Bayesian Classifier Research Articles

Software-Defined-Networking-Based One-versus-Rest Strategy for Detecting and Mitigating Distributed Denial-of-Service Attacks in Smart Home Internet of Things Devices.

The number of connected devices or Internet of Things (IoT) devices has rapidly increased. According to the latest available statistics, in 2023, there were approximately 17.2 billion connected IoT devices; this is expected to reach 25.4 billion IoT devices by 2030 and grow year over year for the foreseeable future. IoT devices share, collect, and exchange data via the internet, wireless networks, or other networks with one another. IoT interconnection technology improves and facilitates people's lives but, at the same time, poses a real threat to their security. Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are considered the most common and threatening attacks that strike IoT devices' security. These are considered to be an increasing trend, and it will be a major challenge to reduce risk, especially in the future. In this context, this paper presents an improved framework (SDN-ML-IoT) that works as an Intrusion and Prevention Detection System (IDPS) that could help to detect DDoS attacks with more efficiency and mitigate them in real time. This SDN-ML-IoT uses a Machine Learning (ML) method in a Software-Defined Networking (SDN) environment in order to protect smart home IoT devices from DDoS attacks. We employed an ML method based on Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (kNN), and Naive Bayes (NB) with a One-versus-Rest (OvR) strategy and then compared our work to other related works. Based on the performance metrics, such as confusion matrix, training time, prediction time, accuracy, and Area Under the Receiver Operating Characteristic curve (AUC-ROC), it was established that SDN-ML-IoT, when applied to RF, outperforms other ML algorithms, as well as similar approaches related to our work. It had an impressive accuracy of 99.99%, and it could mitigate DDoS attacks in less than 3 s. We conducted a comparative analysis of various models and algorithms used in the related works. The results indicated that our proposed approach outperforms others, showcasing its effectiveness in both detecting and mitigating DDoS attacks within SDNs. Based on these promising results, we have opted to deploy SDN-ML-IoT within the SDN. This implementation ensures the safeguarding of IoT devices in smart homes against DDoS attacks within the network traffic.

Prediction of Drug-Induced Liver Injury: From Molecular Physicochemical Properties and Scaffold Architectures to Machine Learning Approaches.

The process of developing new drugs is widely acknowledged as being time-intensive and requiring substantial financial investment. Despite ongoing efforts to reduce time and expenses in drug development, ensuring medication safety remains an urgent problem. One of the major problems involved in drug development is hepatotoxicity, specifically known as drug-induced liver injury (DILI). The popularity of new drugs often poses a significant barrier during development and frequently leads to their recall after launch. In silico methods have many advantages compared with traditional invivo and invitro assays. To establish a more precise and reliable prediction model, it is necessary to utilize an extensive and high-quality database consisting of information on drug molecule properties and structural patterns. In addition, we should also carefully select appropriate molecular descriptors that can be used to accurately depict compound characteristics. The aim of this study was to conduct a comprehensive investigation into the prediction of DILI. First, we conducted a comparative analysis of the physicochemical properties of extensively well-prepared DILI-positive and DILI-negative compounds. Then, we used classic substructure dissection methods to identify structural pattern differences between these two different types of chemical molecules. These findings indicate that it is not feasible to establish property or substructure-based rules for distinguishing between DILI-positive and DILI-negative compounds. Finally, we developed quantitative classification models for predicting DILI using the naïve Bayes classifier (NBC) and recursive partitioning (RP) machine learning techniques. The optimal DILI prediction model was obtained using NBC, which combines 21 physicochemical properties, the VolSurf descriptors and the LCFP_10 fingerprint set. This model achieved a global accuracy (GA) of 0.855 and an area under the curve (AUC) of 0.704 for the training set, while the corresponding values were 0.619 and 0.674 for the test set, respectively. Moreover, indicative substructural fragments favorable or unfavorable for DILI were identified from the best naïve Bayesian classification model. These findings may help prioritize lead compounds in the early stage of drug development pipelines.

New insights into the metallogenic genesis of the Xiadian Au deposit, Jiaodong Peninsula, Eastern China: Constraints from integrated rutile in-situ geochemical analysis and machine learning discrimination

The genesis of the Mesozoic Au deposit within the Jiaodong Peninsula is debated, with vague connections to magmatism, deep-source metamorphic devolatilization, or the involvement of mantle materials over the long term. This study presents an integrated investigation involving textures, in-situ geochemical analysis, and machine learning discrimination of the rutile of the Xiadian Au deposit at the northwest Jiaodong Peninsula, aiming to provide metallogenic insights. Rutile is distinguished into three types: Type 1 rutile formed in the pre-ore stage is fine-grained and replaces and coexists with relict titanite, with euhedral crystal and being homogeneous and dark BSE response. Type 2 and type 3 rutile exhibit subhedral and anhedral crystals and are associated with quartz-sericite-pyrite alteration. The former has low W contents and is dark BSE response, while the latter is brighter in BSE and exhibits pronounced fragmentation and erosion. Chemical trends indicate the elemental substitution mechanism within rutiles from early to later stages: (Nb, Ta)5+ + (V, Fe, Al, Cr)3+ ↔ 2Ti4+ to 2(V, Fe, Al, Cr)3+ + W6+ ↔ 3Ti4+. Elevated concentrations of trivalent iron ions due to elevated oxygen fugacity of the ore-forming fluids are more likely to have contributed to this change, rather than the increment of W content. This may induce the destabilization of gold-sulfur complexes and contribute to gold mineralization. Machine learning models, including Extreme Randomized Tree (ET), Support Vector Machine, and Naive Bayes classifiers, were trained using a collected dataset containing 6598 rutile geochemistry data. These models consistently classify Xiadian rutile as the hydrothermal origin and having affinity to orogenic Au deposit. The SHapley Additive exPlanation (SHAP) framework was used to explain the discrimination criteria for the best-performing ET models. It revealed that elements such as Y, Ta, Mo, Sn, W, and Nb significantly contribute to classifying Xiadian rutile as affiliated with orogenic Au deposit. Further mass balance calculations with titanite, the predominant precursor Ti-bearing mineral of rutile, suggest that the W content in rutile is controlled by ore-forming fluids, likely having a deep source. In contrast, elements like Y, Mo, Cr, Nb, and Ta are influenced by leaching or re-equilibration with host rocks. Collectively, deep geodynamic processes likely provide major Au mineralization-related materials and fluids, while elemental leaching or re-equilibration with shallow crust rocks near fluid pathways influences the geochemical characteristics of the Xiadian gold system.

Android malware detection using GIST based machine learning and deep learning techniques

In today’s digital world, Android phones play a vital part in a variety of facets of both professionals and individuals’ personal and professional lives. Android phones are great for getting things done faster and more organized. The proportionate increase in the number of malicious applications has also been seen to be expanding. Since the play store offers millions of apps, detection of malware apps is challenging task. In this paper, a methodology is introduced for detecting malware in Android applications through the utilization of global image shape transform (GIST) features extracted from grayscale images of the applications. The dataset comprises samples of both malware and benign apps collected from the virus share website. After converting the apps into grayscale images, GIST features are extracted to capture their global spatial layout. Various machine learning (ML) algorithms, such as logistic regression (LR), k-nearest neighbour (KNN), AdaBoost, decision tree (DT), Naïve Bayes (NB), random forest (RF), support vector machine (SVM), extra tree classifier (ETC), and gradient boosting (GB), are employed to classify the applications according to their GIST features. Furthermore, a feed forward neural network (FFNN) is utilized as a deep learning (DL) technique to further improve the accuracy of classification. The performance of each algorithm is evaluated using metrics such as accuracy, precision and recall. The results demonstrated that the FFNN achieves superior accuracy compared to traditional ML classifiers, indicating its effectiveness in detecting malware in Android apps.

Autism detection based on autism spectrum quotient using weighted average ensemble method

Autism spectrum disorder (ASD) is a condition that occurs in an individual, wherein it is accompanied by various symptoms such as difficulties in socializing with others. Early detection of ASD patients can assist in preventing various symptoms caused by ASD. The focus of this research is to automate the diagnosis of ASD in an individual based on the results of the autism spectrum quotient (AQ) using weighted average ensemble method. Initially, preprocessing is carried out on the dataset to ensure optimal performance of the resulting model. In the preprocessing step, the filling of missing values and feature selection occurs, where the feature selection method being utilized is p-value. The model in this research uses the weighted average ensemble method, which is the model that combines three machine learning classification algorithms. Eight classification algorithms are tested to identify the three algorithms with the best performance, namely gaussian Naïve Bayes (NB), logistic regression (LR), and random forest (RF). Following the testing, the model constructed using the weighted average ensemble method exhibits the highest performance compared to the model built using a single classification algorithm. The performance matrix used to measure the model’s performance is area under the curve (AUC)/receiver operating characteristic (ROC), with the developed model achieving an AUC/ROC value of 0.912.

Predicting the effects of microcredit on women’s empowerment in rural Bangladesh: using machine learning algorithms

This study aimed to predict the impact of microcredit on women’s empowerment in Bangladesh using machine learning (ML) algorithms. In rural Bangladesh, where microcredit programs are not significantly employed, data for the study was gathered through a survey. The study gathered data on a range of socioeconomic, demographic, and women’s empowerment indicators. The Naive Bayes (NB), sequential minimal optimization (SMO), k-nearest neighbor (k-NN), decision tree (DT), and random forest (RF) ML techniques were used in the investigation. In terms of the prediction of women’s empowerment, the findings indicated that all five algorithms performed well, with the DT having the highest level of accuracy (83.72%). The results of this study have significant consequences for Bangladesh’s microcredit programs and those in nations that are developing. Microcredit programs can focus their efforts on women who, based on their socioeconomic and demographic features, are most likely to benefit from the program by employing ML algorithms. This may result in more successful microcredit projects that support the empowerment of women and general socioeconomic growth.

A novel approach to analyzing the impact of AI, ChatGPT, and chatbot on education using machine learning algorithms

Artificial intelligence (AI) is one of the most common and essential technologies in this modern era, especially in the education and research sectors. It mimics machine-processed human intellect. In modern times, ChatGPT is one of the most effective and beneficial tools developed by OpenAI. Provides prompt answers and feedback to help academics and researchers. Using ChatGPT has various advantages, including improving methods of instruction, preparing interactive lessons, assessment, and advanced problem-solving. Threats against ChatGPT, however, include diminishing creativity, and analytical thinking. Additionally, students would adopt unfair procedures when submitting any tests or assignments online, which would increase their dependency on AI systems rather than thinking analytically. In this study, we have demonstrated arguments on both sides of AI technology. We believe that our study would provide a depth of knowledge and more informed discussion. Data is collected via an offline platform and then machine learning algorithms such as K-nearest neighbour (K-NN), support vector machine (SVM), naive bayes (NB), decision tree (DT), and random forest (RF) are used to analyze the data which helps to improve teaching and learning techniques where SVM shows best performance. The results of the study would offer several significant learning and research directions as well as ensure safe and responsible adoption.

Study on environmental monitoring classification system based on improved bayesian algorithm

Abstract The commonly used methods for classifying water quality data are based on water quality parameters. The naive Bayesian classification method can be applied to substitute different evaluation criteria and selected water quality parameters for different water bodies. Compared to other water quality data classification methods, naive Bayesian classification methods have advantages such as simple calculation, high classification accuracy, and strong universality. However, this method overlooks the correlation between various water quality parameters and categories. To address the issues of poor universality, computational complexity, and low accuracy of traditional water quality data classification methods, a water quality data classification method based on weighted naive Bayes is proposed. This method comprehensively considers the impact of water quality attributes and their values on the classification results and replaces the original naive Bayes with weighted attribute conditional probabilities to make the classification results as close as possible to the actual category of the sample. The results indicate that this method exceeds 94% accuracy and can be directly utilized as a water quality classification module in water quality monitoring systems.

Learning Balanced Bayesian Classifiers From Labeled and Unlabeled Data

Learning Balanced Bayesian Classifiers From Labeled and Unlabeled Data

Fake News Detection Using Machine Learning

Abstract: The pervasive spread of misinformation in today's digital environment presents a daunting problem, influencing public opinion and possibly causing unfavorable social consequences. This paper conducts a thorough investigation into the effectiveness includes a broad variety of false information in text, images, and video formats. Our study carefully assesses SVM, random forest, MLP, and naive Bayes classifiers using a large and painstakingly selected dataset. We provide a classifier of voting to improve accuracy and consistency by integrating the forecasts from these several models. Voting Classifier's outstanding performance, attaining a perfect accuracy rate and highlighting the ensemble approach's inherent robustness. The research emphasizes the vital ensemble methods' key part in strengthening the foundation the identification of bogus news systems and providing a strong barrier against the unrelenting propagation of fake information.

A Comparative Analysis of Machine Learning Algorithms for Predicting the Telemarketing Campaigns of Portuguese Banking Institutions

A financial institution is an entity of business that handles deposits, money circulation, saving, and other corresponding operations. Banks supply a broad spectrum of services. This differs depending on the capacity of each bank. The bank is going to provide further amenities, which is one of the many industries that frequently engage in direct product launches. Thus, banks might employ technological innovations to do market investigation before directly releasing products, which may assist with decision-making. Marketing initiatives that offer prospective consumers the option of embracing or rejecting an offered product can be carried out using direct email, phone calls, and email campaigns. The volume of incoming data continues to expand as time goes by. One of the bank institutions in Portugal faced difficulties judging whether or not its customer base would choose to sign up for a term deposit because of the growing amount of data. Consequently, to ascertain if the consumer will subscribe to a term deposit, the data mining processes utilized here will incorporate Ensemble Learning (Gradient Boosting (GB), Xg Boost (XGB), Random Forest (RF), Linear Models (Logistic Forest (RF), Linear Models (Logistic Regression (LR), Non-Linear Models (K-Near Neighbors (KNN), Support Vector Machine (SVM), Decision Tree Classifier (DT)), and Probabilistic Models (Naive Bayes (NB))included in classification techniques. Among these, Xg Boost yields the most impressive results, with the best performance in terms of AUC, model accuracy, and cross-validation accuracy in a minimum amount of computation time.

A Research on Emotion Recognition of the Elderly Based on Transformer and Physiological Signals

Aiming at problems such as the difficulty of recognizing emotions in the elderly and the inability of traditional machine-learning models to effectively capture the nonlinear relationship between physiological signal data, a Recursive Map (RM) combined with a Vision Transformer (ViT) is proposed to recognize the emotions of the elderly based on Electroencephalogram (EEG), Electrodermal Activity (EDA), and Heart Rate Variability (HRV) signals. The Dung Beetle Optimizer (DBO) is used to optimize the variational modal decomposition of EEG, EDA, and HRV signals. The optimized decomposed time series signals are converted into two-dimensional images using RM, and then the converted image signals are applied to the ViT for the study of emotion recognition of the elderly. The pre-trained weights of ViT on the ImageNet-22k dataset are loaded into the model and retrained with the two-dimensional image data. The model is validated and compared using the test set. The research results show that the recognition accuracy of the proposed method on EEG, EDA, and HRV signals is 99.35%, 86.96%, and 97.20%, respectively. This indicates that EEG signals can better reflect the emotional problems of the elderly, followed by HRV signals, while EDA signals have poorer effects. Compared with Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbors (KNN), the recognition accuracy of the proposed method is increased by at least 9.4%, 11.13%, and 12.61%, respectively. Compared with ResNet34, EfficientNet-B0, and VGG16, it is increased by at least 1.14%, 0.54%, and 3.34%, respectively. This proves the superiority of the proposed method in emotion recognition for the elderly.

Predicting Online Gaming Behaviour Using Machine Learning Techniques

Understanding player behaviour in online gaming is essential for enhancing user engagement and retention. This study utilizes a dataset from Kaggle, capturing a wide range of player demographics and in-game metrics to predict player engagement levels categorized as 'High,' 'Medium,' or 'Low.' The dataset includes features such as age, gender, location, game genre, playtime, in-game purchases, game difficulty, session frequency, session duration, player level, and achievements. The research employs a Gaussian Naive Bayes model, with data pre-processing steps including feature selection, categorical data encoding, and scaling of numerical features. The dataset is split into training (80%) and testing (20%) sets, and a 5-fold cross-validation is used to ensure model robustness. The model's performance is evaluated using accuracy, precision, recall, and F1-score. The results show consistent performance across different folds, with an average accuracy of 84.27%, precision of 85.59%, recall of 84.27%, and F1-score of 83.98%. These findings indicate that the Gaussian Naive Bayes model can reliably predict player engagement levels, identifying significant predictors such as session frequency and in-game purchases. The study contributes to game analytics by providing a predictive model that can help game developers and marketers design more engaging gaming experiences. Future research should incorporate a broader range of features, including psychological and social factors, and explore other machine learning algorithms to enhance predictive accuracy. This study's insights are valuable for developing strategies to improve player retention and satisfaction in the gaming industry.

EARLY-STAGE DIABETES RISK PREDICTION USING MACHINE LEARNING TECHNIQUES BASED ON ENSEMBLE APPROACH

Diabetes Mellitus which is considered as one of the deadliest is a common, chronic disease. It also causes the emergence of many diseases, especially neuropathy, nephropathy and retinopathy. In this context, early diagnosis of the disease by accurately evaluating the symptoms and initiating a rapid treatment process is very important. The aim of this study is to present an effective model that can determine the diabetes risk in eary-stage with the best accuracy. To do so, the classification algorithms that are frequently used in diabetes risk estimation are supported with ensemble approaches. Firstly, the performance of Naive Bayes (NB), Trees-J48, k Nearest Neighbor (kNN) and Sequential Minimal Optimization (SMO) classifiers is analyzed separately by using a dataset of 520 samples collected with direct questionnaires from Sylhet Diabetes Hospital patients in Sylhet, Bangladesh. Then, the effects of Adabost, Bagging and Random Sub-Space (RSS) algorithms on classifier success are investigated and it is shown that the j48 classifier based on Adabost approach has the best accuracy in this dataset. Finally, the Wrapper Subset Eval (WSE) feature extraction algorithm is applied to reduce the estimation cost of diabetes and increase classification success. Thus, the best accuracy at 99% is achieved using reduced data set with proposed classifier method.

Predicting Student Success: A Comparative Examination of Machine Learning Techniques

An essential component of individual and society growth is student education. It could involve innovative curriculum design, efficient teaching techniques, educational technology, and resources. The majority of educational data mining (EDM) research, however, has concentrated on identifying at-risk children so that early, focused interventions can be given, as well as forecasting students' future performance. EDM seeks to create techniques for examining the distinct and progressively larger amounts of data produced by educational environments in order to gain a deeper comprehension of both learners and the environments in which they are taught. It is applicable to study the consequences of educational support and forecast how students will learn in the future. Using student performance datasets, we assessed the suggested feature selection technique's efficacy employing using five machine learning classifiers: Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Logistic Regression (LR). This suggested approach uses the chosen algorithm to verify training duration, analysis, recall, accuracy, and precision. But in order to determine which algorithm possessed the greatest best accuracy, they compared all of them the outcomes of this review will help academic researchers, practitioners, and professionals deal with imbalanced classification, particularly within the field of higher education

Implementation of Chernobyl optimization algorithm based feature selection approach to predict software defects

Background Software defects can have catastrophic consequences. Therefore, fixing these defects is crucial for the evolution of software. Software Defect Prediction (SDP) enables developers to investigate unscramble faults in the inaugural parts of the software progression mechanism. However, SDP faces many challenges, including the high magnitude of attributes in the datasets, which can degrade the prognostic performance of a defect forecasting model. Feature selection (FS), a compelling instrument for overcoming high dimensionality, selects only the relevant and best features while carefully discarding others. Over the years, several meta-heuristic algorithms such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Ant Colony Optimization (ACO) have been used to develop defect prediction models. However, these models suffer from several drawbacks, such as high cost, local optima trap, lower convergence rate, and higher parameter tuning. To overcome the above shortcomings, this study aims to develop an innovative FS technique, namely, the Chernobyl Optimization Algorithm (FSCOA), to unwrap the most informative features that can produce a precise prediction model while minimizing errors. Methods The proposed FSCOA approach mimicked the process of nuclear radiation while attacking humans after an explosion. The proposed FSCOA approach was combined with four widely used classifiers, namely Decision Tree (DT), K-nearest neighbor (KNN), Naive Bayes (NB), and Quantitative Discriminant Analysis (QDA), to determine the finest attributes from the SDP datasets. Furthermore, the accuracy of the recommended FSCOA method is correlated with existing FS techniques, such as FSDE, FSPSO, FSACO, and FSGA. The statistical merit of the proposed measure was verified using Friedman and Holm tests. Results The experimental findings showed that the proposed FSCOA approach yielded the best accuracy in most cases and achieved an average rank of 1.75, followed by the other studied FS approaches. Furthermore, the Holm test showed that the p-value was lower than or equivalent to the value of α/(A-i), except for the FSCOA and FSGA and FSCOA and FSACO models. Conclusion The experimental findings showed that the prospective FSCOA procedure eclipsed alternative FS techniques with higher accuracy in almost all cases while selecting optimal features.

Leveraging Naive Bayes for Enhanced Survival Analysis in Breast Cancer

The study aims to predict breast cancer survival using Naïve Bayes techniques by comparing different machine learning models on a comprehensive dataset of patient records. The main classification groups were survival and non-survival. The objective was to assess the performance of the Naïve Bayes classifier in the field of data mining and to achieve significant results in survival classification, aligning with current academic research. The Naive Bayes classifier attained an average accuracy of 91.08%, indicating consistent performance, though with some variability across different folds. Conversely, Logistic Regression achieved a higher accuracy of 94.84%, demonstrating proficiency in recognizing instances of class 1, yet encountering challenges with class 0.The Decision Tree model, with an accuracy of 93.42%, exhibited similar performance patterns. With an accuracy of 95.68%, Random Forest surpassed the Decision Tree. Nonetheless, all models encountered challenges in accurately classifying instances of class 0. The Naive Bayes algorithm was juxtaposed with K-Nearest Neighbors (KNN) and Support Vector Machines (SVM). Future research aims to enhance prediction models with novel methods and tackle the challenge of accurately identifying instances of class 0.

Acoustic-based diagnostics for UAV propeller damage using HNR and Gaussian Naive Bayes

PurposeThe purpose of this study is to determine propeller damage based on acoustic recordings taken from unmanned aerial vehicle (UAV) propellers operated at different thrust conditions on a test bench. Propeller damage is especially critical for fixed-wing UAVs to sustain a safe flight. The acoustic characteristics of the propeller vary with different propeller damages.Design/methodology/approachFor the research, feature extraction methods and machine learning techniques were used during damage detection from propeller acoustic data. First of all, sound recordings were obtained by operating five different damaged propellers and undamaged propellers under three different thrusts. Afterwards, the harmonic-to-noise ratio (HNR) feature extraction technique was applied to these audio recordings. Finally, model training and validation were performed by applying the Gaussian Naive Bayes machine learning technique to create a diagnostic approach.FindingsA high recall value of 96.19% was obtained in the performance results of the model trained according to damaged and undamaged propeller acoustic data. The precision value was 73.92% as moderate. The overall accuracy value of the model, which can be considered as general performance, was obtained as 81.24%. The F1 score has been found as 83.76% which provides a balanced measure of the model’s precision and recall values.Practical implicationsThis study include provides solid method to diagnose UAV propeller damage using acoustic data obtain from the microphone and allows identification of differently damaged propellers. Using that, the risk of in-flight failures can be reduced and maintenance costs can be lowered with addressing the occurred problems with UAV propeller before they worsen.Originality/valueThis study introduces a novel method to diagnose damaged UAV propellers using the HNR feature extraction technique and Gaussian Naive Bayes classification method. The study is a pioneer in the use of HNR and the Gaussian Naive Bayes and demonstrates its effectiveness in augmenting UAV safety by means of propeller damages. Furthermore, this approach contributes to UAV operational reliability by bridging the acoustic signal processing and machine learning.

Rapid post-disaster assessment of residential buildings using Unmanned Aerial Vehicles

Disasters impose significant costs on the built environment due to extensive damages. Accelerated by climate change, disasters such as hurricanes have recently become more frequent and destructive, underscoring the need for rapid post-disaster assessments toward swift search and rescue efforts and damage evaluations. In this regard, this study proposes an image-processing-based method employed on Unmanned Aerial Vehicle (UAV) imagery for automatic assessment of post-disaster damaged buildings. The proposed approach integrates texture-based features, including dissimilarity and homogeneity, along with edge-based features, for which Canny edge detection is employed. The edge-based features introduce novel indices representing the level of irregularity by assessing the entropy as well as the uniformity in the distribution of edge angles. The proposed features are fed into a Naïve Bayesian Classification process to classify damaged and undamaged classes, which makes it possible to account for the underlying uncertainties. The proposed method demonstrates a validation accuracy of 89.3 percent using real-life post-disaster images, effectively distinguishing between damaged and undamaged houses. The findings underscore the potential of employing UAV-captured images and advanced image processing techniques for rapid and accurate post-disaster damage assessment.

Development of an enhanced naive bayes algorithm for fake news classification

Development of an enhanced naive bayes algorithm for fake news classification