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With the rapid development of electric power systems, load estimation plays an important role in system operation and planning. Usually, load estimation techniques contain traditional, time series, regression analysis-based, and machine learning-based estimation. Since the machine learning-based method can lead to better performance, in this paper, a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed. First, an image fingerprint construction is proposed for training data. After the data preprocessing, the training data matrix is constructed by the cyclic shift and cubic spline interpolation. Then, the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint. Second, a convolutional neural network (CNN) combined with an attentionmechanism is proposed for training performance improvement. At last, an experiment is carried out to evaluate the estimation performance. Compared with the support vector machine method, CNN method and long short-term memory method, the proposed algorithm has the best load estimation performance. [ABSTRACT FROM AUTHOR]

The health assessment of airborne lithium batteries is crucial for flight testing, ensuring the safety and reliability of aircraft power systems. This paper proposes a support vector machine-based algorithm for the health assessment of airborne lithium batteries, featuring a dynamic correction mechanism for the risk loss penalty parameter. The proposed approach systematically adjusts risk loss penalty parameters based on sample misjudgment ratios and incorporates fault identification corrections to meet the safety requirements of the airborne operation. The experimental results demonstrate the stability and reliability of the proposed algorithm in hyperplane deviation suppression as well as significant improvements in fault sample recall rates. When compared with traditional SVM and other baseline methods such as Random Forest and SVR, our method significantly outperformed these algorithms in terms of accuracy, recall rate, and precision rate. This study provides an efficient and reliable method for the health assessment of airborne lithium batteries, with significant application value. [ABSTRACT FROM AUTHOR]

This paper focuses on both voltage transients and short-duration RMS variations, and presents a unique and heterogeneous approach to their assessment by applying AI tools. The database consists of both real (obtained from Lithuanian PQ monitoring campaigns) and synthetic data (obtained from the simulation and literature review). Firstly, this paper investigates the fundamental grid component and its harmonics filtering with an IIR shelving filter. Secondly, in a key part, both SVM and KNN are used to classify PQ events by their primary cause in the voltage–duration plane as well as by the type of short circuit in the three-dimensional voltage space. Thirdly, since it seemed to be difficult to interpret the results in the three-dimensional space, the new method, based on Clarke transformation, is developed to convert it to two-dimensional space. The method shows an outstanding performance by avoiding the loss of important information. In addition, a geometric analysis of the fault voltage in both two-dimensional and three-dimensional spaces revealed certain geometric patterns that are undoubtedly important for PQ classification. Finally, based on the results of a PQ monitoring campaign in the Lithuanian distribution grid, this paper presents a unique discussion regarding PQ assessment gaps that need to be solved in anticipation of a great leap forward and refers them to PQ legislation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To improve the accuracy of signal recognition in delay-based optical reservoir computing (RC) systems, this paper proposes the use of nonlinear algorithms at the output layer to replace traditional linear algorithms for training and testing datasets and apply them to the identification of frequency-modulated continuous wave (FMCW) LiDAR signals. This marks the inaugural use of the system for the identification of FMCW LiDAR signals. We elaborate on the fundamental principles of a delay-based optical RC system using an optical-injected distributed feedback laser (DFB) laser and discriminate four FMCW LiDAR signals through this setup. In the output layer, three distinct training algorithms—namely linear regression, support vector machine (SVM), and random forest—were employed to train the optical reservoir. Upon analyzing the experimental results, it was found that regardless of the size of the dataset, the recognition accuracy of the two nonlinear training algorithms was superior to that of the linear regression algorithm. Among the two nonlinear algorithms, the Random Forest algorithm had a higher recognition accuracy than SVM when the sample size was relatively small. [ABSTRACT FROM AUTHOR]

The aim of this paper is to introduce a new two-step inertial method for approximating a solution to a generalized split common fixed point problem, which is a unique solution to a variational inequality problem. We establish a strong convergence theorem for the sequence generated by the algorithm. We explore various special cases related to fundamental problems, including the split feasibility problem, the split common null point problem, and the constrained convex minimization problem. To demonstrate the efficacy and performance of our proposed algorithm, we apply it to a practical scenario involving support vector machines for binary classification. The algorithm is employed on diverse datasets sourced from the UC Irvine Machine Learning Repository, serving as the training set. [ABSTRACT FROM AUTHOR]

Many intrusion detection algorithms that use optimization have been developed and are commonly used to detect intrusions. The process of selecting features and the parameters of the classifier are essential parts of how well an intrusion detection system works. This paper provides a detailed explanation and discussion of an improved intrusion detection method for multiclass classification. The proposed solution uses a combination of the modified teaching–learning-based optimization (MTLBO) algorithm, the modified JAYA (MJAYA) algorithm, and a support vector machine (SVM). MTLBO is used with supervised machine learning (ML) to select subsets of features. Selection of the fewest features possible without impairing the accuracy of the results in feature subset selection (FSS) is a multiobjective optimization issue. This paper presents MTLBO as a mechanism and investigates its algorithm-specific, parameter-free idea. This study used the modified JAYA (MJAYA) algorithm to optimize the C and gamma parameters of the support vector machine (SVM) classifier. When the proposed MTLBO-MJAYA-SVM algorithm was compared with the original TLBO and JAYA algorithms on a well-known intrusion detection dataset, it was found to outperform them significantly. [ABSTRACT FROM AUTHOR]

With the widespread use of unmanned aerial vehicles (UAVs), spectrum resources are becoming less and less available. Better utilization of the spectrum can be achieved by spectrum prediction. In this paper, four algorithms, hidden Markov model (HMM), support vector machine (SVM), multi-layer perceptron (MLP), and long short-term memory neural network (LSTM-NN) algorithms, were introduced for the prediction of channel states in the radio-frequency spectrum. Experiments are conducted on a dataset. It was found from results that when the number of output sequence was fixed as 1 and the number of input channel state information (CSI) sequence was 1-20, the more input sequences were, the lower the error rate of the algorithm was; when the number of input CSI sequences was 20, the error rate of the LSTM algorithm was the lowest among several algorithms, which was 4.82%; when the number of input CSI sequenced was fixed as 1 and the future 1-15 CSI sequences were predicted, the more sequences need to be predicted was, the higher the error rate of the algorithm was; when 15 CSI sequences were predicted, the error rate of the LSTM algorithm was the lowest, 42.33%. The experimental results demonstrate that the LSTM algorithm is reliable in radiofrequency spectrum prediction and can be applied in practical UAV communication. [ABSTRACT FROM AUTHOR]

With the rapid advancement of wearable devices, Human Activities Recognition (HAR) based on these devices has emerged as a prominent research field. The objective of this study is to enhance the recognition performance of HAR by proposing an LSTM-1DCNN recognition algorithm that utilizes a single triaxial accelerometer. This algorithm comprises two branches: one branch consists of a Long and Short-Term Memory Network (LSTM), while the other parallel branch incorporates a one-dimensional Convolutional Neural Network (1DCNN). The parallel architecture of LSTM-1DCNN initially extracts spatial and temporal features from the accelerometer data separately, which are then concatenated and fed into a fully connected neural network for information fusion. In the LSTM-1DCNN architecture, the 1DCNN branch primarily focuses on extracting spatial features during convolution operations, whereas the LSTM branch mainly captures temporal features. Nine sets of accelerometer data from five publicly availableHAR datasets are employed for training and evaluation purposes. The performance of the proposed LSTM-1DCNN model is compared with five other HAR algorithms including Decision Tree, Random Forest, Support Vector Machine, 1DCNN, and LSTM on these five public datasets. Experimental results demonstrate that the F1-score achieved by the proposed LSTM-1DCNN ranges from 90.36% to 99.68%, with a mean value of 96.22% and standard deviation of 0.03 across all evaluated metrics on these five public datasetsoutperforming other existingHAR algorithms significantly in terms of evaluationmetrics used in this study. Finally the proposed LSTM-1DCNN is validated in real-world applications by collecting acceleration data of seven human activities for training and testing purposes. Subsequently, the trained HAR algorithm is deployed on Android phones to evaluate its performance. Experimental results demonstrate that the proposed LSTM-1DCNNalgorithm achieves an impressive F1-score of 97.67% on our self-built dataset. In conclusion, the fusion of temporal and spatial information in the measured data contributes to the excellent HAR performance and robustness exhibited by the proposed 1DCNN-LSTM architecture. [ABSTRACT FROM AUTHOR]

Sleep monitoring has become a prevalent area of research where body position and physiological data, such as heart rate and respiratory rate, are monitored. Numerous critical health problems are associated with poor sleep, such as pressure sore development, sleep disorders, and low sleep quality, which can lead to an increased risk of falls, cardiovascular diseases, and obesity. Current monitoring systems can be costly, laborious, and taxing on hospital resources. This paper reviews the most recent solutions for contactless textile technology in the form of bed sheets or mats to monitor body positions, vital signs, and sleep, both commercially and in the literature. This paper is organized into four categories: body position and movement monitoring, physiological monitoring, sleep monitoring, and commercial products. A detailed performance evaluation was carried out, considering the detection accuracy as well as the sensor types and algorithms used. The areas that need further research and the challenges for each category are discussed in detail. [ABSTRACT FROM AUTHOR]

Collecting and analyzing data from all devices to improve the efficiency of business processes is an important task of Industrial Internet of Things (IIoT). In the age of data explosion, extensive text data generated by the IIoT have given birth to a variety of text representation methods. The task of text representation is to convert the natural language to a form that computer can understand with retaining the original semantics. However, these methods are difficult to effectively extract the semantic features among words and distinguish polysemy in natural language. Combining the advantages of convolutional neural network (CNN) and variational autoencoder (VAE), this paper proposes an intelligent CNN-VAE text representation algorithm as an advanced learning method for social big data within next-generation IIoT, which help users identify the information collected by sensors and perform further processing. This method employs the convolution layer to capture the local features of the context and uses the variational technique to reconstruct feature space to make it conform to the normal distribution. In addition, the improved word2vec model based on topical word embedding (TWE) is utilized to add topical information to word vectors to distinguish polysemy. This paper takes the social big data as an example to illustrate the way of the proposed algorithm applied in the next-generation IIoT and utilizes Cnews dataset to verify the performance of proposed method with four evaluating metrics (i.e., recall, accuracy, precision, and F1-score). Experimental results indicate that the proposed method outperforms word2vec-avg and CNN-AE in K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classifiers and distinguishes polysemy effectively. [ABSTRACT FROM AUTHOR]

Background: Cancer diagnosis based on machine learning has become a popular application direction. Support vector machine (SVM), as a classical machine learning algorithm, has been widely used in cancer diagnosis because of its advantages in high-dimensional and small sample data. However, due to the high-dimensional feature space and high feature redundancy of gene expression data, SVM faces the problem of poor classification effect when dealing with such data. Methods: Based on this, this paper proposes a hybrid feature selection algorithm combining information gain and grouping particle swarm optimization (IG-GPSO). The algorithm firstly calculates the information gain values of the features and ranks them in descending order according to the value. Then, ranked features are grouped according to the information index, so that the features in the group are close, and the features outside the group are sparse. Finally, grouped features are searched using grouping PSO and evaluated according to in-group and out-group. Results: Experimental results show that the average accuracy (ACC) of the SVM on the feature subset selected by the IG-GPSO is 98.50%, which is significantly better than the traditional feature selection algorithm. Compared with KNN, the classification effect of the feature subset selected by the IG-GPSO is still optimal. In addition, the results of multiple comparison tests show that the feature selection effect of the IG-GPSO is significantly better than that of traditional feature selection algorithms. Conclusion: The feature subset selected by IG-GPSO not only has the best classification effect, but also has the least feature scale (FS). More importantly, the IG-GPSO significantly improves the ACC of SVM in cancer diagnostic. [ABSTRACT FROM AUTHOR]

The No Free Lunch Theorem tells us that no algorithm can beat other algorithms on all types of problems. The algorithm selection structure is proposed to select the most suitable algorithm from a set of algorithms for an unknown optimization problem. This paper introduces an innovative algorithm selection approach called the CNN-HT, which is a two-stage algorithm selection framework. In the first stage, a Convolutional Neural Network (CNN) is employed to classify problems. In the second stage, the Hypothesis Testing (HT) technique is used to suggest the best-performing algorithm based on the statistical analysis of the performance metric of algorithms that address various problem categories. The two-stage approach can adapt to different algorithm combinations without the need to retrain the entire model, and modifications can be made in the second stage only, which is an improvement of one-stage approaches. To provide a more general structure for the classification model, we adopt Exploratory Landscape Analysis (ELA) features of the problem as input and utilize feature selection techniques to reduce the redundant ones. In problem classification, the average accuracy of classifying problems using CNN is 96%, which demonstrates the advantages of CNN compared to Random Forest and Support Vector Machines. After feature selection, the accuracy increases to 98.8%, further improving the classification performance while reducing the computational cost. This demonstrates the effectiveness of the first stage of the CNN-HT method, which provides a basis for algorithm selection. In the experiments, CNN-HT shows the advantages of the second stage algorithm as well as good performance with better average rankings in different algorithm combinations compared to the individual algorithms and another algorithm combination approach. [ABSTRACT FROM AUTHOR]

Analyzing the spectral and spatial characteristics of Hyperspectral Imaging (HSI) in a three-dimensional space is a challenging task. Recently, there have been developments in 3D feature extraction methods based on tensor decomposition, which allow for the effective utilization of both global and local information in HSI. These methods also explore the inherent low-rank properties of HSI through tensor decomposition. In this paper, we propose a new approach called variable randomized T-product decomposition (Vrt-SVD), which is a variation of Tensor Singular Spectral Analysis. The goal of this approach is to improve the efficiency of tensor methods for feature extraction and reduce artifacts of image processing. By using a randomized algorithm based on the variable t-SVD, we are able to capture both global and local spatial and spectral information in HSI efficiently, which enables us to explore its low-rank characteristics. To evaluate the effectiveness of the extracted features, we use a Support Vector Machine (SVM) classifier to assess the accuracy of image classification. By conducting numerous numerical experiments, we provide strong evidence to show that the proposed method outperforms several advanced feature extraction techniques. [ABSTRACT FROM AUTHOR]

Spatiotemporally mapping winter wheat is imperative for informing and shaping global food security policies. Traditional mapping methods heavily rely on sufficient and reliable samples obtained through labor-intensive fieldwork and manual sample collection. However, these methods are time-consuming, costly, and lack timely and continuous data collection. To address these challenges and fully leverage remote sensing big data and cloud computing platforms like Google Earth Engine (GEE), this paper developed an algorithm for Auto-Generating Winter Wheat Samples for mapping (AGWWS). The AGWWS utilizes historical samples to determine the optimal migration threshold by measuring Spectral Angle Distance (SAD), Euclidean Distance (ED), and Near-Infrared band Difference Index (NIRDI). This facilitates the auto-generation of winter wheat sample sets for the years 2000, 2005, 2010, 2015, and 2021. Approximately two-thirds of the samples were allocated for training, with the remaining one-third used for validating the mapping method, employing the One-Class Support Vector Machine (OCSVM). The Huang–Huai–Hai (HHH) Plain, a major winter wheat production region, was selected to perform the algorithm and subsequent analysis on. Different combinations of the hyper-parameters, gamma and nu, of the OCSVM based on the Gaussian Radial Basis Function Kernel were tested for each year. Following correlation analysis between the winter wheat area derived from the generated maps and the national statistical dataset at the city level, the map with the highest corresponding R2 was chosen as the AGWWS map for each year (0.77, 0.77, 0.80, 0.86, and 0.87 for 2000, 2005, 2010, 2015, and 2021, respectively). The AGWWS maps ultimately achieved an average Overall Accuracy of 81.65%. The study then explores the Non-Grain Production of Winter Wheat (NGPOWW) by analyzing winter wheat change maps from 2000–2005, 2005–2010, 2005–2010, and 2015–2021 in the HHH Plain. Despite an overall increase in the total planted area of winter wheat, the NGPOWW phenomena has led to concerning winter wheat planting marginalization. Compensatory winter wheat areas are notably situated in mountainous and suburban cultivated lands with low qualities. Consequently, despite the apparent expansion in planted areas, winter wheat production is anticipated to be adversely affected. The findings highlight the necessity for improved cultivated land protection policies monitoring the land quality of the compensation and setting strict quota limits on occupations. [ABSTRACT FROM AUTHOR]

In this paper, we present an innovative inertial navigation system (INS)/celestial navigation system (CNS)/scene-matching navigation (SMN) adaptive integrated navigation algorithm designed to achieve prolonged and highly precise navigation in sea areas. The algorithm establishes the structure of the INS/CNS/SMN integrated navigation system. To ensure the availability of CNS in the Nanhai Sea (South China Sea) area, a cloud and fog model is meticulously constructed. Three distinct types of sea area landmarks are defined, and an automated classification model for sea area landmarks, employing support vector machines (SVM), is developed. Corresponding matching methods and strategies for these landmarks are also delineated. Concurrently, the observable probability of each landmark is computed to generate a probability cloud, representing the usability of sea area landmarks. The proposed INS/CNS/SMN adaptive integrated navigation algorithm is simulated and validated across varied altitudes and trajectories in the sea area. The results show that CNS and SMN can dynamically assist INS in achieving prolonged and highly precise navigation. [ABSTRACT FROM AUTHOR]

In order to protect Cyber-Physical Systems (CPS) against constantly changing cyberattacks, machine learning (ML) algorithms must be integrated. The goal of this survey is to investigate attack-resistant machine learning methods that improve CPS security. The limits of standard techniques are emphasized while discussing notable issues in CPS security. The survey thoroughly explores a range of machine learning methods, such as K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Deep Neural Networks (DNN), that are utilized in CPS for behavior analysis, anomaly identification, and intrusion detection. We discuss the importance of having solid training data and the difficulties in ML model adaptation to the dynamic nature of CPS situations. We examine the trade-offs between responsiveness and precision as well as the effects of false positives and false negatives on attack detection. This papers aims to provide a quick overview of the strengths, limitations, and future prospects of these algorithms, enabling stakeholders to formulate effective strategies for CPS security. [ABSTRACT FROM AUTHOR]

Breast Arterial Calcification (BAC) is a mammographic decision dissimilar to cancer and commonly observed in elderly women. Thus identifying BAC could provide an expense, and be inaccurate. Recently Deep Learning (DL) methods have been introduced for automatic BACdetection and quantification with increased accuracy. Previously, classification with deep learning had reached higher efficiency, but designing the structure of DL proved to be an extremely challenging task due to overfitting models. It also is not able to capture the patterns and irregularities presented in the images. To solve the overfitting problem, an optimal feature set has been formed by Enhanced Wolf Pack Algorithm (EWPA), and their irregularities are identified by Dense-kUNet segmentation. In this paper, Dense-kUNet for segmentation and optimal feature has been introduced for classification (severe, mild, light) that integrates DenseUNet and kU-Net. Longer bound links exist among adjacent modules, allowing relatively rough data to be sent to the following component and assisting the system in finding higher qualities. The major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm (EWPA), and Modified Support Vector Machine (MSVM) based learning for classification. k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image segmentation. Longer bound associations occur among nearby sections, allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller characteristics. The proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized mammography. The proposed method achieved the highest precision, recall, F-measure, and accuracy of 84.4333%, 84.5333%, 84.4833%, and 86.8667%when compared to othermethods on the Digital Database for Screening Mammography (DDSM). [ABSTRACT FROM AUTHOR]

Aiming at the problem that the power grid security hidden danger data investigation is interfered by the power grid attack path, which leads to the poor accuracy and low efficiency of the security hidden danger data investigation, an edge computing-based power grid security hidden danger data investigation algorithm is proposed. Combined with the quantitative regression analysis method, the ambiguity function of the power grid security situation awareness assessment is established, the power grid security situation is analyzed, the characteristics of the power grid security hidden danger data samples are extracted, and the power grid security hidden danger data analysis model is constructed based on the support vector machine. Based on the collaborative work of edge computing center nodes and edge nodes, the distributed data detection of power grid security is realized. The power grid security hidden danger data is reconstructed by the joint sparse model, the power grid security hidden danger data is classified, the power grid security hidden danger data feature set is calculated in the high-dimensional space, and the power grid security hidden danger data is checked through the edge detection. The experimental results show that the method has high accuracy and good throughput for the data investigation of power grid security risks, and can efficiently and accurately realize the data investigation of power grid security risks. [ABSTRACT FROM AUTHOR]

Swarm intelligence optimization algorithm has been proved to perform well in the field of parameter optimization. In order to further improve the performance of intelligent optimization algorithm, this paper proposes an improved and adaptive tunicate swarm algorithm (IMATSA) based on tunicate swarm algorithm (TSA). IMATSA improves TSA in the following four aspects: population diversity, local search convergence speed, jumping out of local optimal position, and balancing global and local search. Firstly, IMATSA adopts Tent map and quadratic interpolation to initialize population and enhance the diversity. Secondly, IMATSA uses Golden-Sine algorithm to accelerate the convergence of local search. Thirdly, in the process of global development, IMATSA adopts Levy flight and the improved Gauss disturbance method to adaptively improves and coordinates the ability of global development and local search. Then, this paper verifies the performance of IMATSA based on 14 benchmark functions experiment, ablation experiment, parameter optimization experiments of Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT), Wilcoxon signed rank test and image multi-threshold segmentation experiment with the performance metrics are convergence speed, convergence value, significance level P-value, Peak Signal-to-Noise Ratio (PSNR) and Standard Deviation (STD). Experimental results show that IMATSA performs better in three kinds of benchmark functions; each component of IMATSA has a positive effect on the performance; IMATSA performs better in parameter optimization experiments of SVM experiment and GBDT; there is significant difference between IMATSA and other algorithms by Wilcoxon signed rank test; in image segmentation, the performance is directly proportional to the number of thresholds, and compared with other algorithms, IMATSA has better comprehensive performance. [ABSTRACT FROM AUTHOR]

Twin support vector regression (TSVR) has been widely applied in regression problems. TSVR seeks a pair of ε -insensitive proximal planes by solving two support vector machine type problems. TSVR assumes that the matrices appearing in the dual formulation are positive definite. However, in real-world scenarios, such an assumption may not be fulfilled and, hence, leads to suboptimal performance. ε -Twin support vector regression (ε -TSVR) improved the TSVR by introducing the regularisation term to avoid the singularity issues. Most of the twin support vector machine models involve the computation of matrix inverses. Also, TSVR implements the empirical risk minimization principle. In this paper, we propose an improved twin support vector regression (ITSVR) for brain age estimation by introducing different Lagrangian functions for the primal problems of the TSVR. The proposed ITSVR implements the structural risk minimization principle and avoids the computation of the matrix inverses. To solve the optimization problem more efficiently, we used successive overrelaxation (SOR) technique. We evaluated the proposed ITSVR on cognitively healthy subjects, mild cognitive impairment subjects and Alzheimer's disease subjects. The experimental results demonstrate that the proposed ITSVR has superior performance compared to the baseline models for brain age estimation. [ABSTRACT FROM AUTHOR]

In machine learning, models are derived from labeled training data where labels signify classes and features define sample attributes. However, noise from data collection can impair the algorithm's performance. Blanco, Japón, and Puerto proposed mixed-integer programming (MIP) models within support vector machines (SVM) to handle label noise in training datasets. Nonetheless, it is imperative to underscore that their models demonstrate an observable escalation in the number of variables as sample size increases. The nonparallel support vector machine (NPSVM) is a binary classification method that merges the strengths of both SVM and twin SVM. It accomplishes this by determining two nonparallel hyperplanes by solving two optimization problems. Each hyperplane is strategically positioned to be closer to one of the classes while maximizing its distance from the other class. In this paper, to take advantage of NPSVM's features, NPSVM-based relabeling (RENPSVM) MIP models are developed to deal with the label noises in the dataset. The proposed model adjusts observation labels and seeks optimal solutions while minimizing computational costs by selectively focusing on class-relevant observations within an ε-intensive tube. Instances exhibiting similarities to the other class are excluded from this ε-intensive tube. Experiments on 10 UCI datasets show that the proposed NPSVM-based MIP models outperform their counterparts in accuracy and learning time on the majority of datasets. [ABSTRACT FROM AUTHOR]

The quality of sleep plays a crucial role in physical well-being, and individuals are becoming increasingly concerned about sleep quality and its associated health issues. Although various sleep monitoring devices exist, there remains a need for a highly accurate sleep state identification algorithm. To address this, we present a paper that utilizes machine learning techniques to identify human sleep states based on electroencephalogram (EEG) signals collected by an EEG instrument. We propose a model that incorporates two nonlinear characteristic parameters, MSE and PSE, extracted from artificially designed EEG signals as input. Additionally, we employ a Support Vector Machine (SVM) classifier algorithm to accurately identify sleep states, eliminating uncertainties associated with manually designed feature parameters. Experimental results demonstrate the superior accuracy of our proposed model for sleep state analysis, offering valuable insights for improving sleep quality and addressing related health concerns. [ABSTRACT FROM AUTHOR]

Winding is one of themost important components in power transformers. Ensuring the health state of the winding is of great importance to the stable operation of the power system. To efficiently and accurately diagnose the disc space variation (DSV) fault degree of transformer winding, this paper presents a diagnostic method of winding fault based on the K-Nearest Neighbor (KNN) algorithmand the frequency response analysis (FRA)method. First, a laboratory winding model is used, and DSV faults with four different degrees are achieved by changing disc space of the discs in the winding. Then, a series of FRA tests are conducted to obtain the FRA results and set up the FRA dataset. Second, ten different numerical indices are utilized to obtain features of FRA curves of faulted winding. Third, the 10-fold cross-validation method is employed to determine the optimal k-value of KNN. In addition, to improve the accuracy of the KNN model, a comparative analysis is made between the accuracy of the KNN algorithm and k-value under four distance functions. After getting the most appropriate distance metric and kvalue, the fault classificationmodel based on theKNN and FRA is constructed and it is used to classify the degrees of DSV faults. The identification accuracy rate of the proposed model is up to 98.30%. Finally, the performance of the model is presented by comparing with the support vector machine (SVM), SVM optimized by the particle swarmoptimization (PSO-SVM) method, and randomforest (RF). The results show that the diagnosis accuracy of the proposed model is the highest and the model can be used to accurately diagnose the DSV fault degrees of the winding. [ABSTRACT FROM AUTHOR]

With the continuous improvement in production efficiency and quality of life, the requirements of electrical equipment for power quality are also increasing. Accurate detection of various power quality disturbances is an effective measure to improve power quality. However, in practical applications, the dataset is often contaminated by noise, and when the dataset is not sufficient, the computational complexity is too high. Similarly, in the recognition process of artificial neural networks, the local optimum often occurs, which ultimately leads to low recognition accuracy for the trained model. Therefore, this article proposes a power quality disturbance classification strategy based on the variational mode decomposition (VMD) and improved support vector machine (SVM) algorithms. Firstly, the VMD algorithm is used for preprocessing disturbance denoising. Next, based on the analysis of typical fault characteristics, a multi-SVM model is used for disturbance classification identification. In order to improve the recognition accuracy, the improved Grey Wolf Optimization (IGWO) algorithm is used to optimize the penalty factor and kernel function parameters of the SVM model. The results of the final case study show that the classification accuracy of the proposed method can reach over 98%, and the recognition accuracy is higher than that of the other models. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) as an emerging technology is widely used in various applications such as remote healthcare, smart environment, and intelligent transportation systems. It is necessary to address users' concerns about cost, ease of use, privacy, and comprehensive security to grow the popularity of this technology. Intrusion Detection System (IDS) plays an indispensable role in security and preventing unauthorized users to access authorized network resources through analyzing network patterns. Several techniques such as metaheuristic algorithms, machine learning, fuzzy logic, and artificial intelligence algorithms can be applied to increase the accuracy of IDS, feature selection, and network patterns classification. In this paper, a hybrid intrusion detection system based on Support Vector Machine (SVM) and Grey Wolf Optimization (GWO) is presented that utilizes the advantages of these algorithms. In the proposed approach, the support vector machine has been used to train and differentiate anomaly records from normal records and grey wolf optimization has been used to find the kernel function, feature selection, and adjust optimal parameters for the SVM in order to improve the classification. The conducted simulations prove that the proposed approach outperforms in terms of detection accuracy, precision, recall, and F-score on both NSL-KDD and TON_IoT datasets. [ABSTRACT FROM AUTHOR]

Objective: This paper describes an air compressor fault diagnosis method based on audio signals and applied to one faulty and one healthy condition of rolling bearing considering uni-directional microphone along with a single NI 9234 Data Acquisition (DAQ) hardware unit having multiple ports, a NI 9172 USB interface, and a Lab-VIEW based Data acquisition interface. Methodology: Acquired non-stationary and non-linear bearing fault signals are processed using most recent non-traditional Local Mean Decomposition (LMD) signal processing technique. Further, six statistical indicators (Mean, Variance, RMS, RMA, AMA, and Kurtosis) have been evaluated for feature extraction. Further, four classifying techniques namely: SVM, Naïve Bayes, KNN, and Discriminant have been used for the exploration and classification of the fault features in rolling bearing of an air compressor set-up. Findings: It has been observed that LMD along with Kurtosis and SVM machine learning approach is quite accurate for processing and monitoring in situ rolling bearing fault features in air compressor set-up. [ABSTRACT FROM AUTHOR]

With the deepening application of wearable devices in the field of indoor positioning, the mining and application of human behavior patterns based on new technologies such as mobile terminals, channel technologies and intelligent algorithms have gradually become a research hotspot in the field of intelligent positioning. At this stage, outdoor positioning satellite technology has been gradually improved, but it cannot work effectively in indoor environment, and indoor positioning technology has not formed a set of standard and effective scheme at this stage, which leads to a scene of a hundred contentions for indoor positioning technology. This paper proposes an indoor intelligent localization algorithm based on channel state information, which first forms the measurements of antenna subarrays by the fixed antenna subset arrays located in different paths to achieve the phase estimation based on channel state; then determines the AoA of the direct path from the target to the AP using ToF information and eliminates the STO noise of channel state information by unfolding the best linear fit of CSI phase; again the AoA and ToF estimates from multiple measurements are plotted in two-dimensional space, and the direct path possibility estimation is achieved by identifying the estimates through five clusters of Gaussian-averaged clustering algorithm; finally the coordinates of the target to be located are obtained by coupling direct propagation path, estimation clustering and least-squares estimation using three APs as the receiver and the target to be located as the transmitter. The simulation results show that the intelligent localization algorithm proposed in this paper is able to lack the localization accuracy at the decimeter level in the indoor localization environment with three fixed APs, and the average error is better than the localization algorithms of support vector machine, deep learning and limit learning. [ABSTRACT FROM AUTHOR]

Because of the widespread availability of low-cost printers and scanners, document forgery has become extremely popular. Watermarks or signatures are used to protect important papers such as certificates, passports, and identification cards. Identifying the origins of printed documents is helpful for criminal investigations and also for authenticating digital versions of a document in today's world. Source printer identification (SPI) has become increasingly popular for identifying frauds in printed documents. This paper provides a proposed algorithm for identifying the source printer and categorizing the questioned document into one of the printer classes. A dataset of 1200 papers from 20 distinct (13) laser and (7) inkjet printers achieved significant identification results. A proposed algorithm based on global features such as the Histogram of Oriented Gradient (HOG) and local features such as Local Binary Pattern (LBP) descriptors has been proposed for printer identification. For classification, Decision Trees (DT), k-Nearest Neighbors (k-NN), Random Forests, Aggregate bootstrapping (bagging), Adaptive-boosting (boosting), Support Vector Machine (SVM), and mixtures of these classifiers have been employed. The proposed algorithm can accurately classify the questioned documents into their appropriate printer classes. The adaptive boosting classifier attained a 96% accuracy. The proposed algorithm is compared to four recently published algorithms that used the same dataset and gives better classification accuracy. [ABSTRACT FROM AUTHOR]

This paper presents the performance analysis of object detection algorithms using phase and statistical texture features. Robust metrics like accuracy and precision are used to perform the evaluation. Feature extraction is one of the prime requirements to represent an object. This paper provides a comprehensive and broad framework to phase feature and statistical feature extraction techniques and compares their performance in terms of accuracy and precision. In phase-based approach of object detection group of phase features are used to model the background. Whereas we represented the statistics of object by calculating GLCM features of the block images in statistical feature extraction methods. Support Vector machine algorithm is used to classify object. This method is developed to achieve higher accuracy and speed. Performance analysis of these two methods shows that phase feature is robust to illumination variations. Also, the obtained accuracy is good, but the computational complexity is high. Whereas co-occurrence matrix based statistical approach of object detection a good tradeoff between speed and accuracy. [ABSTRACT FROM AUTHOR]

Anomaly in geoacoustic emission is an important earthquake precursor. Current geoacoustic anomaly detection methods are limited by their low signal-to-noise ratio, low intensity, sample imbalance, and low accuracy. Therefore, this paper proposes a clone selection algorithm optimized one-class support vector machine method (CSA-OCSVM) for geoacoustic anomaly detection. First, the interquartile range (IQR), cubic spline interpolation, and time window are designed to amplify the geoacoustic signal intensity and energy change rules to reduce the interference of geoacoustic signal noise and intensity. Secondly, to address the imbalance of positive and negative samples in geoacoustic anomaly detection, a one-class support vector machine is introduced for anomaly detection. Meanwhile, in view of the optimization capabilities of the clone selection algorithm, it is adopted to optimize the hyperparameters of OCSVM to improve its detection accuracy. Finally, the proposed model is applied to geoacoustic data anomaly detection in nine different datasets, which are derived from our self-developed acoustic electromagnetic to AI (AETA) system, to verify its effectiveness. By designing comparative experiments with IQR, genetic algorithm OCSVM (GA-OCSVM), particle swarm optimization OCSVM (PSO-OCSVM), and evaluating the performance of the true positive rate (TPR) and false positive rate (FPR), the experimental results depict that the proposed model is superior to the existing state-of-the-art geoacoustic anomaly detection approaches. [ABSTRACT FROM AUTHOR]

This study utilizes classification models to provide a robust algorithm for imbalanced data where the minority class is of the interest, that is, in the context of default payments. In developing an integrated predictive accuracy algorithm, this study proposes machine learning classifiers and applies DNN, SVM, KNN, and ANN. The proposed algorithm utilizes a 30,000 imbalanced dataset to improve the accuracy of the prediction of default payments by implementing oversampling and undersampling strategies, such as synthetic minority oversampling technique (SMOTE), SVM SMOTE, random undersampling, and ALL-KNN. The results indicate that the SVM under the ALL-KNN sampling technique is able to achieve an accuracy of 98.6%, with the lowest cross entropy loss measurement of 0.028. Through the accurate implementation of the neural networks and neurons used in the proposed algorithm, this paper presents better insights into the functioning of the neural networks when used in conjunction with the resampling techniques. Using the methodology and algorithm presented in this study, credit risk assessments can be more accurately predicted in practical applications where most of the clients are categorized as non-default payments. [ABSTRACT FROM AUTHOR]

There is a widespread use of cluster-based routing in wireless sensor networks since it is the most energy-efficient. Idealizing cluster heads, on the other hand, is NP-hard and hence requires heuristic or metaheuristic approaches. However, while outperforming algorithms, metaheuristics computation time restricts its ability to respond to routing requests as rapidly as algorithms can today. A network's or an application's parameters can't be easily accommodated by routing methods. This paper offers the HMML, a combination model combining hybrid metaheuristics and machine-learning. Our HMML model makes use of an automated tuning metaheuristic (e.g. evolutionary algorithm) to fine-tune the heuristic technique for each specific configuration. For a variety of combinations, this is done. A network simulation is run using the modified heuristic algorithm in each configuration to arrive at a solution. As a result, a comprehensive dataset for a variety of conditions is produced (e.g., support vector machine). These characteristics include local (round-state), global (network-state), and application-specific aspects of the input feature vector. After training, the HMML model may be used to quickly cluster data. Machine learning's capacity to generalize helps us comprehend the metaheuristic algorithm's behavior in identifying optimal paths for previous configurations. Simulation studies show that HMML can adapt to varied applications while extending network life which increases upto 5% for total energy consumption. [ABSTRACT FROM AUTHOR]

Identifying and assessing Parkinson's disease in its early stages is critical to effectively monitoring the disease's progression. Methodologies based on machine learning enhanced speech analysis are gaining popularity as the potential of this field is revealed. Acoustic features, in particular, are used in a variety of algorithms for machine learning and could serve as indicators of the general health of subjects' voices. In this research paper, a novel method is introduced for the automated detection of Parkinson's disease through speech signal analysis, a support vector machines classifier (SVM) and an Artificial Neural Network (ANN) are used to evaluate and classify the data based on two acoustic features: Bark Frequency Cepstral Coefficients (BFCC) and Mel Frequency Cepstral Coefficients (MFCC). These features are extracted from the denoised signals using Empirical Mode Decomposition (EMD). The most relevant results obtained for a dataset of 38 participants are by the BFCC coefficients with an accuracy up to 92.10%. These results confirm that EMD-BFCC-SVM method can contribute to the detection of Parkinson's disease. [ABSTRACT FROM AUTHOR]

Several machine learning (ML) techniques have demonstrated efficacy in precisely forecasting HIV risk and identifying the most eligible individuals for HIV testing in various countries. Nevertheless, there is a data gap on the utility of ML algorithms in strengthening HIV testing worldwide. This systematic review aimed to evaluate how effectively ML algorithms can enhance the efficiency and accuracy of HIV testing interventions and to identify key outcomes, successes, gaps, opportunities, and limitations in their implementation. This review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. A comprehensive literature search was conducted via PubMed, Google Scholar, Web of Science, Science Direct, Scopus, and Gale OneFile databases. Out of the 845 identified articles, 51 studies were eligible. More than 75% of the articles included in this review were conducted in the Americas and various parts of Sub-Saharan Africa, and a few were from Europe, Asia, and Australia. The most common algorithms applied were logistic regression, deep learning, support vector machine, random forest, extreme gradient booster, decision tree, and the least absolute shrinkage selection operator model. The findings demonstrate that ML techniques exhibit higher accuracy in predicting HIV risk/testing compared to traditional approaches. Machine learning models enhance early prediction of HIV transmission, facilitate viable testing strategies to improve the efficiency of testing services, and optimize resource allocation, ultimately leading to improved HIV testing. This review points to the positive impact of ML in enhancing early prediction of HIV spread, optimizing HIV testing approaches, improving efficiency, and eventually enhancing the accuracy of HIV diagnosis. We strongly recommend the integration of ML into HIV testing programs for efficient and accurate HIV testing. [ABSTRACT FROM AUTHOR]

Audio steganalysis has been little explored due to its complexity and randomness, which complicate the analysis. Audio files generate marks in the frequency domain; these marks are known as fingerprints and make the files unique. This allows us to differentiate between audio vectors. In this work, the use of the Goertzel algorithm as a steganalyzer in the frequency domain is combined with the proposed sliding window adaptation to allow the analyzed audio vectors to be compared, enabling the differences between the vectors to be identified. We then apply linear prediction to the vectors to detect any modifications in the acoustic signatures. The implemented Goertzel algorithm is computationally less complex than other proposed stegoanalyzers based on convolutional neural networks or other types of classifiers of lower complexity, such as support vector machines (SVD). These methods previously required an extensive audio database to train the network, and thus detect possible stegoaudio through the matches they find. Unlike the proposed Goertzel algorithm, which works individually with the audio vector in question, it locates the difference in tone and generates an alert for the possible stegoaudio. In this work, we apply the classic Goertzel algorithm to detect frequencies that have possibly been modified by insertions or alterations of the audio vectors. The final vectors are plotted to visualize the alteration zones. The obtained results are evaluated qualitatively and quantitatively. To perform a double check of the fingerprint of the audio vectors, we obtain a linear prediction error to establish the percentage of statistical dependence between the processed audio signals. To validate the proposed method, we evaluate the audio quality metrics (AQMs) of the obtained result. Finally, we implement the stegoanalyzer oriented to AQMs to corroborate the obtained results. From the results obtained for the performance of the proposed stegoanalyzer, we demonstrate that we have a success rate of 100%. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to evaluate the usability of morphometric features obtained from mandibular panoramic radiographs in gender determination using machine learning algorithms. Materials and methods: High-resolution radiographs of 200 patients aged 20–77 (41.0 ± 12.7) were included in the study. Twelve different morphometric measurements were extracted from each digital panoramic radiography included in the study. These measurements were used as features in the machine learning phase in which six different machine learning algorithms were used (k-nearest neighbor, decision trees, support vector machines, naive Bayes, linear discrimination analysis, and neural networks). To evaluate the reliability, we have performed tenfold cross-validation and we repeated this 10 times for every classification process. This process enhances the reliability of the results for other datasets. Results: When all 12 features are used together, the accuracy rate is found to be 82.6 ± 0.5%. The classification accuracies are also compared using each feature alone. Three features that give the highest accuracy are coronoid height (80.9 ± 0.9%), condyle height (78.2 ± 0.5%), and ramus height (77.2 ± 0.4%), respectively. When compared to the classification algorithms, the highest accuracy was obtained with the naive Bayes algorithm with a rate of 84.0 ± 0.4%. Conclusion: Machine learning techniques can accurately determine gender by analyzing mandibular morphometric structures from digital panoramic radiographs. The most precise results are achieved by evaluating the structures in combination, using attributes obtained from applying the MRMR algorithm to all features. [ABSTRACT FROM AUTHOR]

One expanding area of bioinformatics is medical diagnosis through the categorization of biomedical characteristics. Automatic medical strategies to boost the diagnostic through machine learning (ML) methods are challenging. They require a formal examination of their performance to identify the best conditions that enhance the ML method. This work proposes variants of the Voting and Stacking (VC and SC) ensemble strategies based on diverse auto-tuning supervised machine learning techniques to increase the efficacy of traditional baseline classifiers for the automatic diagnosis of vertebral column orthopedic illnesses. The ensemble strategies are created by first combining a complete set of auto-tuned baseline classifiers based on different processes, such as geometric, probabilistic, logic, and optimization. Next, the three most promising classifiers are selected among k-Nearest Neighbors (kNN), Naïve Bayes (NB), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Decision Tree (DT). The grid-search K-Fold cross-validation strategy is applied to auto-tune the baseline classifier hyperparameters. The performances of the proposed ensemble strategies are independently compared with the auto-tuned baseline classifiers. A concise analysis evaluates accuracy, precision, recall, F1-score, and ROC-ACU metrics. The analysis also examines the misclassified disease elements to find the most and least reliable classifiers for this specific medical problem. The results show that the VC ensemble strategy provides an improvement comparable to that of the best baseline classifier (the kNN). Meanwhile, when all baseline classifiers are included in the SC ensemble, this strategy surpasses 95% in all the evaluated metrics, standing out as the most suitable option for classifying vertebral column diseases. [ABSTRACT FROM AUTHOR]

This paper gives a performance analysis of multiple vote classifiers based on metaclassification methods for estimating the risk of diabetes. The study's dataset includes a number of biological and clinical risk variables that can result in the development of diabetes. In the analysis, classifiers like Random Forest, Logistic Regression, Gradient Boosting, Support Vector Machines, and Artificial Neural Networks were used. In the study, each classifier was trained and evaluated separately, and the outcomes were compared to those attained using meta-classification methods. Some of the meta-classifiers used in the analysis included Majority Voting, Weighted Majority Voting, and Stacking. The effectiveness of each classifier was evaluated using a number of measures, including accuracy, precision, recall, F1-score, and Area under the Curve (AUC). The results show that meta-classification techniques often outperform solo classifiers in terms of prediction precision. Random Forest and Gradient Boosting, two different classifiers, had the highest accuracy, while Logistic Regression performed the worst. The best performing meta-classifier was stacking, which achieved an accuracy of 84.25%. Weighted Majority Voting came in second (83.86%) and Majority Voting came in third (82.95%). [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is the leading cause of death among women worldwide. It affects in general women older than 40 years old. Medical images analysis is one of the most promising research areas since it provides facilities for diagnosis and decision-making of several diseases such as BC. This paper conducts a Structured Literature Review (SLR) of the use of Machine Learning (ML) and Image Processing (IP) techniques to deal with BC imaging. A set of 530 papers published between 2000 and August 2019 were selected and analyzed according to ten criteria: year and publication channel, empirical type, research type, medical task, machine learning techniques, datasets used, validation methods, performance measures and image processing techniques which include image pre-processing, segmentation, feature extraction and feature selection. Results showed that diagnosis was the most used medical task and that Deep Learning techniques (DL) were largely used to perform classification. Furthermore, we found out that classification was the most ML objective investigated followed by prediction and clustering. Most of the selected studies used Mammograms as imaging modalities rather than Ultrasound or Magnetic Resonance Imaging with the use of public or private datasets with MIAS as the most frequently investigated public dataset. As for image processing techniques, the majority of the selected studies pre-process their input images by reducing the noise and normalizing the colors, and some of them use segmentation to extract the region of interest with the thresholding method. For feature extraction, we note that researchers extracted the relevant features using classical feature extraction techniques (e.g. Texture features, Shape features, etc.) or DL techniques (e. g. VGG16, VGG19, ResNet, etc.), and finally few papers used feature selection techniques in particular the filter methods. [ABSTRACT FROM AUTHOR]

The high dimensionality and massive amount of data in open environments make the existing low-dimensional outlier detection methods time-consuming. The support vector machine (SVM) is a commonly used outlier detection method. However, the SVM still faces the problem of difficulty in obtaining optimal parameters quickly and effectively, resulting in low detection efficiency, poor stability, and difficulty in applying to open environment datasets. In order to improve the efficiency and stability of outlier detection, this paper proposes an improved sparrow search algorithm and uses it to optimize SVM parameters. First, the traditional sparrow search algorithm is improved by using improved backtracking learning and variable logarithmic spirals. Then, the improved sparrow search method is used to optimize SVM parameters, and the optimized support vector machine is applied to the field of outlier detection. Simulation experimental results show that the proposed method is significantly better than the compared classification algorithms in multiple evaluation indicators, with better detection efficiency, stability, and generalization ability. [ABSTRACT FROM AUTHOR]

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Introduction: Multi-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer’s disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently. Methods: In this paper, a total of 180 subjects consisting of 44 AD, 66 MCI and 58 HC subjects were enrolled, and the multi-modalities of the resting-state functional magnetic resonance imaging (rs-fMRI) and the structural MRI (sMRI) for all participants were obtained. Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing. Next, these metrics were selected with a minimal redundancy maximal relevance (MRMR) method and a sequential feature collection (SFC) algorithm, and only a subset of optimal features were retained after this step. Finally, the support vector machine (SVM) based classification methods and artificial neural network (ANN) algorithm were utilized to identify the multi-class of AD, MCI and HC subjects in single modal and multi-modal metrics respectively, and a nested ten-fold cross-validation was utilized to estimate the final classification performance. Results: The results of the SVM and ANN based methods indicated the best accuracies of 80.36 and 74.40%, respectively, by utilizing all the multi-modal metrics, and the optimal accuracies for AD, MCI and HC were 79.55, 78.79 and 82.76%, respectively, in the SVM based method. In contrast, when using single modal metric, the SVM based method obtained a best accuracy of 72.62% with the HE metric, and the accuracies for AD, MCI and HC subjects were just 56.82, 80.30 and 75.86%, respectively. Moreover, the overlapping abnormal brain regions detected by multi-modal metrics were mainly located at posterior cingulate gyrus, superior frontal gyrus and cuneus. Conclusion: Taken together, the SVM based method with multi-modal metrics could provide effective diagnostic information for identifying AD, MCI and HC subject. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The automatic calculation of sediment maps from hydroacoustic data is of great importance for habitat and sediment mapping as well as monitoring tasks. For this reason, numerous papers have been published that are based on a variety of algorithms and different kinds of input data. However, the current literature lacks comparative studies that investigate the performance of different approaches in depth. Therefore, this study aims to provide recommendations for suitable approaches for the automatic classification of side-scan sonar data that can be applied by agencies and researchers. With random forests, support vector machines, and convolutional neural networks, both traditional machine-learning methods and novel deep learning techniques have been implemented to evaluate their performance regarding the classification of backscatter data from two study sites located in the Sylt Outer Reef in the German Bight. Simple statistical values, textural features, and Weyl coefficients were calculated for different patch sizes as well as levels of quantization and then utilized in the machine-learning algorithms. It is found that large image patches of 32 px size and the combined use of different feature groups lead to the best classification performances. Further, the neural network and support vector machines generated visually more appealing sediment maps than random forests, despite scoring lower overall accuracy. Based on these findings, we recommend classifying side-scan sonar data with image patches of 32 px size and 6-bit quantization either directly in neural networks or with the combined use of multiple feature groups in support vector machines. [ABSTRACT FROM AUTHOR]

Little work has been carried out to predict the comfort of aircraft seats, a component in close contact with the human body during travel. In order to more accurately predict the nonlinear and complex relationship between subjective and objective evaluations of comfort, this paper proposes a prediction method based on the Improved Particle Swarm Algorithm (IPSO) and optimized Support Vector Machine Regression (SVR). Focusing on the problems of the too-fast convergence and low accuracy of the traditional particle swarm algorithm (PSO), the improved particle swarm algorithm (IPSO) is obtained by linearly decreasing the dynamic adjustments of inertia weight ω , self-learning factor c 1 , and social factor c 2 ; then, the penalty parameter C and kernel function parameter σ of SVR are optimized by the IPSO algorithm, and the comfort prediction of IPSO-SVR is established. The prediction accuracy of IPSO-SVR was 94.00%, the root mean square error RMSE was 0.37, the mean absolute value error MAE was 0.32, and the goodness of fit R2 was 0.92. The results show that the optimized IPSO-SVR prediction model can more accurately predict seat comfort under different angles and backrest tilt angles and can provide reference and research value for related industries. The results show that the optimized nonlinear prediction model of IPSO-SVR has higher accuracy, and its prediction method is feasible and generalizable, meaning it can provide a reliable basis for the prediction of seat comfort under different angles and backrest inclinations, as well as providing reference and research value for related industries. [ABSTRACT FROM AUTHOR]