Random Forest Tree Classifier Research Articles

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

Internet of Things (IoT) devices often operate with limited resources while interacting with users and their environment, generating a wealth of data. Machine learning models interpret such sensor data, enabling accurate predictions and informed decisions. However, the sheer volume of data from billions of devices can overwhelm networks, making traditional cloud data processing inefficient for IoT applications. This paper presents a comprehensive survey of recent advances in models, architectures, hardware, and design requirements for deploying machine learning on low-resource devices at the edge and in cloud networks. Prominent IoT devices tailored to integrate edge intelligence include Raspberry Pi, NVIDIA’s Jetson, Arduino Nano 33 BLE Sense, STM32 Microcontrollers, SparkFun Edge, Google Coral Dev Board, and Beaglebone AI. These devices are boosted with custom AI frameworks, such as TensorFlow Lite, OpenEI, Core ML, Caffe2, and MXNet, to empower ML and DL tasks (e.g., object detection and gesture recognition). Both traditional machine learning (e.g., random forest, logistic regression) and deep learning methods (e.g., ResNet-50, YOLOv4, LSTM) are deployed on devices, distributed edge, and distributed cloud computing. Moreover, we analyzed 1000 recent publications on “ML in IoT” from IEEE Xplore using support vector machine, random forest, and decision tree classifiers to identify emerging topics and application domains. Hot topics included big data, cloud, edge, multimedia, security, privacy, QoS, and activity recognition, while critical domains included industry, healthcare, agriculture, transportation, smart homes and cities, and assisted living. The major challenges hindering the implementation of edge machine learning include encrypting sensitive user data for security and privacy on edge devices, efficiently managing resources of edge nodes through distributed learning architectures, and balancing the energy limitations of edge devices and the energy demands of machine learning.

Prediction of student exam performance using data mining classification algorithms

AbstractStudent outcomes are of great importance in higher education institutions. Accreditation bodies focus on them as an indicator to measure the performance and effectiveness of the institution. Forecasting students’ academic performance is crucial for every educational establishment seeking to enhance performance and perseverance of its students and reduce the failure rate in the future. The main goal of this study is to predict the performance of undergraduate first-level students in the Computer Department during the years 2016 to 2021 to enhance their performance in future by discovering the best algorithm use to analyze the educational data to identify the students’ academic performance. The secondary data was collected by reviewing the Student Affairs Department at the Faculty of Specific Education at Damietta University, in addition to the Statistics Department at the university. The dataset contained 830 instances after excluding 139 instances of missing values, irrelevant rows, and outliers. The dataset was divided into train (577 instances (70%)), test (253 instances (30%)) and involved six features such year, midterm, practical exam, writing exam, final total degree, and grade. This paper use five machine learning (ML) algorithms which was selected according to the literature review and high accuracy in predicting educational data mining: For the purpose of comparison, a number of different machine learning algorithms, such as Random Forest, Decision Tree, Naive Bayes, Neural Network, and K-Nearest Neighbours, were utilized and evaluated with evaluation metrics such as confusion matrix, accuracy, precision, recall, and F-measure. The Random Forest and Decision Tree classifiers emerged as the top-performing algorithms, accurately categorizing 250 instances when predicting students' performance in the statistics course. This was determined based on the findings of the study. Out of a total of 253 instances that were included in the testing set, they only made three incorrect classifications.

EEG based automated detection of seizure using machine learning approach and traditional features

The detection of epileptic seizures is key for neurologists to initiate the right treatment at the earliest. However, the traditional methods are dependent on manual diagnosis which are error-prone. Hence, the current article presents an automated machine learning-based approach with competing results to detect seizures and its types using traditional features. Towards this goal, the present paper explores the performance of two fractal parameters namely Higuchi and, Kat’z fractal dimension, along with power spectral density and spectral entropy to detect EEG signals with seizure and non-seizure characteristics. To validate the performance via cross-validation technique, the features are extracted and experimented from three publically available databases such as Bern-Barcelona (focal, non-focal), Khas (preictal, interictal, ictal), and Bonn. Thereafter evaluated with different machine learning algorithms like Random Forest classifier, Ada Boost classifier, Gradient Boosting classifier, Extra Tree classifier, SVM classifier, and Naive Bayes classifier. The experiments showed 100% F1 score and accuracy in classifying focal and non-focal signals for random forest and extra tree classifier methods. To the best of our knowledge, this is the first paper reporting this score for the Bern-Barcelona dataset. The same features are also experimented for the detection of interictal, ictal and preictal scenarios. In the process, 100% accuracy is obtained in classifying interictal and ictal EEG signals, and an accuracy of 94% is attained in classifying interictal-preictal signals. This score is 14% higher than the current state-of-the-art methods using the same database. The features are also used in the Bonn database, where the proposed approach gave an accuracy of 100% same as the state-of-the-art methods.

Fake Job Posting Detection

In the realm of online job platforms, the rise of fraudulent job postings poses a significant challenge, undermining the credibility and reliability of these platforms. To address this issue, we propose a machine learning- based solution that leverages the power of Random Forest, Logistic Regression, and Decision Tree classifiers. Through the compilation of a comprehensive dataset containing labeled job postings, we embark on a journey of preprocessing and feature engineering to extract pertinent information from the postings, including textual attributes, geographic details, salary indications, and company profiles. Splitting the dataset into training and testing subsets enables us to meticulously train and evaluate the performance of each classifier, utilizing established metrics such as accuracy, precision, recall, and F1-score to quantify their efficacy in discerning between authentic and fake job listings. Our study goes beyond mere model training and evaluation, delving into the intricacies of imbalanced data handling and the practicalities of model deployment and maintenance. By examining the comparative strengths and weaknesses of Random Forest, Logistic Regression, and Decision Tree classifiers, we provide actionable insights for enhancing the integrity of online job platforms through advanced machine learning techniques. With our approach, we aim to not only detect and mitigate the prevalence of fake job postings but also to fortify the trust and credibility of online job- seeking platforms, thereby fostering a more secure and reliable environment for both job seekers and employers alike.

RDET stacking classifier: a novel machine learning based approach for stroke prediction using imbalance data

The main cause of stroke is the unexpected blockage of blood flow to the brain. The brain cells die if blood is not supplied to them, resulting in body disability. The timely identification of medical conditions ensures patients receive the necessary treatments and assistance. This early diagnosis plays a crucial role in managing symptoms effectively and enhancing the overall quality of life for individuals affected by the stroke. The research proposed an ensemble machine learning (ML) model that predicts brain stroke while reducing parameters and computational complexity. The dataset was obtained from an open-source website Kaggle and the total number of participants is 3,254. However, this dataset needs a significant class imbalance problem. To address this issue, we utilized Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADAYSN), a technique for oversampling issues. The primary focus of this study centers around developing a stacking and voting approach that exhibits exceptional performance. We propose a stacking ensemble classifier that is more accurate and effective in predicting stroke disease in order to improve the classifier’s performance and minimize overfitting problems. To create a final stronger classifier, the study used three tree-based ML classifiers. Hyperparameters are used to train and fine-tune the random forest (RF), decision tree (DT), and extra tree classifier (ETC), after which they were combined using a stacking classifier and a k-fold cross-validation technique. The effectiveness of this method is verified through the utilization of metrics such as accuracy, precision, recall, and F1-score. In addition, we utilized nine ML classifiers with Hyper-parameter tuning to predict the stroke and compare the effectiveness of Proposed approach with these classifiers. The experimental outcomes demonstrated the superior performance of the stacking classification method compared to other approaches. The stacking method achieved a remarkable accuracy of 100% as well as exceptional F1-score, precision, and recall score. The proposed approach demonstrates a higher rate of accurate predictions compared to previous techniques.

Evaluation of different machine learning algorithms for LULC classification in heterogeneous landscape by using remote sensing and GIS techniques

Remote sensing land cover classification plays a crucial role in detecting changes, urbanization planning, mapping and monitoring land cover on earth surface. It is very challenging to get accurate result in remote sensing data because different classifiers are very much area dependent. Different classifiers such as decision tree (DT), K-nearest neighbor (KNN), artificial neural network (ANN), support vector machine (SVM), boosted decision tree (BDT), random forest (RF), classification and regression tree (CART) and maximum likelihood classifiers (MLC), have different accuracies for the same classes. Several studies have utilized remote sensing and GIS tools to investigate changes in land use and land cover (LULC) using different classifiers. Seasonal rivers which should be classified as water bodies are mostly classified as urban area with the conventional land cover classification schemes because spectral reflectance of these river bodies is similar to urban area due to stones present in their river bed. It is very difficult to distinguish between these river beds (which are mostly found in various districts of Uttarakhand, India) and urban area in remote sensing images. In this paper, we present a new method to distinguish these river beds with the urban area and to separate other classes easily. First of all different spectral indices such as NDVI, NDBI, EVI and MNDWI are extracted from a high resolution Sentinel-2 MSI image then these indices are integrated with Sentinel-2 MSI image for classification of different land cover classes by using four different machine learning classifiers such as RF, SVM, DT and CART. The obtained results confirm the performance strength of the suggested method as there is much improvement in accuracy.

Integrating Machine Learning for Accurate Prediction of Early Diabetes

In the current world, where diabetes is day by day becoming a very common and fatal disease, it's important that proper measures be taken in order to deal with it. As per the studies, early prediction of diabetes can lead to improved treatment to avoid further complications of the disease, and in order to do so efficiently, machine learning techniques are a great deal. In this study, various factors are taken into consideration, like blood pressure, pregnancy, glucose level, age, insulin, skin thickness, and diabetes pedigree function, which together can be useful to predict whether a person has a risk of developing diabetes or not and help society with the early diagnosis of diabetes. This model is trained using three main classification algorithms, namely support vector, random forest, and decision tree classifiers. The prediction results of each of the classifiers are summarized in this study, and the decision tree gives 78.89% accuracy.

IoMT based smart healthcare system to control outbreaks of the COVID-19 pandemic

The COVID-19 pandemic caused millions of infections and deaths globally requiring effective solutions to fight the pandemic. The Internet of Things (IoT) provides data transmission without human intervention and thus mitigates infection chances. A road map is discussed in this study regarding the role of IoT applications to combat COVID-19. In addition, a real-time solution is provided to identify and monitor COVID-19 patients. The proposed framework comprises data collection using IoT-based devices, a health or quarantine center, a data warehouse for artificial intelligence (AI)-based analysis, and healthcare professionals to provide treatment. The efficacy of several machine learning models is also analyzed for the prediction of the severity level of COVID-19 patients using real-time IoT data and a dataset named ‘COVID Symptoms Checker’. The proposed ensemble model combines random forest and extra tree classifiers using a soft voting criterion and achieves superior results with a 0.922 accuracy score. The use of IoT applications is found to support medical professionals in investigating the features of the contagious disease and support managing the COVID pandemic more efficiently.

Comparing the Effectiveness of Different Machine Learning Algorithms for Crop Cover Classification Using Sentinel 2

Crop cover mapping is an essential tool for controlling and enhancing agricultural productivity. By determining the spatial distribution of different crop types, solidified judgements regarding crop planning, crop management, and risk management can be made. Crop cover classification using optical data pose constraints in terms of spatial and spectral resolution. With Sentinel – 2 data providing the ground information at 10m resolution, users may choose the best spectral band combinations and temporal frame by analysing the spectral-temporal information of different crops. The crop categorization map for the Kallakurichi and Villupuram districts were created in this study using the Random Forest (RF) and Decision tree (C5.0) classifiers. The study mainly focuses on comparing the classification accuracy of two classifiers and figuring out the best classifiers for crop cover mapping with respect to the study area. The ground truth information collected, were partitioned into calibration and validation datasets and the validation resulted with the Overall Accuracy (OA) and kappa coefficient of 66%; 0.63 and 60%; 0.67 for RF and C5.0 algorithms, respectively. From the results, it could be concluded that the RF classifier performed comparatively better than C5.0, thus making it suitable for crop cover classification.

Ensemble-Based AI System for Brain Stroke Prediction

Abstract: Timely detection and proactive measures to avert stroke are of utmost importance due to the significant likelihood of severe disabilities or fatal consequences associated with this condition. It is imperative to promptly administer appropriate thrombolytic or anticoagulant medications for both ischemic and hemorrhagic strokes. The pivotal and initial stage revolves around timely recognition of the initial indicators of a stroke, which may differ among individuals, and promptly seeking medical intervention within the prescribed treatment window. This research introduces a machine learning-based system that employs real-time measurements of electrocardiogram (ECG) and photoplethysmography (PPG) data to forecast and interpret stroke prognostic symptoms in a meaningful way. To achieve real-time stroke prediction, we have developed and implemented an ensemble structure voting classifier that combines SVM, Random Forest, and decision tree classifiers. This approach accurately forecasts stroke diagnosis in patients and can be easily implemented by utilizing a patient's ECG and PPG attribute data.

Early detection of Alzheimer’s Disease using MRI images and deep learning techniques

AbstractBackgroundAlzheimer’s is a neurodegenerative disease that diminishes the capability of individuals to perform their daily activities. It is an irreversible disease once it is started not able to stop it but early detection of the disease can help to slow down the progression of it by starting early treatment. In this experiment, we use MRI images and clinical study data available at the Alzheimer’s Disease Neuroimaging Initiative data set to detect the different stages of Alzheimer’s and forecast the duration required for the conversion from mild cognitive impairment to Alzheimer’s. The clinical signs of Alzheimer’s disease are portrayed by Age, patient education details, the progression rate of disease, and cognitive information. Done experiment by using Machine learning and Deep learning techniques like multi perceptron networks, Random forest, SVM, and decision tree classifiers to do binary and multi‐classification of Alzheimer’s Disease(AD), Late Mild cognitive impairment (MCI), Early cognitive impairment (EMCI), and Cognitive control(CN).MethodDeep learning techniqueResultan average the model achieved 91.25% of precision indicates 91.25% total positive images are correctly classified, 91.25% of sensitivity indicates 91.25% of total images are detected remaining are not, and achieved 95.52% of accuracyConclusionAlzheimer’s is a neuron degenerative disease. Currently no treatment is available to cure AD or to stop progress of disease. But detection of dementia at its early stage might help individual family to think about their future regarding financial issues. In this work we tried our best to categorize the different phases of AD. We used a deep neural network and achieved an accuracy of 95.52%.

ACME: automated classification model for E-learning feedback

ABSTRACT E-learning is the process of sharing knowledge out of the traditional classrooms through different online tools using internet. The availability and use of these tools are not easy for every student. Many institutions gather e-learning feedback to know the problems of students to improve their systems. In e-learning systems, typically a high volume of electronic feedback is received every day. In order to respond to feedback quickly and to address the issues raised by the stakeholders timely, it is desirable to provide an intelligent and automatic classification of the feedback. This study proposes a feedback classification model, named “ACME: Automated Classification Model for E-learning Feedback” to fulfill this need. The proposed model is capable of both sentiment analysis and category classification of the feedback. In this regard, feedback from 20,000 records has been collected from many online sources, such as social media platforms, surveys, and questionnaires. To build a baseline corpus, these feedbacks were analyzed and characterized in two dimensions, that is category and sentiment. The model achieved an accuracy of 98% and 99% by using a Random Forest and Decision Tree classifier, respectively.

Performance Comparison of the Cogent Confabulation Classifier with Other Commonly Used Supervised Machine Learning Algorithms for Bathing Water Quality Assessment

The purpose of this study was to implement a reliable model for bathing water quality prediction using the Cogent Confabulation classifier and to compare it with other well-known classifiers. This study is a continuation of a previously published work and focuses on the areas of Kaštela Bay and the Brač Channel, located in the Republic of Croatia. The Cogent Confabulation classifier is a thorough and simple method for data classification based on the cogency measure for observed classes. To implement the model, we used data sets constructed of remote sensing data (band values) and in situ measurements presenting ground-truth bathing water quality. Satellite data was retrieved from the Sentinel-3 OLCI satellite and it was atmospherically corrected based on the characteristics and specifications of band wavelengths. The results showed that the Random Forest, K-Nearest Neighbour, and Decision Tree classifiers outperformed the Cogent Confabulation classifier. However, results showed that the Cogent Confabulation classifier achieved better results compared to classifiers based on Bayesian theory. Additionally, a qualitative analysis of the four best classifiers was conducted using spatial maps created in the QGIS tool.

Skill-level classification and performance evaluation for endoscopic sleeve gastroplasty.

We previously developed grading metrics for quantitative performance measurement for simulated endoscopic sleeve gastroplasty (ESG) to create a scalar reference to classify subjects into experts and novices. In this work, we used synthetic data generation and expanded our skill level analysis using machine learning techniques. We used the synthetic data generation algorithm SMOTE to expand and balance our dataset of seven actual simulated ESG procedures using synthetic data. We performed optimization to seek optimum metrics to classify experts and novices by identifying the most critical and distinctive sub-tasks. We used support vector machine (SVM), AdaBoost, K-nearest neighbors (KNN) Kernel Fisher discriminant analysis (KFDA), random forest, and decision tree classifiers to classify surgeons as experts or novices after grading. Furthermore, we used an optimization model to create weights for each task and separate the clusters by maximizing the distance between the expert and novice scores. We split our dataset into a training set of 15 samples and a testing dataset of five samples. We put this dataset through six classifiers, SVM, KFDA, AdaBoost, KNN, random forest, and decision tree, resulting in 0.94, 0.94, 1.00, 1.00, 1.00, and 1.00 accuracy, respectively, for training and 1.00 accuracy for the testing results for SVM and AdaBoost. Our optimization model maximized the distance between the expert and novice groups from 2 to 53.72. This paper shows that feature reduction, in combination with classification algorithms such as SVM and KNN, can be used in tandem to classify endoscopists as experts or novices based on their results recorded using our grading metrics. Furthermore, this work introduces a non-linear constraint optimization to separate the two clusters and find the most important tasks using weights.

Performance analysis and comparison of Machine Learning and LoRa-based Healthcare model.

Diabetes Mellitus (DM) is a widespread condition that is one of the main causes of health disasters around the world, and health monitoring is one of the sustainable development topics. Currently, the Internet of Things (IoT) and Machine Learning (ML) technologies work together to provide a reliable method of monitoring and predicting Diabetes Mellitus. In this paper, we present the performance of a model for patient real-time data collection that employs the Hybrid Enhanced Adaptive Data Rate (HEADR) algorithm for the Long-Range (LoRa) protocol of the IoT. On the Contiki Cooja simulator, the LoRa protocol's performance is measured in terms of high dissemination and dynamic data transmission range allocation. Furthermore, by employing classification methods for the detection of diabetes severity levels on acquired data via the LoRa (HEADR) protocol, Machine Learning prediction takes place. For prediction, a variety of Machine Learning classifiers are employed, and the final results are compared with the already existing models where the Random Forest and Decision Tree classifiers outperform the others in terms of precision, recall, F-measure, and receiver operating curve (ROC) in the Python programming language. We also discovered that using k-fold cross-validation on k-neighbors, Logistic regression (LR), and Gaussian Nave Bayes (GNB) classifiers boosted the accuracy.

Efficient Data Preprocessing with Ensemble Machine Learning Technique for the Early Detection of Chronic Kidney Disease

It is a serious global health concern that chronic kidney disease (CKD) kills millions of people each year as a result of poor lifestyle choices and inherited factors. Effective prediction tools for prior detection are essential due to the growing number of patients with this disease. By utilizing machine learning (ML) approaches, this study aids specialists in studying precautionary measures for CKD through prior detection. The main objective of this paper is to predict and classify chronic kidney disease using ML approaches on a publicly available dataset. The dataset of CKD has been taken from the publicly available and accessible dataset Irvine ML Repository, which included 400 instances. ML methods (Support Vector Machine (SVM), K-Nearest Neighbors (KNN), random forest (RF), Logistic Regression (LR), and Decision Tree (DT) Classifier) are used as base learners and their performance has been compared with eXtreme Gradient Boosting (XGBoost). All ML algorithms are evaluated against different performance parameters: accuracy, recall, precision, and F1-measure. The results indicated that XGBoost outperformed with 98.00% accuracy as compared to other ML algorithms. For policymakers to forecast patterns of CKD in the population, the model put forth in this paper may be helpful. The model may enable careful monitoring of individuals who are at risk, early CKD detection, better resource allocation, and management that is patient-centered.

Fault Detection in Steel Surfaces Using Deep Learning Approaches

There is sustainable growth in the industries in various developing nations. Quality maintenance of the product under development is an essential part of the product development process. Product quality affects the performance of the product. Various kinds of issues in the manufacturing can impact negatively on the product. Therefore, it is needed to make sure that the manufactured products are fault-free by establishing and employing such softwares that will ultimately bring some ease in the fault detection process. This paper aims to diagnose faults on steel surfaces by using convolutional neural networks and classification by making use of 5 different types of classifiers. They are Support Vector machines, Naive Bayes Classifier, Decision Tree, K-nearest Neighbors, and Random Forest. We have used 4 different types of models namely, Alexnet, InceptionV3, Resnet and VGG16. The testing accuracy was found to be maximum for the VGG16 model which was recorded to be 75.02%. Among the classifiers, the best accuracy was found out with Random Forest and Decision Tree classifiers to be 74.9% and 74.3% respectively. The defects are classified among the 4 categories of defects and are highlighted using image segmentation.

Deep Neural Network Based Cardio Vascular Disease Prediction Using Binarized Butterfly Optimization

In this digital era, Cardio Vascular Disease (CVD) has become the leading cause of death which has led to the mortality of 17.9 million lives each year. Earlier Diagnosis of the people who are at higher risk of CVDs helps them to receive proper treatment and helps prevent deaths. It becomes inevitable to propose a solution to predict the CVD with high accuracy. A system for predicting Cardio Vascular Disease using Deep Neural Network with Binarized Butterfly Optimization Algorithm (DNN–BBoA) is proposed. The BBoA is incorporated to select the best features. The optimal features are fed to the deep neural network classifier and it improves prediction accuracy and reduces the time complexity. The usage of a deep neural network further helps to improve the prediction accuracy with minimal complexity. The proposed system is tested with two datasets namely the Heart disease dataset from UCI repository and CVD dataset from Kaggle Repository. The proposed work is compared with different machine learning classifiers such as Support Vector Machine, Random Forest, and Decision Tree Classifier. The accuracy of the proposed DNN–BBoA is 99.35% for the heart disease data set from UCI repository yielding an accuracy of 80.98% for Kaggle repository for cardiovascular disease dataset.

Knowledge based topic retrieval for recommendations and tourism promotions

The Users regularly share their views on services through internet reviews. Digital tourism allows travelers to plan and manage their trips ahead of time using the internet. The proposed system will enable users to create recommender systems that can help to find new places as per individual interests. The portals such as Tripadvisor and Yelp assist travelers in decision-making and providing feedback. The data available in the form of reviews, blogs, and rating help the researcher build a better recommendation and promotion system. The recommendation system will help tourists discover restaurants often ignored due to a lack of promotion. This research proposes an automated system to perform three subtasks: predicting star ratings from reviews, a feedback model, and a knowledge-based recommender system. The experimental work results show that Random Forest and Decision Tree classifiers give the highest accuracy for predicting star ratings. The research utilizes clustering and topic modeling to identify the topic from the reviews. Moreover, this research uses salience and valence of the topic to generate the feedback model. Furthermore, a recommender system is developed using user preferences, restaurant knowledge, and the valence of each topic of each restaurant. Inverse Document Frequency and K-means clustering have been used to improve the recommendation quality and construct a CSR matrix. This system will link restaurants with similar reviews to ensure that customers find restaurants almost identical to the ones they enjoyed in the past.

MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses.

Viral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range of viruses and hosts, however, these predictors produce better performance only for specific types of hosts and viruses. The prime objective of this research is to develop a robust meta predictor (MP-VHPPI) capable of more accurately predicting VHPPI across multiple hosts and viruses. The proposed meta predictor makes use of two well-known encoding methods Amphiphilic Pseudo-Amino Acid Composition (APAAC) and Quasi-sequence (QS) Order that capture amino acids sequence order and distributional information to most effectively generate the numerical representation of complete viral-host raw protein sequences. Feature agglomeration method is utilized to transform the original feature space into a more informative feature space. Random forest (RF) and Extra tree (ET) classifiers are trained on optimized feature space of both APAAC and QS order separate encoders and by combining both encodings. Further predictions of both classifiers are utilized to feed the Support Vector Machine (SVM) classifier that makes final predictions. The proposed meta predictor is evaluated over 7 different benchmark datasets, where it outperforms existing VHPPI predictors with an average performance of 3.07, 6.07, 2.95, and 2.85% in terms of accuracy, Mathews correlation coefficient, precision, and sensitivity, respectively. To facilitate the scientific community, the MP-VHPPI web server is available at https://sds_genetic_analysis.opendfki.de/MP-VHPPI/.