Classification of team sport activities using a single wearable tracking device

To analyze the effect of macular choroidal thickness (MCT) and peripapillary choroidal thickness (PPCT) on the classification of obese and healthy children by comparing the performance of the random forest (RF), support vector machine (SVM), and multilayer perceptrons (MLP) algorithms. Fifty-nine obese children and 35 healthy children aged 6 to 15 years were studied in this prospective comparative study using optical coherence tomography. MCT and PPCT were measured at distances of 500 μm, 1,000 μm, and 1,500 μm from the fovea and optic disc. Three different feature selection algorithms were used to determine the most prominent features of all extracted features. The classification efficiency of the extracted features was analyzed using the RF, SVM, and MLP algorithms, demonstrating their efficacy for distinguishing obese from healthy children. The precision and reliability of measurements were assessed using kappa analysis. The correlation feature selection algorithm produced the most successful classification results among the different feature selection methods. The most prominent features for distinguishing the obese and healthy groups from each other were PPCT temporal 500 μm, PPCT temporal 1,500 μm, PPCT nasal 1,500 μm, PPCT inferior 1,500 μm, and subfoveal MCT. The classification rates for the RF, SVM, and MLP algorithms were 98.6%, 96.8%, and 89%, respectively. Obesity has an effect on the choroidal thicknesses of children, particularly in the subfoveal region and the outer semi-circle at 1,500 μm from the optic disc head. Both the RF and SVM algorithms are effective and accurate at classifying obese and healthy children.

Human motion monitoring is important in applications of movies, animation, sport training, physical rehabilitation, and human-robot interaction. There is a demand for a simple and easy-to-use method to recognize motions of limbs and trunk. In this paper, we developed a wearable velocity tracking device using two orthogonally-placed micro flow sensors to implement three-dimensional motion velocity measurement. In addition, we proposed a functional link artificial neural network (FLANN) model to extract trunk velocity and relative limb velocity from an absolute limb motion detected by the wearable tracking device according to their different dynamic features. Experiments were conducted to validate the effectiveness of the velocity tracking methodology. Results showed the proposed method with wearable device enables real-time measurements of motion velocities of limb and trunk, which were free of accumulated error, robust for dynamic walking and running, and simple to use.

Classification is data mining techniques which used for the purposes of diagnosis in the medical field as measured by the high accuracy produced. The accuracy of classification algorithm is influenced by the use of features and dimensions in dataset. In this study, Chronic Kidney Disease (CKD) dataset was used where the data is one of the high dimension datasets. Support Vector Machine (SVM) algorithm is used because its ability to handle high-dimensional data. In the dataset, it consists of 24 attributes and 1 class which if all are used results accuracy of classification will be diminished. Method for selecting features with Particle Swarm Optimization (PSO) is applied to reduce redundant features and produce optimal features. In addition, ensemble AdaBoost also applied in this research to increase performance of entirety classification algorithm. The results showed that the optimization of SVM algorithm by using PSO as a selection and ensemble feature of AdaBoost with an average of selected features of 18 features could increase the accuracy of 36.20% to 99.50% in the diagnosis of CKD compared to the SVM algorithm without optimization only resulting in accuracy 63.30%. This research can be used as a reference for further research in focusing on the preprocessing stage.

Multi class SVM algorithm with active learning for network traffic classification

This research aims to categorize survey data to determine the level of satisfaction with the services provided by the village government as a public service provider. Villages or sub-districts currently offer services in response to community demand, although only partially or as efficiently as possible. The data collection technique used was distributing questionnaires to the village community. The method used for classification is the machine learning method. Before the classification process, feature selection is carried out at the data pre-processing stage using Particle Swarm Optimization (PSO), which has been proven to increase the accuracy of the classification values. The classification methods employed include Decision Tree (DT), Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms for classification purposes. This study achieves the maximum level of accuracy in decision tree classification, attaining an accuracy rate of 97.74%. Subsequently, the KNN algorithm achieved an accuracy of 77.90%, the Nave Bayes algorithm achieved 64.4%, and the SVM algorithm, which yielded the lowest accuracy value, achieved 59.90%. Following the application of Particle Swarm Optimization (PSO) for optimization, the accuracy of the SVM and KNN algorithms improved to 98.3%. The Decision Tree algorithm achieved a value of 97.77%, while the Naive Bayes technique yielded a value of 69.30%.

Detection of visual faults in photovoltaic modules using a stacking ensemble approach

Frothy Disturbance Intrusion Detection Systems (FIDS) can help detect and prevent security attacks using the Support Vector Machine (SVM) algorithm. Recognizing the importance of FIDS in protecting various domains linked to the internet, focus lies on adapting traditional intrusion detection methods for the landscape, which faces challenges such as resource constraints and limited memory and battery capacity. This study entails the creation of a lightweight attack detection technique that utilizes a supervised machine learning-based FIDS using the SVM algorithm. Simulations are used to demonstrate the usefulness of the proposed SVM-based FIDS classifier, which employs a combination of two or three complex features and achieves satisfactory classification accuracy and detection time. This strategy has the ability to enhance application security by effectively addressing the particular.

Detecting hate speech in social media is challenging due to its rarity, high-dimensional complexity, and implicit expression via sarcasm or spelling variations, rendering linear models ineffective. In this study, the SVM (Support Vector Machine) algorithm is used to map text features from low-dimensional to high-dimensional space using kernel function techniques to meet complex nonlinear classification challenges. By maximizing the category interval to locate the optimal hyperplane and combining nuclear techniques to implicitly adjust the data distribution, the classification accuracy of hate speech detection is significantly improved. Data collection leverages social media APIs (Application Programming Interface) and customized crawlers with OAuth2.0 authentication and keyword filtering, ensuring relevance. Regular expressions validate data integrity, followed by preprocessing steps such as denoising, stop-word removal, and spelling correction. Word embeddings are generated using Word2Vec’s Skip-gram model, combined with TF-IDF (Term Frequency–Inverse Document Frequency) weighting to capture contextual semantics. A multi-level feature extraction framework integrates sentiment analysis via lexicon-based methods and BERT for advanced sentiment recognition. Experimental evaluations on two datasets demonstrate the SVM model’s effectiveness, achieving accuracies of 90.42% and 92.84%, recall rates of 88.06% and 90.79%, and average inference times of 3.71 ms and 2.96 ms. These results highlight the model’s ability to detect implicit hate speech accurately and efficiently, supporting real-time monitoring. This research contributes to creating a safer online environment by advancing hate speech detection methodologies.

<p>Systems like health care, educational organization, financial institution etc., and produce huge amount of data. For decision making, there is a need of processing a raw data generated by these systems automatically. For example there is need of decision support system in health care to decrease the hospitalization rate. The quality of service provided will be low if the rate of hospitalization is more</p> <p><a href="https://journalnx.com/journal-article/20150697">https://journalnx.com/journal-article/20150697</a></p>

Comparison between five pattern-based approaches for automated diagnostic classification of mature/peripheral B-cell neoplasms based on standardized EuroFlow flow cytometry immunophenotypic data.

The environmental perception and control are the core issues of driverless vehicles. Considering the weak signal of global positioning system (GPS) and the difficulty of road information acceptance in rural area, the DIPLECS autonomous driving datasets is selected. The roads in dataset are classified by support vector machine algorithm (SVM), and established into the road template library. Then, on different roads, images and steering direction are used as the input and labels of the training set for back propagation neural network (BPNN) model. Finally, the effect of the model is verified on the video clip. The results show that the accuracy of road classification reaches 99.8% through SVM, the shortest time consumption of a single frame is 0.9 ms. The highest accuracy of steering prediction is 94.5%, and it only takes 0.224 ms to detect a frame. The algorithm meets the requirements of accuracy and real-time, and it can effectively assist and warn the drivers or automatic driving systems.

Prediction of a heart attack is very important since it is one of the leading causes of sudden death, especially in low-income countries. Although cardiologists use traditional clinical methods such as electrocardiography and blood tests for heart attack prediction, computer aided diagnosis systems that use machine learning methods are also in use for this task. In this study, we used machine learning and feature selection algorithms together. Our aim is to determine the best machine learning method and the best feature selection algorithm to predict heart attacks. For this purpose, many machine learning methods with optimum parameters and several feature selection methods were used and evaluated on the Statlog (Heart) dataset. According to the experimental results, the best machine learning algorithm is the support vector machine algorithm with the linear kernel, while the best feature selection algorithm is the reliefF method. This pair gave the highest accuracy value of 84.81%.

Deep learning has been successfully applied in classification of white blood cells (WBCs), however, accuracy and processing time are found to be less than optimal hindering it from getting its full potential. This is due to imbalanced dataset, intra-class compactness, inter-class separability and overfitting problems. The main research idea is to enhance the classification and prediction accuracy of blood images while lowering processing time through the use of deep convolutional neural network (DCNN) architecture by using the modified loss function. The proposed system consists of a deep neural convolution network (DCNN) that will improve the classification accuracy by using modified loss function along with regularization. Firstly, images are pre-processed and fed through DCNN that contains different layers with different activation function for the feature extraction and classification. Along with modified loss function with regularization, weight function aids in the classification of WBCs by considering weights of samples belonging to each class for compensating the error arising due to imbalanced dataset. The processing time will be counted by each image to check the time enhancement. The classification accuracy and processing time are achieved using the dataset-master. Our proposed solution obtains better classification performance in the given dataset comparing with other previous methods. The proposed system enhanced the classification accuracy of 98.92% from 96.1% and a decrease in processing time from 0.354 to 0.216 s. Less time will be required by our proposed solution for achieving the model convergence with 9 epochs against the current convergence time of 13.5 epochs on average, epoch is the formation white blood cells (WBCs) and the development of granular cells. The proposed solution modified loss function to solve the adverse effect caused due to imbalance dataset by considering weight and use regularization technique for overfitting problem.

This paper presents a motion prediction scheme of object vehicles based on the dynamic occupancy grid map considering movement of the vehicles by applying a temporal flow and a cascaded algorithm for support vector machine (SVM). We divided occupancy grid map into two types of upper-level and lower level. The upper-level occupancy grid is used to predict motion that the object vehicle can move into the ego vehicle and the lower-level one is needed for decision using the SVM for sensor resolution. The presented algorithm was validated with a experimental data set and the overall accuracy of classification was obtained 90.42% from a confusion matrix.

As an effective classifier, Support Vector Machine (SVM) has received more and more attention in remote sensing communities. Spatial data and remote sensing image may have a lot of features, but too many spatial-features usually lead to the reduction of training speed and the decrease in classification accuracy. To solve this problem, our study presents a novel parallelized remote sensing classifier (NRSC) by utilizing rough set theory and SVM algorithm. In our algorithm, feature set is firstly split sub-feature sets, each sub-SVM classifier is then trained paralleled by these sub-feature sets, and sub-SVMs vote their decisions to avoid the instable performance a single classifier. The experimental results show that both the classification accuracy and training speed, compared with the traditional SVM method, are all improved in remote sensing image classification.