Explore the vast range of titles available.

With the rapid development of IoT technology, a large of number complex and diverse IoT devices are widely deployed, which brings new challenges for device identification due to the heterogeneous nature of devices. This paper proposes a network traffic-based IoT device recognition method, in order to solve the high cost problem of traditional recognition methods in the feature extraction process and the potential privacy leakage problem. The proposed method requires only a short period of time in the network traffic data of IoT devices, and extracts the protocol statistical features and flow-level statistical features of this data. It avoids in-depth inspection of packet payloads and reduces the cost of feature extraction effectively. It is demonstrated that the proposed method can improve the performance of device identification while ensuring privacy security through empirical studies on two widely recognized public datasets. The proposed method provides users with a low-cost, high-efficiency IoT device identification solution with strong privacy protection, which promotes wider and more secure application of IoT technology.

Feature extraction is the main core in diagnosis, classification, clustering, recognition, and detection. Many researchers may by interesting in choosing suitable features that used in the applications. In this paper, the most important features methods are collected, and explained each one. The features in this paper are divided into four groups; Geometric features, Statistical features, Texture features, and Color features. It explains the methodology of each method, its equations, and application. In this paper, we made acomparison among them by using two types of image, one type for face images (163 images divided into 113 for training and 50 for testing) and the other for plant images(130 images divided into 100 for training and 30 for testing) to test the features in geometric and textures. Each type of image group shows that each type of images may be used suitable features may differ from other types.

As robots begin to perform jobs autonomously, with minimal or no human intervention, a new challenge arises: robots also need to autonomously detect errors and recover from faults. In this paper, we present a Support Vector Machine (SVM)-based fault diagnosis system for a bio-inspired reconfigurable robot named Scorpio. The diagnosis system needs to detect and classify faults while Scorpio uses its crawling and rolling locomotion modes. Specifically, we classify between faulty and non-faulty conditions by analyzing onboard Inertial Measurement Unit (IMU) sensor data. The data capture nine different locomotion gaits, which include rolling and crawling modes, at three different speeds. Statistical methods are applied to extract features and to reduce the dimensionality of original IMU sensor data features. These statistical features were given as inputs for training and testing. Additionally, the c-Support Vector Classification (c-SVC) and nu-SVC models of SVM, and their fault classification accuracies, were compared. The results show that the proposed SVM approach can be used to autonomously diagnose locomotion gait faults while the reconfigurable robot is in operation.

Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain signals produced by brain neurons. Neurons are connected to each other in a complex way to communicate with human organs and generate signals. The monitoring of these brain signals is commonly done using Electroencephalogram (EEG) and Electrocorticography (ECoG) media. These signals are complex, noisy, non-linear, non-stationary and produce a high volume of data. Hence, the detection of seizures and discovery of the brain-related knowledge is a challenging task. Machine learning classifiers are able to classify EEG data and detect seizures along with revealing relevant sensible patterns without compromising performance. As such, various researchers have developed number of approaches to seizure detection using machine learning classifiers and statistical features. The main challenges are selecting appropriate classifiers and features. The aim of this paper is to present an overview of the wide varieties of these techniques over the last few years based on the taxonomy of statistical features and machine learning classifiers—‘black-box’ and ‘non-black-box’. The presented state-of-the-art methods and ideas will give a detailed understanding about seizure detection and classification, and research directions in the future.

Human behavior pattern recognition (BPR) from accelerometer signals is a challenging problem due to variations in signal durations of different behaviors. Analysis of human behaviors provides in depth observations of subject’s routines, energy consumption and muscular stress. Such observations hold key importance for the athletes and physically ailing humans, who are highly sensitive to even minor injuries. A novel idea having variant of genetic algorithm is proposed in this paper to solve complex feature selection and classification problems using sensor data. The proposed BPR system, based on statistical dependencies between behaviors and respective signal data, has been used to extract statistical features along with acoustic signal features like zero crossing rate to maximize the possibility of getting optimal feature values. Then, reweighting of features is introduced in a feature selection phase to facilitate the segregation of behaviors. These reweighted features are further processed by biological operations of crossover and mutation to adapt varying signal patterns for significant accuracy results. Experiments on wearable sensors benchmark datasets HMP, WISDM and self-annotated IMSB datasets have been demonstrated to testify the efficacy of the proposed work over state-of-the-art methods.

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

Induction motors constitute the largest proportion of motors in industry. This type of motor experiences different types of failures, such as broken bars, eccentricity, and inter-turn failure. Stator winding faults account for approximately 36% of these failures. As such, condition monitoring is used to protect motors from sudden breakdowns. This paper proposes the use of neural networks as an efficient diagnostic tool for estimating the percentage of stator winding shorted turns in three-phase induction motors. A MATLAB-based model was developed and simulated under different fault-load combination cases for different sizes of motors. The motor’s developed electromechanical torque was selected as a fault indicator. For the design and training of the neural network, the mean, variance, max, min, and F120 time based on statistical and frequency-related features were found to be very distinct for correlating the captured electromechanical torque with its corresponding percentage of shorted turns. In the training phase of the neural network, five different motors were used and are referred to as seen motors. On the other hand, for testing the efficiency of the developed diagnostic tool, the electromechanical torque under different fault-load combination cases, previously never seen from the first five motors and those of two new motors (referred to as unseen), was used. Testing results revealed accuracy in the range of 88–99%.

To address the non-stationarity of rolling bearing vibration signals and the interference under complex working conditions, this paper proposes a fault diagnosis method based on the fusion of short-time Fourier transform (STFT) and time-domain statistical features, optimized by an adaptive Lévy–seagull optimization algorithm (AL-SOA) for Bagging Tree (denoted as STSF-AL-SOA-BT). To simultaneously capture both the frequency-domain impulsive components and the time-domain waveform structure while controlling the input dimensionality, a lightweight dual-channel attention fusion module (AFF) is designed. The module adaptively weights and concatenates a 256-dimensional STFT spectrum with six time-domain statistical features, followed by dimensionality reduction via PCA to mitigate redundancy and overfitting. For optimizing the key structural parameters of the Bagging Tree, an AL-SOA strategy is developed, integrating Lévy flight and linear inertia weighting while maintaining an external elite archive to preserve the Pareto optimal set. This approach achieves a multi-objective balance among accuracy, model complexity, and training efficiency. The proposed method is systematically validated on the CWRU, SEU, and self-built SUT test rigs, achieving average accuracies of 98.88%, 98.50%, and 97.53%, respectively. Ablation studies demonstrate that both the attention mechanism and AL-SOA make significant contributions; the introduction of attention improves accuracy by 0.6–1.2% (p < 0.01), while the Pareto front analysis confirms an effective trade-off between accuracy and complexity. Overall, the proposed method balances discriminative capability, interpretability, and engineering deployability, providing a feasible solution for lightweight bearing fault diagnosis under variable operating conditions and strong noise interference.

Heavy hexagonal coding is a type of quantum error-correcting coding in which the edges and vertices of a low-degree graph are assigned auxiliary and physical qubits. While many topological code decoders have been presented, it is still difficult to construct the optimal decoder due to leakage errors and qubit collision. Therefore, this research proposes a Re-locative Guided Search optimized self-sparse attention-enabled convolutional Neural Network with Long Short-Term Memory (RlGS2-DCNTM) for performing effective error correction in quantum codes. The integration of the self-sparse attention mechanism in the proposed model increases the feature learning ability of the model to selectively focus on informative regions of the input codes. In addition, the use of statistical features computes the statistical properties of the input, thus aiding the model to perform complex tasks effectively. For model tuning, this research utilizes the RIGS nature-inspired algorithm that mimics the re-locative, foraging, and hunting strategies, which avoids local optima problems and improves the convergence speed of the RlGS2-DCNTM for Quantum error correction. When compared with other methods, the proposed RlGS2-DCNTM algorithm offers superior efficacy with a Minimum Mean Squared Error (MSE) of 4.26, Root Mean Squared Error of 2.06, Mean Absolute Error of 1.14 and a maximum correlation and of 0.96 and 0.92 respectively, which shows that the proposed model is highly suitable for real-time error decoding tasks.

Background Feature extraction of protein sequences is widely used in various research areas related to protein analysis, such as protein similarity analysis and prediction of protein functions or interactions. Results In this study, we introduce FEGS (Feature Extraction based on Graphical and Statistical features), a novel feature extraction model of protein sequences, by developing a new technique for graphical representation of protein sequences based on the physicochemical properties of amino acids and effectively employing the statistical features of protein sequences. By fusing the graphical and statistical features, FEGS transforms a protein sequence into a 578-dimensional numerical vector. When FEGS is applied to phylogenetic analysis on five protein sequence data sets, its performance is notably better than all of the other compared methods. Conclusion The FEGS method is carefully designed, which is practically powerful for extracting features of protein sequences. The current version of FEGS is developed to be user-friendly and is expected to play a crucial role in the related studies of protein sequence analyses.