AdaBoost Support Vector Machine Research Articles

Optimized Adaboost Support Vector Machine-Based Encryption for Securing IoT-Cloud Healthcare Data

The Internet of Things (IoT) connects various medical devices that enable remote monitoring, which can improve patient outcomes and help healthcare providers deliver precise diagnoses and better service to patients. However, IoT-based healthcare management systems face significant challenges in data security, such as maintaining a triad of confidentiality, integrity, and availability (CIA) and securing data transmission. This paper proposes a novel AdaBoost support vector machine (ASVM) based on the grey wolf optimization and international data encryption algorithm (ASVM-based GWO-IDEA) to secure medical data in an IoT-enabled healthcare system. The primary objective of this work was to prevent possible cyberattacks, unauthorized access, and tampering with the security of such healthcare systems. The proposed scheme encodes the healthcare data before transmitting them, protecting them from unauthorized access and other network vulnerabilities. The scheme was implemented in Python, and its efficiency was evaluated using a Kaggle-based public healthcare dataset. The performance of the model/scheme was evaluated with existing strategies in the context of effective security parameters, such as the confidentiality rate and throughput. When using the suggested methodology, the data transmission process was improved and achieved a high throughput of 97.86%, an improved resource utilization degree of 98.45%, and a high efficiency of 93.45% during data transmission.

Handling imbalanced data in intrusion detection using time weighted Adaboost support vector machine classifier and crossover boosted Dwarf Mongoose Optimization algorithm

Cybersecurity threats pose a serious challenge in the present day and age, and Intrusion Detection Systems (IDS) have emerged as an effective solution to counter these threats. In this paper, a novel IDS is proposed that captures data from the NSL-KDD dataset and are preprocessed. The Kernel Principal Component Analysis (KPCA) model extracts features presented in the data, and the Crossover Boosted Dwarf Mongoose Optimization (CDMO) algorithm selects the relevant features for classification. The CDMO algorithm offers the advantages of improving exploitation, providing optimal solutions, and balancing global exploitation and local search capabilities. The selected features are classified into five classes using the Time Weighted Adaboost Support Vector Machine (TWASVM) classifier. The TWASVM classifier effectively handles imbalanced data and delivers high-performance results. Experiments conducted on MATLAB R2019a and the proposed model achieved an higher accuracy of 98.6 % and less time complexity of 13 seconds. Comparative analysis demonstrated that the proposed IDS outperforms other state-of-the-art methods. The advantages of CDMO algorithm include improved exploitation, optimal solutions, and a balanced crossover strategy for global exploitation and local search capabilities. The advantages of the TWASVM classifier include the ability to handle imbalanced data and deliver high-performance results. Overall, the proposed IDS offer a novel solution to the challenges of intrusion detection in a rapidly evolving cybersecurity landscape.

Evaluation System for the Talent Training Quality of Higher Education Based on the Combination of the Subjective and Objective Evaluation Method and AdaBoost-SVM

This paper mainly researches for the evaluation index system of college students’ cultivation quality. First, we establish a comprehensive evaluation index system of college students’ cultivation quality from three aspects of college students’ knowledge, ability, and quality, to establish the evaluation index system which consists of 3 first-level indexes, 14 second-level indexes, and 40 index observation points. Then, the method of assigning the index weight is optimized and the method of combining subjective and objective weight is adopted. Finally, this paper introduces the algorithm of machine learning as an evaluation method. In order to improve the performance of the algorithm, diversity measure is introduced to ensure that the base learner has good diversity. We proposed a new algorithm AdaBoost-support vector machine (AdaBoost-SVM); it is found that the proposed method gets a high accuracy rate of 91% with a small amount of data in our education datasets. The research results of this paper solve the problems of incomplete index creation, unreasonable index weight assignment, and low efficiency of evaluation methods in the current evaluation of college student’s cultivation quality.

Detection of black-hole attacks in MANET using adaboost support vector machine

Mobile Ad hock Networks (MANETs) are currently used for developing the privacy and accuracy of modern networks. Furthermore, MANET applications are fit to be data-oriented systems, that introduce a secure and more robust data transmission protocol making it a topmost priority in the design. The lack of infrastructure in the existence of dynamic topology as well as limited resources of MANET is a major challenge facing those interested in the field. Further, the nonexistence of a formerly authorized trust relationship within the connected nodes produces instability of the detection process in MANETs. Basically, by adding adapted LEACH routing protocol to MANET, enhancement of the preserved nodes vitality will be achieved, moreover, the load balancing with data loss reduction provides MANET ability to tracks along with shortest and limited paths. This paper proposes a newly developed detection scheme for both active and passive black-hole attacks in MANETs. Moreover, the scheme deals with assessing a group of selected features for each node-based AdaBoost-SVM algorithm. These features are collected from cluster members nodes based on Ad hoc On-demand Multipath Distance Vector (OMDV) with LEACH routing protocol clustering approaches. Although SVM is considered a more stable classifier, there are great influences of the AdaBoost weight adaption algorithm to enhance the classification process in terms of strengthening the weights of extracted features. This hybrid algorithm is essential for active black-hole attacks as well as for identifying passive black-hole attacks in MANET. The proposed scheme is tested against the effect of mobility variation to determine the accuracy of the detection process including the routing overhead protocol. The experimental results investigated that the accuracy of detecting both active and passive black-holes attacks in MANET reached 97% with a promising time complexity for different mobility conditions. Moreover, the proposed scheme provides an accurate decision about malicious vs benign node dropping behavior using an adjustable threshold value.

Review on Smile Detection

Facial expressions are a result of specific movement of face muscles, and these face expressions are considered as a visible sign of a person’s internal thought process, intensions, and internal emotional states. Smile is such a face expression which often indicates, satisfaction, agreement, happiness, etc. Though, a lot of studies have been done over detection of Facial Expression in last decade, smile detection had attracted researcher for more deeper studies. In this review paper, different type of available smile detection so far has been discussed such as Deep Convolutional Neural Network (CNN), Hidden Marcov Model(HMM), K-Nearest Neighbours(KNN), Self Similarity of Gradient(GSS), Histogram of Oriented Gradients (HOG), Gabor-Energy Filters and Local Binary Pattern(LBP) etc and classifier like HAAR Classifier, Hidden Markov Model(HMM), Adaboost Support Vector Machine (SVM),Softmax Classifier and Extreme Learning Machine(ELM).This review paper will prove beneficial for learning about smile detection and its application.

Leak monitoring and localization in baghouse filtration system using a distributed optical fiber dynamic air pressure sensor

• Distributed optical fiber dynamic air pressure sensor is proposed to identify and locate broken bags in baghouse filtration systems in cement industry. • Detection of broken bags with hole diameters of 0.3 cm is achieved using ADA-SVM algorithm without the interference of electromagnetic field. • The technique can be potentially used for dust emission control in environment protection field. Coherent Rayleigh backscattering noise produced by the detection of the dynamic airflow applied to a standard optical fiber is proposed to monitor and locate the potential broken fabric bags within a baghouse filtration system with a sensing distance of 2 km. The optical fiber cable hangs freely inside the fabric bags with small leak holes. Experimental results demonstrated that the proposed distributed optical air pressure sensor is capable of identifying broken bags via the measurement of airflow perturbation applied onto the optical fiber cable. Field test at a baghouse filtration test platform was conducted and results showed that the location accuracy of the system is less than ±5 m with a detectable hole diameter of 0.3 cm and a recognition rate of ~87.6% measured by Adaboost-Support Vector Machine (ADA-SVM) method. A false alarm rate of 9% for this particular leak diameter is achieved.

Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting

This paper focuses on how to effectively construct dynamic financial distress prediction models based on class-imbalanced data streams. Two class-imbalanced dynamic financial distress prediction approaches are proposed based on the synthetic minority oversampling technique (SMOTE) combined with the Adaboost support vector machine ensemble integrated with time weighting (ADASVM-TW). One is the simple integration model of SMOTE with ADASVM-TW, which uses SMOTE to make each data batch class-balanced before ADASVM-TW is applied for dynamic financial distress prediction modeling. The other is the embedding integration model of SMOTE with ADASVM-TW, which embeds SMOTE into the iteration of ADASVM-TW and creatively designs a new sample weighting mechanism. Namely, in each round of iteration, it does not only resample the majority financial normal samples by time weighting, but also generates more synthetic minority samples around new and difficult minority samples and less synthetic minority samples around old and easy minority samples to make the training dataset class-balanced. The empirical experiments are carried out based on the financial data of totally 2628 Chinese listed companies. Since there is certain degree of randomness in the proposed models, 50 times of experiments were performed under the same computational environment, so that the experimental results of model performance can be statistically compared and tested. The results indicate that both the simple integration model and the embedding integration model can greatly improve the recognition ability for the minority financial distress samples, and the embedding integration model is even more preferred because it also significantly outperforms the simple integration model.

Classification of Osteoarthritis Disease Severity Using Adaboost Support Vector Machines

Osteoarthritis (OA) is a condition when the joint is painful due to mild inflammation that arises due to friction of the ends of the joint bone. OA is the most chronic disease and joint disability in elderly people. One way to prevent this disease is to do early detection using machine learning for classification. In this study, it was used Adaptive Boosting (AdaBoost) and Support Vector Machines (SVM) together as classifiers. The purpose of this study was to see whether AdaBoost SVM could produce good accuracy with SVM as comparison. Tests were conducted using 10% until 90% data training. Polynomial and RBF kernel were used with number of AdaBoost cycle. The highest accuracy value of SVM was 75% in 90% training data, while the highest accuracy value of AdaBoost SVM was 85,714% in 80% training data. Therefore, it could be that AdaBoost can improve the performance of SVM in classification of OA disease severity.

Feature Selection using FFS and PCA in Biomedical Data Classification with AdaBoost-SVM

: Recently, there has been an increasing trend to propose computer aided diagnosis systems for biomedical pattern recognition. A computer aided diagnosis method, which aims higher classification accuracy, is developed to classify the biomedical dataset. This new method includes two types of machine learning algorithms: feature selection and classification. In this method, firstly, features were extracted from biomedical dataset, then the extracted features were classified by hybrid AdaBoost-Support Vector Machines (SVM) classifier structure. For feature selection, Forward Feature Selection (FFS) and Principal Component Analysis (PCA) algorithms were used. Following it, advantages and disadvantages of these algorithms were evaluated. The proposed two different hybrid structures and other studies in literature were compared with our findings. Wisconsin Breast Cancer (WBC), Pima Diabetes (PD), Heart (Statlog) biomedical datasets and Electrocardiogram (ECG) signals were taken from UCI database and these datasets were used to test the proposed hybrid structure. The obtained results show that the proposed hybrid structure has high classification accuracy for biomedical data classification.

Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble

Dynamic financial distress prediction (DFDP) is important for improving corporate financial risk management. However, earlier studies ignore the time weight of samples when constructing ensemble FDP models. This study proposes two new DFDP approaches based on time weighting and Adaboost support vector machine (SVM) ensemble. One is the double expert voting ensemble based on Adaboost-SVM and Timeboost-SVM (DEVE-AT), which externally combines the outputs of an error-based decision expert and a time-based decision expert. The other is Adaboost SVM internally integrated with time weighting (ADASVM-TW), which uses a novel error-time-based sample weight updating function in the Adaboost iteration. These two approaches consider time weighting of samples in constructing Adaboost-based SVM ensemble, and they are more suitable for DFDP in case of financial distress concept drift. Empirical experiment is carried out with sample data of 932 Chinese listed companies’ 7 financial ratios, and time moving process is simulated by dividing the sample data into 13 batches with one year as time step. Experimental results show that both DEVE-AT and ADASVM-TW have significantly better DFDP performance than single SVM, batch-based ensemble with local weighted scheme, Adaboost-SVM and Timeboost-SVM, and they are more suitable for disposing concept drift of financial distress.

Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms.

Dermoscopy is a technique used to capture the images of skin, and these images are useful to analyze the different types of skin diseases. Malignant melanoma is a kind of skin cancer whose severity even leads to death. Earlier detection of melanoma prevents death and the clinicians can treat the patients to increase the chances of survival. Only few machine learning algorithms are developed to detect the melanoma using its features. This paper proposes a Computer Aided Diagnosis (CAD) system which equips efficient algorithms to classify and predict the melanoma. Enhancement of the images are done using Contrast Limited Adaptive Histogram Equalization technique (CLAHE) and median filter. A new segmentation algorithm called Normalized Otsu's Segmentation (NOS) is implemented to segment the affected skin lesion from the normal skin, which overcomes the problem of variable illumination. Fifteen features are derived and extracted from the segmented images are fed into the proposed classification techniques like Deep Learning based Neural Networks and Hybrid Adaboost-Support Vector Machine (SVM) algorithms. The proposed system is tested and validated with nearly 992 images (malignant & benign lesions) and it provides a high classification accuracy of 93 %. The proposed CAD system can assist the dermatologists to confirm the decision of the diagnosis and to avoid excisional biopsies.

Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose.

The paper proposes a novel approach toward EEG-driven position control of a robot arm by utilizing motor imagery, P300 and error-related potentials (ErRP) to align the robot arm with desired target position. In the proposed scheme, the users generate motor imagery signals to control the motion of the robot arm. The P300 waveforms are detected when the user intends to stop the motion of the robot on reaching the goal position. The error potentials are employed as feedback response by the user. On detection of error the control system performs the necessary corrections on the robot arm. Here, an AdaBoost-Support Vector Machine (SVM) classifier is used to decode the 4-class motor imagery and an SVM is used to decode the presence of P300 and ErRP waveforms. The average steady-state error, peak overshoot and settling time obtained for our proposed approach is 0.045, 2.8% and 44 s, respectively, and the average rate of reaching the target is 95%. The results obtained for the proposed control scheme make it suitable for designs of prosthetics in rehabilitative applications.

Ensemble application of SVM and Boosting in content-based image retrieval

An AdaBoostSVM(AdaBoost Support Vector Machine) algorithm applied to content-based image retrieval was proposed.It uses Support Vector Machine(SVM) as component classifier of the AdaBoost algorithm,and simulates the basic sample re-weighting method of AdaBoost algorithm by adding important samples based on relevance feedback mechanism.The experimental results show that the AdaBoostSVM algorithm can improve the performance of retrieval system in the database of 2000 images effectively.

Infinite ensemble of support vector machines for prediction of musculoskeletal disorders risk

Several modeling techniques have been used to classify the low-back disorders (LBDs) risk associated with the industrial jobs. Many researchers have demonstrated the use of artificial neural networks (ANNs) to predict musculoskeletal disorders risk associated with occupational exposures. In order to improve the accuracy of LBDs risk classification, this paper proposes to use the support vector machines (SVMs), a machine learning algorithm used extensively in the last decade. The results of SVMs based ensemble classification approach to classify the low-back disorders (LBDs) risk associated with the industrial jobs are presented. Four different kernels (i.e. the stump kernel, perceptron kernel, Laplacian kernel and exponential kernel) were used to create infinite ensemble of SVMs and their performance have been compared with the SVMs, AdaBoost SVMs, and a backpropagation neural network. The results suggest an increased performance by stump and Laplacian kernel in comparison to the radial basis function and polynomial kernel based SVMs, AdaBoost SVMs, and the back propagation neural network. Highest classification accuracy of 77.01% was achieved by Laplacian kernel based SVMs in comparison to 71.3% and 74.7% by radial basis function kernel based SVMs and back propagation neural network respectively.