Voting Technique Research Articles

An Improved MSER using Grid Search based PCA and Ensemble Voting Technique

An Improved MSER using Grid Search based PCA and Ensemble Voting Technique

Comparison of artificial intelligence algorithm for the diagnosis of hip fracture on plain radiography with decision-making physicians: a validation study.

This study aimed to compare an algorithm developed for diagnosing hip fractures on plain radiographs with the physicians involved in diagnosing hip fractures. Radiographs labeled as fractured (n=182) and non-fractured (n=542) by an expert on proximal femur fractures were included in the study. General practitioners in the emergency department (n=3), emergency medicine (n=3), radiologists (n=3), orthopedic residents (n=3), and orthopedic surgeons (n=3) were included in the study as the labelers, who labeled the presence of fractures on the right and left sides of the proximal femoral region on each anteroposterior (AP) plain pelvis radiograph as fractured or non-fractured. In addition, all the radiographs were evaluated using an artificial intelligence (AI) algorithm consisting of 3 AI models and a majority voting technique. Each AI model evaluated each graph separately, and majority voting determined the final decision as the majority of the outputs of the 3 AI models. The results of the AI algorithm and labelling physicians included in the study were compared with the reference evaluation. Based on F-1 scores, here are the average scores of the group: majority voting (0.942) > orthopedic surgeon (0.938) > AI models (0.917) > orthopedic resident (0.858) > emergency medicine (0.758) > general practitioner (0.689) > radiologist (0.677). The AI algorithm developed in our previous study may help recognize fractures in AP pelvis in plain radiography in the emergency department for non-orthopedist physicians. Level IV, Diagnostic Study.

Detecting Fake Social Media Profiles Using the Majority Voting Approach

INTRODUCTION: The rise of social media platforms has brought about a concerning surge in the creation of fraudulent user profiles, with intentions ranging from spreading false information and perpetrating fraud to engaging in cyberbullying. The detection of these deceptive profiles has emerged as a critical imperative to safeguard the trustworthiness and security of online communities.OBJECTIVES: This paper focused on the detection and identification of fake social media profiles.METHODS: This paper introduces an innovative approach for discerning and categorizing counterfeit social media profiles by leveraging the majority voting approach. The proposed methodology integrates a range of machine learning algorithms, including Decision Trees, XGBoost, Random Forest, Extra Trees, Logistic Regression, AdaBoost and K-Nearest Neighbors each tailored to capture distinct facets of user behavior and profile attributes. This amalgamation of diverse methods results in an ensemble of classifiers, which are subsequently subjected to a majority voting mechanism to render a conclusive judgment regarding the legitimacy of a given social media profile.RESULTS: We conducted thorough experiments using a dataset containing both legitimate and fake social media profiles to determine the efficiency of our methodology. Our findings substantiate that the Majority Voting Technique surpasses individual classifiers, attaining an accuracy rate of 99.12%, a precision rate of 99.12%, a recall rate of 99.12%, and an F1-score of 99.12%.CONCLUSION: The results show that the majority vote method is reliable for detecting and recognising fake social media profiles.

Blockchain Adoption in E-Voting in Malaysian Higher Education: Concept paper

Many industries use blockchain. Its potential to change eVoting systems has received global interest. This concept paper addresses the pros and downsides of integrating blockchain technology into Malaysian higher education's eVoting system. To contribute to the scientific debate on secure, transparent, and efficient voting techniques. This topic's difficulties and prospects are examined. We analyse the benefits of blockchain technology for Malaysian electronic voting. Blockchain technology in eVoting is challenging to implement despite its benefits. This section covers important issues such as technology readiness, scalability, and regulation. These challenges must be overcome to enable blockchain-based eVoting.

Noise signature identification using mobile phones for indoor localization

Indoor localization is still nowadays a challenge with room to improve. Even though there are many different approaches that have evidenced as effective, most of them require specific hardware or infrastructure deployed along the building that can be discarded in many potential scenarios. Others that do not require such on-site infrastructure, like inertial navigation-based systems, entail certain accuracy problems due to the accumulation of errors. However, this error-accumulation can be mitigated using beacons that support the recalibration of the system. The more frequently beacons are detected, the smaller will be the accumulated error. In this work, we evaluate the use of the noise signature of the rooms of a building to pinpoint the current location of a low-cost Android device. Despite this strategy is not a complete indoor localization system (two rooms could share the same signature), it allows us to generate beacons automatically. The noise recorded by the device is preprocessed performing audio filtering, audio frame segmentation, and feature extraction. We evaluated binary (determining if the ambient sound recording belonged to a specific room) and multi-class (identifying which room an ambient noise recording belonged to by comparing it amongst the remaining 18 rooms from the original 19 rooms sampled) classification methods. Our results indicate that the two Stacking techniques and K-Nearest Neighbor (KNN) machine learning classifier are the most successful methods in binary classification with an average accuracy of 99.19%, 99,08%, and 99.04%. In multi-class classification the average accuracy for KNN is 90.77%, and 90.52% and 90.15% for both Voting techniques.

A voting gray wolf optimizer-based ensemble learning models for intrusion detection in the Internet of Things

The Internet of Things (IoT) has garnered considerable attention from academic and industrial circles as a pivotal technology in recent years. The escalation of security risks is observed to be associated with the growing interest in IoT applications. Intrusion detection systems (IDS) have been devised as viable instruments for identifying and averting malicious actions in this context. Several techniques described in academic papers are thought to be very accurate, but they cannot be used in the real world because the datasets used to build and test the models do not accurately reflect and simulate the IoT network. Existing methods, on the other hand, deal with these issues, but they are not good enough for commercial use because of their lack of precision, low detection rate, receiver operating characteristic (ROC), and false acceptance rate (FAR). The effectiveness of these solutions is predominantly dependent on individual learners and is consequently influenced by the inherent limitations of each learning algorithm. This study introduces a new approach for detecting intrusion attacks in an IoT network, which involves the use of an ensemble learning technique based on gray wolf optimizer (GWO). The novelty of this study lies in the proposed voting gray wolf optimizer (GWO) ensemble model, which incorporates two crucial components: a traffic analyzer and a classification phase engine. The model employs a voting technique to combine the probability averages of the base learners. Secondly, the combination of feature selection and feature extraction techniques is to reduce dimensionality. Thirdly, the utilization of GWO is employed to optimize the parameters of ensemble models. Similarly, the approach employs the most authentic intrusion detection datasets that are accessible and amalgamates multiple learners to generate ensemble learners. The hybridization of information gain (IG) and principal component analysis (PCA) was employed to reduce dimensionality. The study utilized a novel GWO ensemble learning approach that incorporated a decision tree, random forest, K-nearest neighbor, and multilayer perceptron for classification. To evaluate the efficacy of the proposed model, two authentic datasets, namely, BoT-IoT and UNSW-NB15, were scrutinized. The GWO-optimized ensemble model demonstrates superior accuracy when compared to other machine learning-based and deep learning models. Specifically, the model achieves an accuracy rate of 99.98%, a DR of 99.97%, a precision rate of 99.94%, an ROC rate of 99.99%, and an FAR rate of 1.30 on the BoT-IoT dataset. According to the experimental results, the proposed ensemble model optimized by GWO achieved an accuracy of 100%, a DR of 99.9%, a precision of 99.59%, an ROC of 99.40%, and an FAR of 1.5 when tested on the UNSW-NB15 dataset.

Optimizing Loop Tiling in Computing Systems through Ensemble Machine Learning Techniques

This work investigates the use of ensemble machine-learning algorithms to optimize loop-tiling in computing systems, with the goal of improving performance by predicting optimal tile sizes. It compares two approaches: independent training and averaging (soft voting) and an ensemble technique (hard voting) that employs models such as linear regression, ridge regression, and random forests. Experiments on an Intel Core i7-8565U CPU with several benchmark programs revealed that the hard voting Ensemble Approach beat the soft voting technique, providing more dependable and accurate predictions across a range of computing environments. The hard voting technique reduced execution time by around 87.5% for dynamic features and 89.89% for static features, whereas the soft voting approach showed an average drop of 75.45% for dynamic features and 78.13% for static characteristics. This work demonstrates the effectiveness of hard voting ensemble machine learning approaches in improving cache efficiency and total execution time, opening the way for future advances in high-performance computing settings.

Ensemble Classification Model With CFS-IGWO-Based Feature Selection for Cancer Detection Using Microarray Data.

Cancer is the top cause of death worldwide, and machine learning (ML) has made an indelible mark on the field of early cancer detection, thereby lowering the death toll. ML-based model for cancer diagnosis is done using two forms of data: gene expression data and microarray data. The data on gene expression levels includes many dimensions. When dealing with data with a high dimension, the efficiency of an ML-based model is decreased. Microarray data is distinguished by its high dimensionality with a greater number of features and a smaller sample size. In this work, two ensemble techniques are proposed using majority voting technique and weighted average technique. Correlation feature selection (CFS) is used for feature selection, and improved grey wolf optimizer (IGWO) is used for feature optimization. Support vector machines (SVMs), multilayer perceptron (MLP) classification, logistic regression (LR), decision tree (DT), adaptive boosting (AdaBoost) classifier, extreme learning machines (ELMs), and K-nearest neighbor (KNN) are used as classifiers. The results of each distinct base learner were then combined using weighted average and majority voting ensemble methods. Accuracy (ACC), specificity (SPE), sensitivity (SEN), precision (PRE), Matthews correlation coefficient (MCC), and F1-score (F1-S) are used to assess the performance. Our result shows that majority voting achieves better performance than the weighted average ensemble technique.

MLW-BFECF: a multi-weighted dynamic cascade forest based on bilinear feature extraction for predicting the stage of Kidney Renal Clear Cell Carcinoma on multi-modal gene data.

The stage prediction of kidney renal clear cell carcinoma (KIRC) is important for the diagnosis, personalized treatment, and prognosis of patients. Many prediction methods have been proposed, but most of them are based on unimodal gene data, and their accuracy is difficult to further improve. Therefore, we propose a novel multi-weighted dynamic cascade forest based on the bilinear feature extraction (MLW-BFECF) model for stage prediction of KIRC using multimodal gene datasets (RNA-seq, CNA, and methylation). The proposed model utilizes a dynamic cascade framework with shuffle layers to prevent early degradation of the model. In each cascade layer, a voting technique based on three gene selection algorithms is first employed to effectively retain gene features more relevant to KIRC and eliminate redundant information in gene features. Then, two new bilinear models based on the gated attention mechanism are proposed to better extract new intra-modal and inter-modal gene features; Finally, based on the idea of the bagging, a multi-weighted ensemble forest classifiers module is proposed to extract and fuse probabilistic features of the three-modal gene data. A series of experiments demonstrate that the MLW-BFECF model based on the three-modal KIRC dataset achieves the highest prediction performance with an accuracy of 88.92%.

Contextual emotion detection using ensemble deep learning

Emotion detection from online textual information is gaining more attention due to its usefulness in understanding users’ behaviors and their desires. This is driven by the large amounts of texts from different sources such as social media and shopping websites. Recent studies investigated the benefits of deep learning in the detection of emotions from textual conversations. In this paper, we study the performance of several deep learning and transformer-based models in the classification of emotions in English conversations. Further, we apply ensemble learning using a majority voting technique to improve the overall classification performance. We evaluated our proposed models on the SemEval 2019 Task 3 public dataset that categorizes emotions as Happy, Angry, Sad, and Others. The results show that our models can successfully distinguish the three main classes of emotions and separate them from Others in a highly imbalanced dataset. The transformer-based models achieved a micro-averaged F1-score of up to 75.55%, whereas the RNN-based models only reached 67.03%. Further, we show that the ensemble model significantly improves the overall performance and achieves a micro-averaged F1-score of 77.07%.

Hybrid prediction model for reinforcements' corrosion stage by multiple nondestructive electrochemical indices

Assessment of the corrosion stage in reinforcing steel within reinforced concrete structures is crucial for determining the durability of such structures. However, research on the correlation between electrochemical indicators and the stages of corrosion experienced by reinforcing steel is limited. The primary impediment to this is the scarcity of study samples capable of simultaneously capturing data on half-cell potential (Ecorr), corrosion current (icorr), polarisation resistance (Rρ), and concrete resistivity (ρ) under specific structural corrosion conditions. It is essential to integrate the findings from multiple electrochemical indicators to determine the corrosion stage. Therefore, a Bayesian network is employed for the purpose of training to model the relationships among various electrochemical indicators and predict the missing data, thereby generating a comprehensive dataset. Subsequently, multilayer perceptron, support vector machine, K-nearest neighbour, naive Bayes, and decision tree models are trained and integrated using bagging resampling and soft voting techniques. Finally, a hybrid classifier system (HCS) is devised with Ecorr, icorr, Rρ, and ρ as inputs and used to determine the probability of reinforcing steel being in a particular corrosion stage. Based on the verification of the HCS model, it can be concluded that Ecorr has the greatest influence on determining the corrosion stage of reinforcing steel, followed by icorr and Rρ. When Ecorr, Rρ, and ρ are less than −242 mV, 209 kΩ cm2 and 2089 Ω m, respectively, and icorr is greater than 0.07 μA cm-2, the structure is considered to have entered the corrosion initiation stage and has begun to corrode. When Ecorr, Rρ, and ρ are less than −584 mV, 2.4 kΩ cm2 and 95 Ω m, respectively, and icorr is greater than 1.20 μA cm-2, the structure is considered to have entered the corrosion expansion stage. This study presents a quick and accurate method for the determination of the corrosion stage of reinforcing steel in RC structures.

A Study of Vietnamese Sentiment Classification with Ensemble Pre-Trained Language Models

Sentiment Analysis (SA) has attracted increasing research attention in recent years. Most existing works tackle the SA task by fine-tuning single pre-trained language models combined with specific layers. Despite their effectiveness, the previous studies overlooked the utilization of feature representations from various contextual language models. Ensemble learning techniques have garnered increasing attention within the field of Natural Language Processing (NLP). However, there is still room for improvement in ensemble models for the SA task, particularly in the aspect-level SA task. Furthermore, the utilization of heterogeneous ensembles, which combine various pre-trained transformer-based language models, may prove beneficial in enhancing overall performance by incorporating diverse linguistic representations. This paper introduces two ensemble models that leverage soft voting and feature fusion techniques by combining individual pre-trained transformer-based language models for the SA task. The latest transformer-based models, including PhoBERT, XLM, XLM-Align, InfoXLM, and viBERT_FPT, are employed to integrate knowledge and representations using feature fusion and a soft voting strategy. We conducted extensive experiments on various Vietnamese benchmark datasets, encompassing sentence-level, document-level, and aspect-level SA. The experimental results demonstrate that our approaches outperform most existing methods, achieving new state-of-the-art results with F1-weighted scores of 94.03%, 95.65%, 75.36%, and 76.23% on the UIT_VSFC, Aivivn, UIT_ABSA for the restaurant domain, and UIT_ViSFD datasets, respectively.

Crystal violet removal using ZIF-60: Batch adsorption studies, mechanistic & machine learning modeling

This study, for the first time, reported the utilization of ZIF-60 for the adsorptive removal of organic pollutant from contaminated water. Characterization techniques were used to confirm the synthesis of ZIF-60 adsorbent which was then used to study the uptake of crystal violet (CV) from the aqueous system. A remarkably high experimental uptake (in excess of 7000 mg/g) of CV onto ZIF-60 was noted which was due to the careful selection of process parameters while conducting the batch adsorption studies. Response surface methodology technique was utilized as the tool for response optimization and for studying the effect of varying adsorption process parameters. Temperature had the most significant influence on CV adsorption followed by CV concentration, ZIF-60 dose and pH. Adsorption kinetics and isotherm investigations were conducted based on the optimized adsorption conditions, and the obtained good data fitting with more than one model suggests an intricate adsorption process. On the other hand, the thermodynamic studies revealed a highly endothermic and spontaneous process. The post adsorption analysis revealed π-π stacking interaction as the dominant force driving the uptake of CV onto ZIF-60. Additionally, several machine learning models that were based on distinct algorithms were utilized for the prediction of CV adsorption onto ZIF-60. An ensemble model that was based on the combination of several optimized models was created using the voting technique; this model proved to be the most accurate in predicting the CV uptake by ZIF-60 as suggested by its superior value for several evaluation metrics.

Deep-kidney: an effective deep learning framework for chronic kidney disease prediction

Chronic kidney disease (CKD) is one of today’s most serious illnesses. Because this disease usually does not manifest itself until the kidney is severely damaged, early detection saves many people’s lives. Therefore, the contribution of the current paper is proposing three predictive models to predict CKD possible occurrence within 6 or 12 months before disease existence namely; convolutional neural network (CNN), long short-term memory (LSTM) model, and deep ensemble model. The deep ensemble model fuses three base deep learning classifiers (CNN, LSTM, and LSTM-BLSTM) using majority voting technique. To evaluate the performance of the proposed models, several experiments were conducted on two different public datasets. Among the predictive models and the reached results, the deep ensemble model is superior to all the other models, with an accuracy of 0.993 and 0.992 for the 6-month data and 12-month data predictions, respectively.

Machine Learning Model of ResNet50-Ensemble Voting for Malignant-Benign Small Pulmonary Nodule Classification on Computed Tomography Images.

The early detection of benign and malignant lung tumors enabled patients to diagnose lesions and implement appropriate health measures earlier, dramatically improving lung cancer patients' quality of living. Machine learning methods performed admirably when recognizing small benign and malignant lung nodules. However, exploration and investigation are required to fully leverage the potential of machine learning in distinguishing between benign and malignant small lung nodules. The aim of this study was to develop and evaluate the ResNet50-Ensemble Voting model for detecting the benign and malignant nature of small pulmonary nodules (<20 mm) based on CT images. In this study, 834 CT imaging data from 396 patients with small pulmonary nodules were gathered and randomly assigned to the training and validation sets in an 8:2 ratio. ResNet50 and VGG16 algorithms were utilized to extract CT image features, followed by XGBoost, SVM, and Ensemble Voting techniques for classification, for a total of ten different classes of machine learning combinatorial classifiers. Indicators such as accuracy, sensitivity, and specificity were used to assess the models. The collected features are also shown to investigate the contrasts between them. The algorithm we presented, ResNet50-Ensemble Voting, performed best in the test set, with an accuracy of 0.943 (0.938, 0.948) and sensitivity and specificity of 0.964 and 0.911, respectively. VGG16-Ensemble Voting had an accuracy of 0.887 (0.880, 0.894), with a sensitivity and specificity of 0.952 and 0.784, respectively. Machine learning models that were implemented and integrated ResNet50-Ensemble Voting performed exceptionally well in identifying benign and malignant small pulmonary nodules (<20 mm) from various sites, which might help doctors in accurately diagnosing the nature of early-stage lung nodules in clinical practice.

Image patch-based deep learning approach for crop and weed recognition

Accurate classification of weed species in crop plants plays a crucial role in precision agriculture by enabling targeted treatment. Recent studies show that artificial intelligence deep learning (DL) models achieve promising solutions. However, several challenging issues, such as lack of adequate training data, inter-class similarity between weed species and intra-class dissimilarity between the images of the same weed species at different growth stages or for other reasons (e.g., variations in lighting conditions, image capturing mechanism, agricultural field environments) limit their performance. In this research, we propose an image based weed classification pipeline where a patch of the image is considered at a time to improve the performance. We first enhance the images using generative adversarial networks. The enhanced images are divided into overlapping patches, a subset of which are used for training the DL models. For selecting the most informative patches, we use the variance of Laplacian and the mean frequency of Fast Fourier Transforms. At test time, the model's outputs are fused using a weighted majority voting technique to infer the class label of an image. The proposed pipeline was evaluated using 10 state-of-the-art DL models on four publicly available crop weed datasets: DeepWeeds, Cotton weed, Corn weed, and Cotton Tomato weed. Our pipeline achieved significant performance improvements on all four datasets. DenseNet201 achieved the top performance with F1 scores of 98.49%, 99.83% and 100% on Deepweeds, Corn weed and Cotton Tomato weed datasets, respectively. The highest F1 score on the Cotton weed dataset was 98.96%, obtained by InceptionResNetV2. Moreover, the proposed pipeline addressed the issues of intra-class dissimilarity and inter-class similarity in the DeepWeeds dataset and more accurately classified the minority weed classes in the Cotton weed dataset. This performance indicates that the proposed pipeline can be used in farming applications.

COVID-19 Diagnosis Based on Multi Deep Neural Networks of Chest Lung X-Ray Images

In this paper, increased attempts are carried out to develop the deep neural networks to diagnose COVID-19 based on Chest X-Ray (CXR). This work introduces a new diagnostic system, utilizing different deep neural networks, InceptionV3, DenseNet121, ResNet50, VGG16 and MobileNetV2 models, to classify CXR images into healthy normal, pneumonia bacterial and COVID-19. Additionally, transfer learning and data augmentation techniques are utilized to solve the problem of scarcity of CXR images; hence the over-fitting of deep models will be avoided. The proposed end-to-end deep system is applied on the dataset sourced from COVID-19 lung CXR images from Kaggle and the IEEE8020 COVID-19 CXR dataset which is introduced by John Hopkins Hospital. The classification results reveal that diagnostic of CXR lung images by InceptionV3 provides the best results, with 98.87% accuracy, 98.88% area under the curve (AUC), 98.98% sensitivity, 98.79% precision, 97.99% F1- score and 1.4574 s computational time. Finally, the ensemble classifier with a voting strategy is proposed to boost the diagnosis performance using the multiple convolutional neural networks (CNNs) instead of one. The classification results are aggregated of each image using the voting technique. It is observed that the performance of the proposed ensemble classifiers with voting strategy reaches the state-of-art performance.

Standard fetal ultrasound plane classification based on stacked ensemble of deep learning models

Identifying standard fetal ultrasound (US) planes with key anatomical structures during mid-pregnancy prenatal screening is crucial for measuring fetal growth parameters and early detection of abnormalities. However, obtaining these standard planes is laborious and time-consuming and depends on the clinical experience of sonographers. Automatic detection of these planes can aid sonographers in identifying the correct standard planes. In recent times, various deep learning techniques have developed to automate the detection of standard fetal US planes. However, a common limitation among these approaches is their dependence on a single model prediction to make the final decision, which introduces the possibility of inaccuracies. Therefore, we propose an automated identification of commonly used standard fetal US planes based on the stacking ensemble of deep convolutional neural networks (CNN). The stacking ensemble method employs three pre-trained deep CNNs: AlexNet, VGG-19, and DarkNet-19. Softmax and random forest classifiers are used to get predictions from deep CNNs. The final prediction is made using the absolute majority voting technique. A publicly available fetal US dataset is employed to evaluate the performance of the stacking ensemble approach. The proposed ensemble model classifies fetal US planes into six distinct classes: abdomen, brain, femur, thorax, maternal cervix, and other (less commonly employed planes, such as kidney, and limbs) fetal planes. Experimental findings demonstrate that the stacking ensemble approach achieved high performance with an accuracy of 95.69%, precision of 94.02%, recall of 96.28%, F1-score of 95.08%, specificity of 99.12%, and Matthews correlation coefficient of 94.19% compared to individual deep CNN models and other competing methods.

Machine failure prediction using joint reserve intelligence with feature selection technique

A model with high accuracy of machine failure prediction is important for any machine life cycle. In this paper, a prediction model based on machine learning methods is proposed. The used method is a combination of machine learning algorithms and techniques. The machine learning algorithm is a data mining technique that has been widely used as a prediction model for classifying problems. Five algorithms have been tested including JRIP, logistic, KStar, Bayes network and decision table machine learning. The evaluation process is done by applying the algorithms on a predictive dataset using different performance measures. In the proposed model, the feature selection and voting techniques are used and applied in the classification process for each classifier. From the comparison of the result, the feature selection shows the best performance result. Paired t-test evaluation measures were considered to confirm our conclusion. The best accuracy result among the five classifiers shows that joint reserve intelligence classifier can be used to predict the failure with an accuracy high as 0.983. Applying classifier subset evaluation using the JRIP classifier can enhance the accuracy result to be 0.985. The finding shows that the proposed model improves the results of the classifiers.

Enhancing Parkinson's disease severity assessment through voice-based wavelet scattering, optimized model selection, and weighted majority voting

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms that significantly impact an individual's quality of life. Voice changes have shown promise as early indicators of PD, making voice analysis a valuable tool for early detection and intervention. This study aims to assess and detect the severity of PD through voice analysis using the mobile device voice recordings dataset. The dataset consisted of recordings from PD patients at different stages of the disease and healthy control subjects. A novel approach was employed, incorporating a voice activity detection algorithm for speech segmentation and the wavelet scattering transform for feature extraction. A Bayesian optimization technique is used to fine-tune the hyperparameters of seven commonly used classifiers and optimize the performance of machine learning classifiers for PD severity detection. AdaBoost and K-nearest neighbor consistently demonstrated superior performance across various evaluation metrics among the classifiers. Furthermore, a weighted majority voting (WMV) technique is implemented, leveraging the predictions of multiple models to achieve a near-perfect accuracy of 98.62%, improving classification accuracy. The results highlight the promising potential of voice analysis in PD diagnosis and monitoring. Integrating advanced signal processing techniques and machine learning models provides reliable and accessible tools for PD assessment, facilitating early intervention and improving patient outcomes. This study contributes to the field by demonstrating the effectiveness of the proposed methodology and the significant role of WMV in enhancing classification accuracy for PD severity detection.