AdaBoost Machine Learning Algorithm Research Articles

Construction and validation of machine learning algorithm for predicting depression among home-quarantined individuals during the large-scale COVID-19 outbreak: based on Adaboost model

ObjectivesCOVID-19 epidemics often lead to elevated levels of depression. To accurately identify and predict depression levels in home-quarantined individuals during a COVID-19 epidemic, this study constructed a depression prediction model based on multiple machine learning algorithms and validated its effectiveness.MethodsA cross-sectional method was used to examine the depression status of individuals quarantined at home during the epidemic via the network. Characteristics included variables on sociodemographics, COVID-19 and its prevention and control measures, impact on life, work, health and economy after the city was sealed off, and PHQ-9 scale scores. The home-quarantined subjects were randomly divided into training set and validation set according to the ratio of 7:3, and the performance of different machine learning models were compared by 10-fold cross-validation, and the model algorithm with the best performance was selected from 15 models to construct and validate the depression prediction model for home-quarantined subjects. The validity of different models was compared based on accuracy, precision, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC), and the best model suitable for the data framework of this study was identified.ResultsThe prevalence of depression among home-quarantined individuals during the epidemic was 31.66% (202/638), and the constructed Adaboost depression prediction model had an ACC of 0.7917, an accuracy of 0.7180, and an AUC of 0.7803, which was better than the other 15 models on the combination of various performance measures. In the validation sets, the AUC was greater than 0.83.ConclusionsThe Adaboost machine learning algorithm developed in this study can be used to construct a depression prediction model for home-quarantined individuals that has better machine learning performance, as well as high effectiveness, robustness, and generalizability.

Editorial:Blockchain‐based 6Gand industrial internet of things systems for industry 4.0/5.0

The industrial internet of things (IIoT) and Industry 4.0/5.0 enable the integration of machinery, equipment, processes, and humans across a variety of vertical sectors, including manufacturing and logistics supply chains, transportation, and medical care (Yadav et al., 2022). Several types of sensor nodes link these interconnected machines/appliances, sensing and transmitting data to the nodes or the cloud. To manage those links, next-generation networking technologies, such as 6G and Cybertwin, are being introduced. Sixth-generation (6G) communication will be critical in providing complex wireless interconnections, with 6G networks expected to be capable of supporting millions of linked devices and systems while maintaining high data rates and low latency (Kanwal et al., 2022). Blockchain is one emerging technology that can contribute to IIoT stability. Blockchain looks to offer a solution to maintain user privacy while still preserving the capacity for immutable information and replication. A blockchain technology is used to enforce a free distributed ledger to record transactions by peer-to-peer network nodes (P2Ps) and to avoid the need for a central authority through a distributed consensus process. This special issue focuses on six high-quality studies that address real-world issues in IIoT. Sharma et al. (2021) suggested a blockchain-based IoT architecture that uses the identity-based encryption (IBE) algorithm to improve the security of healthcare data. In this case, the smart contract outlines all of the fundamental processes of the healthcare system, which can benefit all stakeholders. Many tests are carried out in order to assess the efficacy of the suggested strategy. The findings reveal that the suggested system outperforms the current well-known strategies. Al-Haija et al. (2022) built a strong classifier for identifying and categorizing various cyberattacks in IoT networks using the AdaBoost machine learning algorithm paired with decision trees and substantial data engineering techniques. We test our system using the TON IoT 2020 datasets, which are a collection of datasets designed particularly for three-layered IoT systems that include physical, network, and application layers. We compare our system's performance to that of existing cutting-edge technologies. Our experimental results show that our framework is capable of offering improved classification accuracy and reduces kinds 1 and 2 mistakes for building more durable IoT infrastructures. Babu et al. (2022) introduced a unique IoT device authentication technique based on IBE and a blockchain network. Blockchain is utilized as a distributed PKG, removing the single point of failure and key escrow issue associated with PKGs. Furthermore, the suggested work is implemented on Hyperledger Fabric, an open-source blockchain platform that effectively handles the adding, updating, and deletion operations required for successful IoT device authentication and communication. Mehbodniya et al. (2022) created a framework for signature creation and verification using a modified Lamport Merkle Digital Signature technique. It employs a central healthcare controller (CHC) to determine the origin of the created signature as well as verification and authentication. To validate the signature, the validation hash public key with create key is necessary. When compared with conventional approaches, this resulted in more efficient, cost-effective, and speedier security. Vigneysh et al. (2022) provided a successful technique for increasing dynamic responsiveness during system uncertainties such as voltage distortions, frequency changes, renewable energy source variations, and the presence of non-linear and unbalanced loads. It also increases the quality of current fed into the grid during uncertainty. The suggested control approach is used to manage both the dc side capacitor voltage and the current loop of a grid-connected inverter. The suggested system is simulated in the MATLAB Simulink environment, and its performance is compared with that of standard controllers to demonstrate the efficacy of the proposed control technique during system anomalies. Patil et al. (2022) developed a two-phased technique for blockchain and IoT federated networks that use a multi-criteria-based approach to connection selection. The dynamic gateway scheduling technique is capable of supporting both blockchain-based transactions and IoT device connectivity. Furthermore, the suggested technique improves the fairness of data transfer for each gateway, resulting in efficient data transmission. Before using the link selection process, machine learning (ML) approaches are used to examine the state of the communication channels. The links are then selected using multi-criteria statistical approaches. Finally, scheduling is carried out in order to identify the best gateway for quickly channelling blockchain data.

Novel Vehicle Detection in Real Time Road Traffic Density Using Haar Cascade Comparing with KNN Algorithm based on Accuracy and Time Mean Speed

Aim: The main objective of the paper is to detect objects in iconic real time traffic density videos from CCTVs and Cameras using Haar Cascade algorithm and to compare algorithms with K-Nearest Neighbour algorithm (KNN). In this case we tried improving the rate of accuracy in predicting the traffic density. Materials and methods: Haar Cascade algorithm is applied on 5 realistic videos and which consists of more than 250 frames. For the same we evaluated the Accuracy and Precision values. Harr-like function displays the vehicle’s visual structure, and the AdaBoost machine learning algorithm was used to create a classifier by combining individual classifiers. The significance value achieved for finding the accuracy and precision was 0.445 and 0.754 respectively. Results and Discussions: Detection of vehicles in high speed videos is performed by using Haar Cascade which has mean accuracy with 85.22% and mean precision with 90.63% and 60% of mean accuracy and 58.53% mean precision in KNN classifiers. Conclusion: The performance of the Haar Cascade appears to be better than KNN in terms of both Accuracy and Precision.

Fault Diagnosis and Asset Management of Power Transformer Using Adaptive Boost Machine Learning Algorithm

Dissolved Gas Analysis (DGA) data of liquid insulation used to find the incipient faults such as partial discharge, thermal faults of various temperatures, discharge of high and low energy faults, combination of electrical and thermal faults in transformers. The conventional approaches of DGA namely Gas Ratio method, Duval triangle method and the Neural Network seems to be time consuming and sometimes yield erroneous results. In this paper, Adaptive BOOST machine learning algorithm is proposed, which is effective in classifying the transformer incipient faults. The results of proposed algorithm is compared with the results of different other machine learning algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, Ensembler algorithm for the same set of transformers data. From the comparison, it is evident that ADABOOST machine learning algorithm performs well.

Intelligent Identification of Reservoir Fluid in Daniudi Gas Field Based on AdaBoost Machine Learning Algorithm

Intelligent Identification of Reservoir Fluid in Daniudi Gas Field Based on AdaBoost Machine Learning Algorithm

Vehicle Classification using Haar Cascade Classifier Method in Traffic Surveillance System

Object detection based on digital image processing on vehicles is very important for establishing monitoring system or as alternative method to collect statistic data to make efficient traffic engineering decision. A vehicle counter program based on traffic video feed for specific type of vehicle using Haar Cascade Classifier was made as the output of this research. Firstly, Haar-like feature was used to present visual shape of vehicle, and AdaBoost machine learning algorithm was also employed to make a strong classifier by combining specific classifier into a cascade filter to quickly remove background regions of an image. At the testing section, the output was tested over 8 realistic video data and achieved high accuracy. The result was set 1 as the biggest value for recall and precision, 0.986 as the average value for recall and 0.978 as the average value for precision.

Automated detection of new impact sites on Martian surface from HiRISE images

In this study, an automated method for Martian new impact site detection from single images is presented. It first extracts dark areas in full high resolution image, then detects new impact craters within dark areas using a cascade classifier which combines local binary pattern features and Haar-like features trained by an AdaBoost machine learning algorithm. Experimental results using 100 HiRISE images show that the overall detection rate of proposed method is 84.5%, with a true positive rate of 86.9%. The detection rate and true positive rate in the flat regions are 93.0% and 91.5%, respectively.

Automatic detection of Martian dark slope streaks by machine learning using HiRISE images

Dark slope streaks (DSSs) on the Martian surface are one of the active geologic features that can be observed on Mars nowadays. The detection of DSS is a prerequisite for studying its appearance, morphology, and distribution to reveal its underlying geological mechanisms. In addition, increasingly massive amounts of Mars high resolution data are now available. Hence, an automatic detection method for locating DSSs is highly desirable. In this research, we present an automatic DSS detection method by combining interest region extraction and machine learning techniques. The interest region extraction combines gradient and regional grayscale information. Moreover, a novel recognition strategy is proposed that takes the normalized minimum bounding rectangles (MBRs) of the extracted regions to calculate the Local Binary Pattern (LBP) feature and train a DSS classifier using the Adaboost machine learning algorithm. Comparative experiments using five different feature descriptors and three different machine learning algorithms show the superiority of the proposed method. Experimental results utilizing 888 extracted region samples from 28 HiRISE images show that the overall detection accuracy of our proposed method is 92.4%, with a true positive rate of 79.1% and false positive rate of 3.7%, which in particular indicates great performance of the method at eliminating non-DSS regions.