with statistical significance at the 0.043 level. Additionally, the Innovative Support Vector Machine-based feature extraction technique achieved an accuracy of 94.63%, significantly surpassing the J48 Decision Tree-based feature extraction system. [ABSTRACT FROM AUTHOR]

The objective of the research is accuracy enhancement in creation of personalized emoji using Innovative AlexNet Classifier is compared over Support Vector Machine Algorithm. AlexNet Classifier is Group 1 and Support Vector Machine Algorithm is assigned as Group 2. Group 1 and Group 2 each consist of 20 iterations. The determination of this sample size incorporated an alpha value of 0.05, with a G power set to 0.8, and a confidence interval of 95%. In total, the dataset comprises 3600 samples, with 2500 allocated for training and 1100 for testing purposes. The Innovative AlexNet Classifier was found to have an precision of 95.36%, while the SVM algorithm had an precision of 85.87% and based on the outcomes of the independent sample T-test, the obtained p-value was 0.000, indicating statistical significance (p<0.05). This implies a notable divergence in the performance of the two algorithms. Comparing the Innovative AlexNet Classifier with the SVM algorithm, the Innovative AlexNet Classifier had a better accuracy of 95.36%. AlexNet classifiers provide users with more precise personalization options when creating emojis. [ABSTRACT FROM AUTHOR]

This study emphasis on enhancing the detection of air quality pollutants using Novel Convolutional Neural Networks (CNN) compared with Support Vector Machines (SVM). The research study consists of two groups, Novel CNN (N=10) and SVM (N=10), which are applied to the dataset of India Air Quality (Kaggle.com) with the total samples of 17481. The iteration for each group is 10 and was calculated using ClinCalc software with α= 0.05 and a pretest power of 0.8. The proposed Novel Convolutional Neural Networks Technique has the potential to improve air quality detection. Novel Convolutional Neural Networks (95.34%) has increased air quality detection accuracy over SVM (91.2010%) with a significant value of 0.068 (Independent sample t-test, p<0.05). This means that the two methods have a statistically significant difference. The study revealed that compared to the accuracy of SVM, Novel CNN is more accurate in detection of air quality pollutants. [ABSTRACT FROM AUTHOR]

The primary aim of this research is to create an effective approach for identifying Cross-Site Scripting (XSS) attacks by utilizing a novel Support Vector Machine (SVM) and contrasting it with the K-Nearest Neighbor (KNN) method. For this study, two distinct cohorts were utilized: group 1 represented the integrated Support Vector Machine, meanwhile group 2 represented K-Nearest Neighbor approach. Each group consisted of 20 samples, determined through Clincalc software, incorporating a pretest power of 80 percent, a significance level of 5%, with a confidence level of 95%. In this study, the incorporated Support Vector Machine achieved a precision of 94%, whereas K-Nearest Neighbor method yielded an accuracy of 84%. The statistical analysis, utilizing an independent t-test (p<0.05) involving a significance value of 0.001, revealed a significant difference statistically between the groups. The Enhanced Integrated Support Vector Machine (SVM) demonstrates significantly better accuracy compared to the K-Nearest Neighbor (KNN) method. [ABSTRACT FROM AUTHOR]

The focus of this study lies in assessment of Hidden Markov Model in User Authentication and compared over accuracy of Support Vector Machine algorithm to increase the security of client authentication. This research study utilizes a dataset of 4000 tuples, known as the keystroke-dynamics dataset. The research project includes using two algorithms, Novel HMM (N=10) and SVM (N=10). ClinCalc software was used to calculate the iterations size for each group, which was set at 10 with a pretest power value of 0.8 and a significance threshold α=0.05. The independent sample t-test yielded a statistically significant result with a significance level of 0.000 (p<0.05), Indicates a statistically significant distinction between the two algorithms. Based on the results, the novel HMM method (84.76% accuracy) has significantly higher mean accuracy compared to SVM (71.61%). When compared to HMM performs significantly better than the Decision tree. [ABSTRACT FROM AUTHOR]

In contrast to the Linear Support Vector Machine (LSVM) algorithm, the objective about the investigation is to increase the accuracy rate of automatic speaker detection by applying a unique MLP Multilayer Perceptron algorithm. The National Centre for Human Language Technology (NCHLT) database provided the research dataset for this study, which had a G-power of 0.8, alpha and beta values of 0.05 and 0.2, and a 95% confidence interval. Automatic speaker recognition is carried out using MLP and SVM, both of which have the same number of data samples (N=10). MLP, however, has a higher accuracy rate. In comparison to the system, the suggested Multilayer Perceptron gets a success rate of 95.66 percent. higher degree of precision compared to the Linear Support Vector Machine (LSVM). Based on a sample size of twenty (ten from Group 1 and ten from Group 2), the enhanced accuracy rate of automatic speaker recognition was calculated, and the result was an 88.90 percent success rate for the linear SVM classifier. This indicates a noteworthy distinction. The study's significance level was found to be p=0.029. In comparison to Linear Support Vector Machine (LSVM), the proposed Multilayer Perceptron (MLP) model offers a higher level of accuracy for the performance analysis of automatic speaker recognition. [ABSTRACT FROM AUTHOR]

The purpose of this work is to apply the Novel Support Vector Machine alongside a Gaussian Naive Bayes approach to recognise rock and mining items from SONAR return and to compare their accuracy. A dataset from Kaggle was employed, which produced a sample size of 12840, determined by G Power with a computed value of 0.8. The accuracy of the Novel Support Vector Machine method rose to 91.03%, while that of a Gaussian Naive Bayes approach climbed to 73.79%. An independent sample T-test yielded a mean value of 73.79, showing a statistically significant distinction in accuracy among the two approaches (p=0.000, p<0.05). The Novel Support Vector Machine (91.03%) greatly beats the Gaussian Naive Bayes method (73.79%) in predicting mines as well as rocks using the SONAR dataset. [ABSTRACT FROM AUTHOR]

The primary objective of the study is to compare a Novel Elastic Net approach with the Support Vector Machine Technique to identify narcissistic personality disorder. The g-power value is 80%, datasets from various online sources with continuing reviews a limit of 0.05%, certainty span 95%, mean and standard deviation were iterated ten times to collect data for the unique elastic net approach and support vector machine technology. Elastic Net Algorithm predictability is 86%, whereas Support Vector Machine predictability is 81%. The difference in the predictions is statistically significant at p = 0.001, (p<0.05). This indicates both of the methods differ statistically significant in a meaningful way. When compared to Support vector machine technology, the novel elastic net technique provides improved accuracy. [ABSTRACT FROM AUTHOR]

It is compared how well a novel recurrent neural network predicts email spam to a support vector machine. Support vector machines and novel recurrent neural networks are two of the groupings included in this study. There are ten samples in each group, and the study's parameters include alpha value 0.8 and beta value 0.2. The G-power value of 80% was taken into consideration while determining the significance value of the dataset using SPSS. In the electronic mail spam detector, the Novel Recurrent Neural Network outperforms the Support Vector Machine by 97.96%, with a significance value of p=0.030 (p<0.05). The Novel Recurrent Neural Network performance study outperforms the Support Vector Machine performance analysis in the electronic mail spam predictor. [ABSTRACT FROM AUTHOR]

This study compares the Novel SVM algorithm with K-Means algorithm to predict the efficiency of phonic coinage revelation. Both the Novel SVM algorithm and K-Means algorithm with sample size 10 each, with a G power (value=0.8) along with a 95% confidence interval has been considered for the research. When compared to the more traditional K-Means (67.02%) approach, the novel Support Vector Machine algorithm achieves higher accuracy (73.15). The p=0.021 (p0.05) significance value shows that there is a genuinely tremendous distinction between the two gatherings. When contrasted with the K-Means strategy, the Novel Support Vector Machine (SVM) move toward tolls much better. [ABSTRACT FROM AUTHOR]

Aim: The goal of the proposed study is to compare the overall performance of chosen classifiers and use two distinct machine learning (ML) algorithms to conduct hand gesture detection in real-time to assist the deaf. For this study, neural networks namely K-means clustering, and Support vector mechanism are chosen. Materials and methods: The choice of the data collection, training, and testing of the chosen data using optional classifiers are all part of this phase. A dataset volume of 80% is used to train the suggested ML classifier model, while 20% is used for testing. The output of binary classifiers is divided into two sets for SPSS analysis, with each set consisting of 10 output values under various practical operations, for a total of 20. Next the metrics CI and alpha are valued at 0.292 and 0.95, p<0.05 respectively, during the SPSS analysis. The dataset for the present work contains 1000 unique values related to the light condition and hand position and the factors considered are reviews. Result: The accuracy improvement between both classifiers is compared throughout the experimental investigation using the Python compiler. The accuracy of the chosen Novel K- means clustering, which increased recognition of hand gesture signs, was 96.01%, compared to the Support Vector Mechanism classifier's accuracy of 90.33%. It demonstrates that there is a statistical implication difference amid the K Means Clustering and SVM algorithm with p=0.000 (p<0.05). Conclusion: The hand gestures in complicated backgrounds from the UCI database are used to test and validate an unique hand gesture identification system that has been developed in this research effort. Skin color detection was being employed to segment the hand motion, and morphological operation has subsequently been done to exclude any additional potential skin color detection regions. The system was put to the test using a wide variety of photos that were scaled and rotated, and it performed with an overall accuracy of 96.01%. [ABSTRACT FROM AUTHOR]

The objective of the paper is to use the Neural Network Support Vector Machine (NNSVM) algorithm to compare the prediction accuracy, so as to predict and classify Indian Currency using the parameters taken from the currency data set with VGG19. For each of the group 130 samples, thus for two groups a total of 260 samples were collected for this investigation. Group 1 uses Neural Network Support Vector Machine while group 2 uses VGG 19 and according to the workflow that was followed, Neural Network Support Vector Machine code has been implemented based on the imported data set using anaconda software and Jupyter notebook is launched. The simulation results shows that Neural Network Support Vector Machine algorithm has 95.4% prediction accuracy whereas VGG 19 has 80.1% prediction accuracy. This gives a significance value of 0.0026 that is (p<0.05). The research result is that for Counterfeit recognition, Neural Network Support Vector Machine has better performance than VGG 19 when used to find the prediction accuracy. [ABSTRACT FROM AUTHOR]

Generator coherency identification is an important step that needs to be taken in the case of a fault to enable durable islanding that could prevent a cascading blackout. Coherent generators are generally identified using the behaviour of rotor angles of the generators. Machine learning techniques have been implemented to tackle this problem. However, these techniques can be configured in different variations This research aims to implement a fine-tuned machine learning technique into the problem of generator coherency identification, to determine coherent generator groups with extreme accuracy. For this purpose, SVM was chosen as the machine learning technique to be implemented in this research, and was fine-tuned to yield the most accurate results for this problem. Data was collected in the form of rotor angles using a simulation of the IEEE 39-bus test power system in DIgSILENT PowerFactory. Based on the results obtained, SVM proved to be accurate in the primary performance metric RMSE, with a value of 0.077. Furthermore, in the secondary performance metric, R2, the value was extremely high at 0.98. Moreover, in various tests such as with reduced input data of 1000, 500, 250 and 50 out of the original 2000, and reduced computational power of 50% and 90%, SVM was accurate, in both RMSE and R2. A third performance metric, accuracy as a percentage, was also analysed, where SVM had an accuracy of 65.7%, which was compared to an RBFNN model in the literature that had a 95.03% accuracy. However, as other performance metrics show that SVM is accurate, it can be concluded that the fine-tuned SVM is applicable for the clustering of coherent generators. [ABSTRACT FROM AUTHOR]

Predicting heart disease in advance could be a significant medical breakthrough because it is widespread. A reliable strategy that can be utilized to do this is machine learning. Decision tree classifiers, random forests, and multilayer perceptron have all been used in studies to predict heart disease. However, several of these techniques could be improved, like poor precision. In our research, we have taken the South African heart Disease dataset and implemented a few models, which include Support Vector Machine (SVM), K Neighbors (KNN), Artificial neural network and XG Boost Classifier. We have used different methods for measuring performance. SVM with 69.0 accuracy, KNN with 86.0 accuracy, and ANN with 80.0 accuracy. However, the XGB classifier has shown some promising results in predicting heart disease with an accuracy of 90%. Further, when the hyperparameters were tuned using the random search method, the accuracy increased to 92.8%. The benefit of this work is that it uses machine-learning approaches to enhance the performance of coronary heart disease prediction. [ABSTRACT FROM AUTHOR]