Transfer Learning-Based Muscle Activity Decoding Scheme by Low-frequency sEMG for Wearable Low-cost Application

Credit risk analysis forms an important criterion for the endurance of the banking sector. Understanding credit liability of customer enhances the security stability of the banker. This research is aimed at comparing the predictive accuracy of data mining techniques with feature selection and extraction techniques in the default of credit card clients and suggesting the most efficient technique for analyzing credit card default. Customer's default payments in Taiwan were used as the dataset. Machine Learning classification algorithms like K-nearest neighbor(KNN), Naive Bayesian(NB), Decision Tree(DT) and Support Vector Machines(SVM) were applied to the dataset. Feature filter methods like information gain (IG), gain ratio (GR) and chi-square (CS) and Wrapper based methods like Best First search(BFS) and Hill Climbing search (HCS) were used to identify minimum features required. Feature extraction methods such as Principal Component Analysis(PCA) and Linear Discriminant Anal-ysis(LDA) were also applied. The minimum features of feature selection algorithms along with PCA and LDA were then applied to Base classifiers and their accuracies were tabulated and compared. SVM with feature selection and extraction techniques had the best predictive accuracy.

Machine learning today becomes more and more effective instrument to solve many particular problems, where there are difficulties to apply well known and described math model. In other words - it is a great tool to describe nonlinear phenomena. We tried to use this technique to improve existing process of stratigraphy and lithology interpretation and reduce costs on site by applying computer leaded predictions on the basis of existing on-field collected data. Article describes usage of machine learning algorithms for several geology data stratigraphy and lithology boundaries classification based on geophysics logging data for deposits in Kazakhstan. Correct marking of stratigraphy and lithology from geophysics logging data is complex non-linear task. To solve this task we applied several algorithms of machine learning: random forest, logistic regression, gradient boosting (scikit-learn library), k - nearest neighbour (KNN) and XGBoost.

The healthcare industry struggles with diagnosing health disorders and finding the right treatment for diseases at an earlier stage. Many diseases can be detected early and treated well. Thyroid malfunction is one such disease. One of the most important organs in both men and women is the thyroid gland, a vascular gland. This gland secretes the hormones that help to control the body's metabolism. Hyperthyroidism and hypothyroidism are two types of thyroid ailments. Clinical examination and numerous blood tests are necessary for the traditional methods of detecting thyroid disorders. The main aim is to more precisely identify thyroid dysfunction at an early stage. This research makes a study of a variety of classification approaches, including Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), Logistic Regression (LR), Linear Discriminant Analysis (LDA), K Nearest Neighbour (KNN) and Multi-Layer Perceptron (MLP), to predict thyroid illness as early as possible. This study majorly focuses on the performance metric accuracy observed from various Machine Learning (ML) classification Algorithms.

IntroductionThe application of motor imagery in human–computer interaction and rehabilitative medicine has attracted growing attention due to recent advances in brain–computer interface technologies. However, traditional EEG decoding paradigms based on fixed frequency–band segmentation often exhibit limited performance because they fail to capture individual variability in brain rhythms.MethodsThis work proposes an adaptive method that integrates the sparrow search algorithm (SSA) with Filter Bank Common Spatial Pattern (FBCSP) to optimize sub–band segmentation for motor imagery EEG decoding. SSA adaptively searches for optimal sub–band boundaries, enabling individualized frequency–band selection.ResultsExperiments on the BCI Competition IV 2a dataset under a cross–session evaluation protocol (training on session T, testing on session E) demonstrated that SSA–FBCSP effectively improves frequency–band adaptability. The SSA–FBCSP approach was further combined with Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and k–Nearest Neighbor (KNN) classifiers to evaluate the influence of different downstream classifiers.ConclusionAmong them, SSA–FBCSP–LDA achieved the best performance, outperforming the conventional uniform sub–band approach by 21.76% and reaching an average accuracy of 89.92%. The adaptively selected sub–bands closely matched the ERD/ERS distribution, confirming the method’s effectiveness in frequency–band optimization. Compared with recent deep–learning–based MI–EEG models, the proposed technique offers a balance of accuracy, interpretability, and computational efficiency, providing a promising direction for personalized brain–computer interface systems.

Signals play a fundamental role in science, technology, and communication by conveying information through varying patterns, amplitudes, and frequencies. This paper introduces innovative methodologies for processing electromyographic (EMG) signals to develop artificial intelligence systems capable of decoding muscle activity for controlling arm movements. The study investigates advanced signal processing techniques and machine learning classification algorithms using the GRABMyo dataset, aiming to enhance prosthetic control systems and rehabilitation technologies. A comprehensive analysis was conducted on signal processing techniques, including signal filtering and discrete wavelet transform (DWT), alongside a composite feature set comprising Mean Absolute Value (MAV), Waveform Length (WL), Zero Crossing (ZC), Slope Sign Changes (SSC), Root Mean Square (RMS), Enhanced Waveform Length (EWL), and Enhanced Mean Absolute Value (EMAV). These features, refined through Linear Discriminant Analysis (LDA) for dimensionality reduction, were classified using Support Vector Machine (SVM) algorithms. Signal filtering and DWT improved signal quality, facilitating better feature extraction, while the diverse feature set enhanced classification accuracy. LDA further improved accuracy by isolating the most informative features, and the SVM achieved optimal performance in decoding complex EMG patterns. Machine learning models, including K-Nearest Neighbor (KNN), Naïve Bayes (NB), and the SVM, were evaluated, with the SVM outperforming the others. The significance of these results lies in their potential applications in prosthetic control systems and rehabilitation technologies. By accurately decoding muscle activity, the developed systems can facilitate more intuitive and responsive robotic arm movements, contributing to the advancement of innovative solutions for individuals requiring prosthetic devices or undergoing rehabilitation, hence improving the quality of life for users. This research marks a significant step forward in the integration of advanced signal processing and machine learning in the field of EMG analysis.

Although the consortium blockchains commonly adopt consensus algorithms with Byzantine fault tolerance (such as practical Byzantine fault tolerance (PBFT)), its consensus efficiency will be degraded by the existence of malicious nodes or behaviors. However, the existing researches mainly focus on the detection of malicious behaviors for public blockchains, but are rare about consortium blockchains. In this paper, an anomaly detection model based on machine learning (ML) classification algorithm is proposed for consortium blockchains that adopt PBFT. Besides, a two-stage process is proposed to reduce the resource consumption for anomaly detection. The data needed for proposed model only has two dimensions and is convenient to obtain. The results of experiment show that ML is very effective in anomaly detection for consortium blockchains. Specifically, the algorithms with the highest accuracy are convolutional neural networks (CNN), k-nearest neighbor (KNN) and support vector machines (SVM) in turn. However, KNN and SVM are more suitable because resource consumption of both algorithms are one third of CNN, and the accuracy rates are above 0.9 which is 0.9% lower than CNN.

ABSTRACTObjectiveIn spinal surgery, precise identification of high‐speed bur milling states is crucial for patient safety. This study investigates whether integrating tactile and auditory perception can enhance the accuracy of milling state detection in robot‐assisted cervical laminectomy.MethodsBased on the mathematical and physical model of vibration and sound in high‐speed bur milling bone, the feasibility of employing vibration and sound characteristics to identify the milling states of high‐speed bur is studied systematically. Cervical laminectomy was performed on the cervical spine of the sheep. During the signal acquisition process, acceleration sensors and microphones were installed to collect vibration and sound signals, respectively. Seven milling states were set up in the experiment: (1) Milling depths of cortical bone (CTB): 0.5, 1.0, and 1.5 mm; (2) Milling depths of milling of cancellous bone (CCB): 0.5, 1.0, and 1.5 mm; (3) Boundary conditions: high‐speed bur idling or complete penetration of bone (PT). The milling speed was set at 0.5 mm/s, the milling angle was 45°, and the bur diameter was 4 mm. The vibration or sound was extracted by Fast Fourier Transform (FFT) in the frequency domain of the first nine harmonics to generate the feature vector in 9 dimensions (9‐D) space. These vibration and sound features were combined to form an 18‐D multi‐perception spatial vector for subsequent analysis, including five machine learning algorithms: Support Vector Machine (SVM), K Nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and Decision Tree (DT), and deep learning models: Long Short‐Term Memory networks (LSTM).ResultsBased on the 18‐D features of tactile and auditory multisensory fusion, the LSTM model is trained using 6600 sets of high‐speed bur milling data. In order to achieve the best performance, a layer‐by‐layer parameter optimization strategy was used to determine the optimal parameter configuration, and finally, a single‐layer LSTM with 12 memory units was constructed. In terms of accuracy and stability, the model is significantly superior to the machine learning algorithms (SVM, KNN, NB, LDA, and DT), and the accuracy of LSTM is 99.32% in the milling states identification of cervical lamina milling with high‐speed bur.ConclusionThrough theoretical analysis and experimental verification, the study built a multi‐perception fusion framework based on tactile and auditory perception and accurately identified the cervical vertebra milling states through the LSTM model, which can provide perception means for operational spinal surgery robots in the future.

The field of machine learning is growing in modern times, computational models are able to go beyond the performance of previous forms of artificial intelligence. The use of evaluation model ,selection of model and algorithm selecting techniques play an major role in machine learning study and also in field of industries. In this work, we made evaluation of various supervised, unsupervised machine learning classifiers for flower datasets. We made use of local features such as Histogram of gradient , Kaze, Local binary pattern(LBP) ,Oriented Fast and Rotated Brief( ORB), global features like Color Histograms, Haralick Textures , Hu Moments , fusion of both and Bag of visual words(BOVW) using Vocabulary builder K-Means clustering which represents color ,texture, shape features of image. Experiment is carried out on 20 classes of flower datasets with 100 images each. .Flower datasets have many characteristic in common like sunflower will be similar to daffodil in terms of color and texture .Hence to quantify the image we need to combine different feature descriptors like color, texture and shape features. We develop a Content based classification system to find efficiency comparison of different machine learning algorithms for classification and retrieval problems. Eleven classifiers mainly Support Vector Machine, K Nearest Neighbor, Gaussian Naive Bayes , CART, Kmeans, Linear Discriminant Analysis, Adaboost ,Logistic Regression, MLP, Random Forest, CNN are analyzed on the shape, color ,texture features. Experimentation are carried out and results are recorded using CPU as well as GPU on google cobalatory platform.

One of the most destructive natural hazard is an earthquake. Both the built environment and significant lives are lost. Due to presence of subduction zone and continuous continent to continent collision, major earthquakes in India are concentrated along the seismo-tectonically active Himalayan arc. Seismicity along the Alpine Himalayan Belt varies in well-defined patterns which are based on different parameters such as distance from tectonic source, presence of other historic earthquakes, intersections of tectonic features (in numbers), river length, presence of water body, meandering of rivers, soil conditions. An earthquake catalogue consisting of 540 earthquakes from year 1552 to 2020 is prepared for the consideration of data set. 117 tectonic features are considered for the study. For extracting feature a buffer of radius 25 km is considered along each earthquake of the catalogue. Four features namely distance from nearest tectonic feature, magnitude of earthquake, number of earthquakes within the buffer zone and presence of rivers (length) is extracted. A combination of these features allows identifying patterns based on training and classification of the entire data set. This paper is an attempt to compare four classification algorithms namely Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), K nearest neighbors (KNN) and Artificial Neural Networks (ANN) and present an integrated predictive model for identifying seismically susceptible zones (SSZ) around the Kangra Region. The seismicity in the study area is divided into two classes: class A and Class B based on the magnitude of earthquakes and are called as training data set for the classification algorithms. The training data set provides a basis for classifying a set of data into class A and Class B based on the extracted features. After classification the study area is the classified into two classes: Class A: Significant seismically susceptible area, Class B: less Susceptible zone. A comparative analysis is done in terms of accuracy of classifying an earthquake into the correct class as per training data set. Of the four classification methodologies, ANN showed the best performance by reducing the error in misclassifying the epicenters from either class. The errors accuracy of classifying an earthquake in the correct group were found to be 24.74%, 6.45%, 7.78%, 2.9% for LDA, SVM, KNN and ANN respectively.

Current studies document the effectiveness of multi-sensing technique implementation to mimic or to complement human senses. This work demonstrated the successful application of multi-sensing techniques such electronic tongue (e-tongue), electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR). The fusion of these modalities enhance the classification of pure Tualang honey using Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). KNN and PNN are able to classify between pure and adulterated honey samples, outperform LDA and SVM. By performing data fusion, SVM and LDA classifier can achieved more than 80% accuracy, while KNN and PNN obtained greater precision, up to 96% correct classification. The findings confirmed that, multi-sensing technique; either KNN or PNN was significantly superior compared to SVM and LDA classification methods. Thus, both analyses are able to discriminate between pure and adulterated honey.

A novel online tool condition monitoring method for milling titanium alloy with consideration of tool wear law

In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes (N.B.), and support vector machine (SVM) have made significant progress in classification issues. This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements. The motor movements considered are related to the left hand, right hand, both fists, feet, and relaxation, making this a multiclass problem. In this study, nine ML algorithms were trained with a dataset created by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used to create a dataset related to movement. We used electrodes C3, C1, CZ, C2, and C4 according to the standard 10-10 placement. Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and statistical techniques to obtain the set of features. LabVIEW™2015 version custom applications were used for reading the EEG signals; for channel selection, noise filtering, band selection, and feature extraction operations; and for creating the dataset. MATLAB 2021a was used for training, testing, and evaluating the performance metrics of the ML algorithms. In this study, the model of Medium-ANN achieved the best performance, with an AUC average of 0.9998, Cohen’s Kappa coefficient of 0.9552, a Matthews correlation coefficient of 0.9819, and a loss of 0.0147. These findings suggest the applicability of our approach to different scenarios, such as implementing robotic prostheses, where the use of superficial features is an acceptable option when resources are limited, as in embedded systems or edge computing devices.

Introducing two Random Forest based methods for cloud detection in remote sensing images

Brain Computer Interface (BCI) technology is used to help patients who do not have control over motor neurons such as ALS or paralyzed patients, to communicate with outer world. This work aims to classify motor imageries using real-time EEG dataset, which was published by Graz University, Austria. The dataset consists of two-channel EEG signals of right-hand movement imagery and left-hand movement imagery of 8 subjects. There are a total of 120 motor imagery trials (60 left and 60 right) EEG signals recorded from each subject. EEG signals are filtered and feature vectors were extracted that consist of 24, 32 and 40 relative band power values (RBPV). In this work, feature vectors classified by three different methods, linear discriminant analysis (LDA), K nearest neighbor (KNN) and support vector machines (SVM). Results show that best performance was achieved by 24 RBPV feature vector and LDA classification method.

Healthcare industry is advancing at a lightning speed with extensive usage of IT tools and techniques. The use of machine learning algorithms is not only restricted to the field of computer science. It has sneaked into the healthcare industry too and is assisting the medical practitioners in the prediction of the onset of several diseases based on a particular set of attributes like age, BMI, blood pressure, glucose and insulin level and so on. Diabetes is one such disease that is growing at a very rapid rate and is pretty fatal leading to the requirement of a promising prediction system to diagnose the onset of the disease before it silently attacks the patients and causes the avoidable damage to health. Machine Learning techniques are doable in mining the diabetes dataset to efficiently classify and predict the disease. In this study, four machine learning classification algorithms are compared to find the more viable one in classifying a diabetic and a non-diabetic. KeywordsDiabetesClassificationPIDD (Pima Indian Diabetes Dataset)LDA (Linear Discriminant Analysis)kNN (k-Nearest Neighbour)RF (Random Forest)SVM (Support Vector Machine)ANN (Artificial Neural Network)