Level Of Classification Accuracy Research Articles

DÉDUCTION EFFICACE DE LANGUE OFFENSIVE UTILISANT L'APPRENTISSAGE PROFONDE DANS LES MÉDIAS SOCIAUX

Offensive language detection is the technique of identifying and detecting user-generated offensive comments such as insults, pain, profanity, and racism that are targeted at a specific individual or group on social media. As social media platforms become more prominent, offensive language is used more frequently, becoming a major challenge in modern society. A novel effective offensive language classification (EOLC) technique has been proposed to overcome these challenges. English language tweets from YouTube and X (Twitter) with offensive, mild, swear, and non-offensive tweets are used in this paper. Initially, the tweets and comments are pre-processed, and the features are extracted using different techniques, namely term frequency-inverse document frequency (TF-IDF), WordVec, and lexicon-based features. The extracted features are classified using the graph-based deep learning (GDL) method for numerical representation and decision-making. GDL network is optimized with red fox optimization (RFO) to normalize the weight and biases of the network and achieve better accuracy. The proposed GDL model achieves the highest levels of classification accuracy on the X (Twitter) and YouTube datasets, with 95.5 % and 96.8 %, respectively. The results obtained from GDL are more accurate and of higher quality than those obtained from traditional classifiers. The proposed EOLC method improves the overall accuracy by 5.56 %, 7.4 %, 7.7 %, and 10.2 % better than Text CNN, CNN-LSTM, DRNN, and LogitBoost, respectively.

BREAST CANCER CLASSIFICATION USING SUPPORT VECTOR MACHINE (SVM) AND LIGHT GRADIENT BOOSTING MACHINE (LIGHTGBM) MODELS

This study examines the existence of breast cancer from the perspective of statistics as one alternative solution. From a statistical point of view, breast cancer management can be done with early detection and appropriate and fast treatment measures through diagnosis classification. In conducting early detection, an accurate diagnosis model is needed and can be developed by developing and testing statistical methods, one of which is the classification method. The classification methods used in this study are Support Vector Machine (SVM) and LightGBM. Both methods have a high level of classification accuracy because the algorithm used is robust and sensitive in determining each object in the classification member. Therefore, these two methods classify breast cancer into malignant and benign categories. The results of this study show that the best method to classify breast cancer is the SVM method, with an accuracy rate of 97.9%.

Evaluation of performance enhancement in Ethereum fraud detection using oversampling techniques

With the growing popularity of cryptocurrencies and their decentralized nature, the risk of fraudulent activities within these ecosystems has become a pressing concern. This research paper focuses on Ethereum fraud detection using a dataset specifically curated for this purpose. The methodology encompasses essential steps, including data cleaning, correlation analysis, data splitting, and exploratory data analysis to understand the data characteristics. Subsequently, self-optimized machine learning models are trained with the Pycaret library while addressing the class imbalance using SMOTENC (Synthetic Minority oversampling Technique for Nominal and Continuous Data), ADA-SYN (Adaptive Synthetic Algorithm), and K-Means-SMOTE techniques. The performance of the various models is evaluated on test and validation datasets using metrics such as accuracy, precision, recall, and AUC (Area Under Curve). The study reveals that the ensemble models, particularly CATBoost (Categorical Boost) and LGBM (Light Gradient Boost Method), show exceptional efficiency, with accuracy ranging from 97% to 98.42% after oversampling. Moreover, these models exhibit higher F1 scores and AUC values, indicating their potential to detect fraud effectively. The validation metrics also lie in the same range, demonstrating that the models do not suffer from over-fitting. The experiment demonstrates the promise of ensemble models in Ethereum fraud detection, paving the way for deploying robust fraud detection systems in crypto-currency ecosystems. The results show that the K-Means SMOTE oversampling technique has the highest classification accuracy levels of 98.42% with an AUC of 99.82%.

Classification of traffic accidents’ factors using TrafficRiskClassifier

The TrafficRiskClassifier model introduced in this study adopts an innovative approach that incorporates migration learning, image classification, and self-supervised learning, aiming to significantly improve the accuracy and efficiency of traffic accident risk analysis. Compared with traditional traffic safety analysis techniques, this model focuses on utilizing contextual information and situational data of traffic accidents to achieve a higher level of risk classification accuracy. The core of this approach is to deeply mine and analyze the detailed information in the accident environment, to provide more scientific and effective support for traffic accident risk prevention and response. Initially, by integrating migration learning with image classification techniques, the model proficiently extracts pivotal features from intricate traffic scenarios and formulates initial assessments of accident risks. Subsequently, self-supervised learning is incorporated in this study, augmenting the model's capability to comprehend and categorize accident imagery. The TrafficRiskClassifier model exhibits a generalization ability of 91.82%, 85.16%, and 80.92% on individual classification tasks, respectively, signifying its robust learning capacity and proficiency in managing unseen data. Furthermore, the TrafficRiskClassifier model delineates a functional nexus between accident risk and variables such as weather, road conditions, and personal factors, employing a polynomial regression approach. This methodology not only amplifies the predictive precision of the model but also renders it versatile across diverse scenarios. Through the analysis of various polynomial functions, the model achieves enhanced accuracy in classifying disparate risk levels. The outcomes demonstrate that the TrafficRiskClassifier model can efficaciously amalgamate contextual information within traffic scenarios, thereby achieving more precise classification of traffic accident risks, and consequently serving as an invaluable instrument for urban traffic safety management.

U-Net Segmentation for CNN Classification to Improve the Accuracy of Automatic Prediction of Brain Tumors Using MRI Images

Among various cancers, a brain tumor is a serious one that affects the patient's brain and causes sudden death. Several clinical diagnoses of brain tumors help medical experts provide the proper treatment at the right time; earlier identification and detection of brain tumors are much less. With the increase of cancer patients, managing and diagnosing efficiently with massive data collected is complex. Diagnosing patients with a brain tumor in the earlier stages is also tricky. Medical image processing methods and computer-aided diagnosis methods proposed in earlier works have not provided a high level of accuracy in classification, but the segmentation accuracy was good. Some recent research works have focused on implementing machine learning algorithms for predicting brain tumors for a massive amount of data, where the accuracy still needs to be improved. Machine learning and deep learning algorithms are used to predict brain tumors and their types earlier. With a large amount of data available, the ML and DL models are trained efficiently to make accurate prediction models. The DL models provide better accuracy in processing and predicting medical images than ML models. Some pre-trained deep learning algorithms like VGG16, VGG19, InceptionV3, and ImageNet provide improved accuracy and less computation power to achieve the required results. This paper proposes a CNN algorithm with a VGG16 pre-trained model for detecting brain tumors with MRI images. The input images are segmented using a U-Net model, improving the prediction process's accuracy, and are classified using a CNN model with VGG16, which helps in the efficient and faster prediction of brain tumors from the MRI images. The results show that the proposed model provides better accuracy than the existing ones.

Gray level fuzzy deep neural networks for enhancing performance in lung disease detection: A comparative study with fuzzy logic methods

AbstractLung cancer is a deadly disease, and its early detection is crucial for effective treatment. In this context, accurate classification of lung cancers from computed tomography imaging is a vital research area. Irregularly detected gray matter in these images can affect classification outcomes, making accurate lung cancer detection difficult. To address this issue, researchers have developed a new approach that combines fuzzy logic and deep neural networks (DNNs) for extracting the hidden characteristic features of lung diseases from the images, and thereby, ensuring that only relevant features are used for classification. The proposed gray‐level fuzzy approach on DNNs (GL‐FDNN) is designed to accurately classify four distinguished lung cancer classes namely, Large cell carcinoma, Adenocarcinoma, Normal lung computed tomography, and Squamous cell carcinoma. After identifying the area of interest, its pixel intensity ratio is used to derive the fuzzy logic, which is then used to extract the most obscure gray matter features. Then, ResNet‐18 sorts the obscure features into categories and picks only relevant features to improve classification accuracy. The gray‐level fuzzy approach on DNNs technique was tested alongside some of the renowned, existing techniques for their relative performances. Experimental analysis was carried out on standard Kaggle datasets and the outcomes reveal that the proposed technique offers the highest level of lung disease classification accuracy (99.2%). It also improves the recall and precision factors. Thus, it can serve as a valuable diagnosis tool that can enhance the detection of lung cancer and the effectiveness of its treatment to save lives.

U-Net Segmentation for CNN Classification to Improve the Accuracy of Automatic Prediction of Brain Tumors Using MRI Images

Among various cancers, a brain tumor is a serious one that affects the patient's brain and causes sudden death. Several clinical diagnoses of brain tumors help medical experts provide the proper treatment at the right time; earlier identification and detection of brain tumors are much less. With the increase of cancer patients, managing and diagnosing efficiently with massive data collected is complex. Diagnosing patients with a brain tumor in the earlier stages is also tricky. Medical image processing methods and computer-aided diagnosis methods proposed in earlier works have not provided a high level of accuracy in classification, but the segmentation accuracy was good. Some recent research works have focused on implementing machine learning algorithms for predicting brain tumors for a massive amount of data, where the accuracy still needs to be improved. Machine learning and deep learning algorithms are used to predict brain tumors and their types earlier. With a large amount of data available, the ML and DL models are trained efficiently to make accurate prediction models. The DL models provide better accuracy in processing and predicting medical images than ML models. Some pre-trained deep learning algorithms like VGG16, VGG19, InceptionV3, and ImageNet provide improved accuracy and less computation power to achieve the required results. This paper proposes a CNN algorithm with a VGG16 pre-trained model for detecting brain tumors with MRI images. The input images are segmented using a U-Net model, improving the prediction process's accuracy, and are classified using a CNN model with VGG16, which helps in the efficient and faster prediction of brain tumors from the MRI images. The results show that the proposed model provides better accuracy than the existing ones.

K-NN with Purity Algorithm to Enhance the Classification of the Air Quality Dataset

The large number of attributes in a large dataset can cause a decrease in the level of classification accuracy. Attribute reduction can be a solution to improve classification performance, especially in the K-NN algorithm. This research discusses the classification results of K-NN with attribute reduction using Purity. Based on the results of testing carried out on the Air Quality Dataset, the level of accuracy obtained after attribute reduction was 70.71%, while the level of accuracy obtained before attribute reduction was 56.44%, the increase in accuracy obtained from testing this dataset was equal to 14.27%. The proposed Purity method for attribute reduction can increase the accuracy level of the K-NN classification process.

Particle Swarm Optimization for Optimizing Public Service Satisfaction Level Classification

This research aims to categorize survey data to determine the level of satisfaction with the services provided by the village government as a public service provider. Villages or sub-districts currently offer services in response to community demand, although only partially or as efficiently as possible. The data collection technique used was distributing questionnaires to the village community. The method used for classification is the machine learning method. Before the classification process, feature selection is carried out at the data pre-processing stage using Particle Swarm Optimization (PSO), which has been proven to increase the accuracy of the classification values. The classification methods employed include Decision Tree (DT), Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms for classification purposes. This study achieves the maximum level of accuracy in decision tree classification, attaining an accuracy rate of 97.74%. Subsequently, the KNN algorithm achieved an accuracy of 77.90%, the Nave Bayes algorithm achieved 64.4%, and the SVM algorithm, which yielded the lowest accuracy value, achieved 59.90%. Following the application of Particle Swarm Optimization (PSO) for optimization, the accuracy of the SVM and KNN algorithms improved to 98.3%. The Decision Tree algorithm achieved a value of 97.77%, while the Naive Bayes technique yielded a value of 69.30%.

An efficient intrusion detection model based on convolutional spiking neural network

Many intrusion detection techniques have been developed to ensure that the target system can function properly under the established rules. With the booming Internet of Things (IoT) applications, the resource-constrained nature of its devices makes it urgent to explore lightweight and high-performance intrusion detection models. Recent years have seen a particularly active application of deep learning (DL) techniques. The spiking neural network (SNN), a type of artificial intelligence that is associated with sparse computations and inherent temporal dynamics, has been viewed as a potential candidate for the next generation of DL. It should be noted, however, that current research into SNNs has largely focused on scenarios where limited computational resources and insufficient power sources are not considered. Consequently, even state-of-the-art SNN solutions tend to be inefficient. In this paper, a lightweight and effective detection model is proposed. With the help of rational algorithm design, the model integrates the advantages of SNNs as well as convolutional neural networks (CNNs). In addition to reducing resource usage, it maintains a high level of classification accuracy. The proposed model was evaluated against some current state-of-the-art models using a comprehensive set of metrics. Based on the experimental results, the model demonstrated improved adaptability to environments with limited computational resources and energy sources.

High-Dimensional Ensemble Learning Classification: An Ensemble Learning Classification Algorithm Based on High-Dimensional Feature Space Reconstruction

When performing classification tasks on high-dimensional data, traditional machine learning algorithms often fail to filter out valid information in the features adequately, leading to low levels of classification accuracy. Therefore, this paper explores the high-dimensional data from both the data feature dimension and the model ensemble dimension. We propose a high-dimensional ensemble learning classification algorithm focusing on feature space reconstruction and classifier ensemble, called the HDELC algorithm. First, the algorithm considers feature space reconstruction and then generates a feature space reconstruction matrix. It effectively achieves feature selection and reconstruction for high-dimensional data. An optimal feature space is generated for the subsequent ensemble of the classifier, which enhances the representativeness of the feature space. Second, we recursively determine the number of classifiers and the number of feature subspaces in the ensemble model. Different classifiers in the ensemble system are assigned mutually exclusive non-intersecting feature subspaces for model training. The experimental results show that the HDELC algorithm has advantages compared with most high-dimensional datasets due to its more efficient feature space ensemble capability and relatively reliable ensemble operation performance. The HDELC algorithm makes it possible to solve the classification problem for high-dimensional data effectively and has vital research and application value.

Sentiment review of coastal assessment using neural network and naïve Bayes

An assessment of a place will provide an overview for other people whether the place is feasible to be visited or not. Assessment of coastal places will provide a separate assessment for potential visitors in considering visitation. This article proposes a model using the neural network (NN) and naïve Bayes (NB) methods to classify sentiment toward coastal assessments. The proposed NN and NB models are optimized using information gain (IG) and feature weights, namely particle swarm optimization (PSO) and genetic algorithm (GA) which are carried out to increase the level of classification accuracy. Based on the experimental results, the best level of accuracy for the classification of coastal assessments is 87.11% and is named the NB IG+PSO model. The best model obtained is a model that can be used as a decision support for potential beach visitors in deciding to visit the place.

Penerapan Convolutional Neural Network dengan Arsitektur Mobilenetv2 Pada Aplikasi Penerjemah dan Pembelajaran Bahasa Isyarat

Sign Language is the main communication medium for deaf people, in Indonesia itself there are several types of sign language, one of which is SIBI or Indonesian Sign Language System. This type of SIBI sign language is one type of sign language used in special school (SLB) environments. Deaf children are not yet able to use sign language and others have difficulty communicating with people around them. This research aims to help deaf children learn sign language, as well as help them communicate through sign language interpreters. The methodology applied to the application is by using a Convolutional Neural Network with the MobileNetV2 architecture. CNN is an algorithm that is included in the artificial neural network category which has the advantage of having a very high level of accuracy in classification, and MobileNetV2 is a form of Convolutional Neural Network (CNN) architecture that is able to overcome the need for excess computing resources created by Google researchers. so that it can be used on mobile devices or cell phones. The model training stage obtained an accuracy value of 98.99% with a total of 100 epochs. The model trained did not experience overfitting or underfitting so the model can be used. The testing stage uses black box testing to ensure the application is running correctly. The final stage is conducting research using TAM (Technology Acceptance Model) to measure the level of approval of the application using a Likert scale. Testing using black box testing was successful and met expectations, and the implementation of the application received an approval rate of 78.4% on average.

CNN-GMM approach to identifying data distribution shifts in forgeries caused by noise: a step towards resolving the deepfake problem.

Recently, there have been notable advancements in video editing software. These advancements have allowed novices or those without access to advanced computer technology to generate videos that are visually indistinguishable to the human eye from real ones to the human observer. Therefore, the application of deepfake technology has the potential to expand the scope of identity theft, which poses a significant risk and a formidable challenge to global security. The development of an effective approach for detecting fake videos is necessary. Here, we introduce a novel methodology that employs a convolutional neural network (CNN) and Gaussian mixture model (GMM) to effectively differentiate between fake and real images or videos. The proposed methodology presents a novel CNN-GMM architecture in which the fully connected (FC) layer in the CNN is replaced with a customized Gaussian mixture model (GMM) fully connected layer. The GMM layer utilizes a weighted set of Gaussian probability density functions (PDFs) to represent the distribution of data frequencies in both real and fake images. This representation indicates there is a shift in the distribution of the manipulated images due to added noise. The CNN-GMM model demonstrates the ability to accurately identify variations resulting from different types of deepfakes within the probability distribution. It achieves a high level of classification accuracy, reaching up to 100% in training accuracy and up to 96% in validation accuracy. Notwithstanding the ratio of the genuine class to the counterfeit class being 16.6% to 83.4%, the CNN-GMM model exhibited high-performance metrics in terms of recall, accuracy, and F-score when classifying the least genuine class.

SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification.

In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for epileptic electroencephalogram (EEG) signal classification. The goal is to develop a lightweighted deep learning model while retaining a high level of classification accuracy. To do so, we propose a weighted neighborhood field graph (WNFG) to represent EEG signals. The WNFG reduces redundant edges between graph nodes and has lower graph generation time and memory usage than the baseline solution. The sequential graph convolutional network is further developed from a WNFG by combining sparse weight pruning and the alternating direction method of multipliers (ADMM). Compared with the state-of-the-art method, our method has the same classification accuracy on the Bonn public dataset and the spikes and slow waves (SSW) clinical real dataset when the connection rate is ten times smaller.

Penerapan Data Mining Menggunakan Metode Algoritma Naive Bayes Classifier untuk Mendukung Strategi Promosi

The purpose of this study is to make it easier to find out the prediction results with the classification method from PPDB data for 2021-2022 to support promotion strategies at the Skye Foundation with the Naive Bayes Classifier Algorithm and to find out the level of accuracy in classification in determining promotion strategies. In this study the authors used 210 data as training data and 53 data as testing data for manual testing in excel and for testing using RapidMiner tools. Based on the results of the analysis in the research conducted, the results of predicting registration and non-registration quickly and accurately from tests carried out by comparing training data with data testing using RapidMiner tools obtained an accuracy rate of 92.45%.

AN UNSUPERVISED-SUPERVISED ENSEMBLE TECHNOLOGY WITH NON-ITERATIVE TRAINING ALGORITHM FOR SMALL BIOMEDICAL DATA ANALYSIS

Improving the accuracy of intelligent data analysis is an important task in various application areas. Existing machine learning methods do not always provide a sufficient level of classification accuracy for their use in practice. That is why, in recent years, hybrid ensemble methods of intellectual data analysis have begun to develop. They are based on the combined use of clustering and classification procedures. This approach provides an increase in the accuracy of the classifier based on machine learning due to the expansion of the space of the input data of the task by the results of the clustering.
 In this paper, the tasks of modification and improvement of such technology for small data analysis are considered. The basis of the modification is the use of clustering with output at the first step of the method to increase the accuracy of the entire technology. Despite the high accuracy of the work, this approach requires a significant expansion of the inputs of the final linear classifier (labels of the obtained clusters are added to the initial inputs). To avoid this shortcoming, the paper proposes an improvement based on the introduction of a new classification procedure at the first step of the method and replaces all the initial inputs of the task with the results of its work. In parallel with it, clustering is performed taking into account the original attribute, the results of which are added to the output of the classifier of the first step. In this way, the formation of an expanded set of data of significantly lower dimensionality in comparison with the existing method takes place (here there is no longer a large number of initial features, which is characteristic of biomedical engineering tasks). This reduces the training time of the method and increases its generalization properties.
 Modeling of the method was based on the use of a short dataset contained in an open repository. After the preprocessing procedures, the dataset has only 294 vectors, each of which was characterized by 18 attributes. Data classification was done using an SGTM-based neural-like structure classifier. This linear classifier provides high accuracy of work. In addition, it does not provide for the implementation of an iterative training procedure and additional adjustment of work parameters. Data clustering was performed using the k-means method. This choice is due to both the simplicity and speed of its work. 
 The search for the optimal number of k-means clusters was carried out using 4 different methods. They all showed different results. That is why, some experiments were conducted to assess the influence of different numbers of clusters (from 3 to 7) on the accuracy of all 4 algorithms of the developed technology. The accuracy of the proposed technology has been established experimentally in comparison with the linear classifier and the existing hybrid method. In addition, by reducing the inputs of the final classifier, the developed technology reduces the duration of the training procedure compared to the basic method. All this ensures the possibility of using the proposed technology when solving various applied problems of medical diagnostics, in particular, based on the analysis of small data.
 Keywords: small data approach, non-iterative training, ensemble learning, unsupervised-supervised technology, biomedical engineering.

Hybrid GA-mSVM: Dimensionality reduction using hybrid genetic algorithm and modified support vector machine classifier

<p>The expansion of technology results in the generation of enormous amounts of data in all areas. The researchers face a difficult challenge when they attempt to categorize these high-dimensional data. A method called as feature selection is used to reduce the high-dimensionality of the data. It is possible to consider selecting features as an issue of global combinatorial optimization in the field of machine learning. This minimizes on the total number of features, gets rid of data that is insignificant, noisy, or duplicative, and ultimately achieves an acceptable level of classification accuracy. For the purpose of dimensionality reduction, a unique approach known as the Hybrid Genetic Algorithm – modified Support Vector Machine Classifier (Hybrid GA-mSVM) is presented in this study. The genetic algorithm component conducts a search using the principles underlying the evolutionary process in order to find the best feature set and after that the trimmed dataset is provided to the SVMs. The results of the experiments reveal that the proposed approach efficiently minimizes the features and achieves a better classification accuracy than other feature selection methods.</p>

Sentiment Analysis of Shopee App Reviews Using Random Forest and Support Vector Machine

During the COVID-19 outbreak, Indonesian marketplaces were significantly impacted including Shopee app. It is necessary to evaluate the features and services of the Shopee application by looking at the feedback given by the public in Google Play Store reviews. This is what prompted research to be conducted from Kaggle data in the form of Shopee reviews. From this data, sentiment analysis is carried out utilizing the Support Vector Machine (SVM) and Random Forest methods. This method are used to classify reviews based on positive and negative sentiments. The results showed that the level of classification accuracy in the Random Forest model is 82.21%. While the SVM model provides a higher level of accuracy of 84.71%. Data exploration on positive and negative sentiment classes is used to find insight into this problem. In positive sentiment, words that often appear such as “belanja”, “aplikasi”, and “barang” are found. As for the negative sentiments, namely “ongkir”, “kirim”, “aplikasi”. These words can be used to be a quality improvement or evaluation for the Shopee company.

A fast and time-efficient machine learning approach to dark matter searches in compressed mass scenario

Various analyses for searching for the signature of SUSY or exotic particles have been carried out by the experiments at CERN. These analyses made use of traditional cut and count methods. While this method has yielded promising results, it has been challenging in the region where the mass difference between SUSY particles is small. Deep learning is currently widely employed in most data analysis tasks, including high energy physics, and has made significant advances in almost all fields for collecting and interpreting huge data samples. In this paper, a fast and time-efficient classification technique is proposed, utilizing machine learning algorithms to distinguish dark matter signal from SM background in compressed mass spectra scenarios at a center-of-mass energy of 14 TeV. A classification model was built in a short amount of time using 2D histograms produced with less amount of data, effectively reducing computational costs through the transfer learning of pre-trained deep models while maintaining a high level of classification accuracy.