Localization of quantum walks with classical randomness: Comparison between manual methods and supervised machine learning.

Though large amounts of work with artificial intelligence are used in typhoon rainfall forecasting, the predictive skills of existing models are unsatisfactory. To address this problem, this study aims to propose an improved cluster‐wise typhoon rainfall forecasting model that integrates the grid‐search cross‐validation method with machine learning and deep learning (DL) models including support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), convolutional neural network (CNN), and long short‐term memory (LSTM). Grid‐search cross‐validation is a modified parameterization technique that helps to find the best parameters for machine/DL models. In the first stage, a second‐order polynomial regression model was used to cluster the typhoon track; and in the second stage, cluster‐wise typhoon rainfall was recognized within a 500 km radius from each typhoon center. After that, a modified cluster‐wise typhoon rainfall forecasting model was proposed using cluster‐wise antecedent hourly typhoon rainfall within this distance for 1–6 hr lead time. Results show that the proposed model based on the SVM, RF, AdaBoost, CNN, and LSTM is capable of providing more accurate forecasts (the efficiency of the forecast is increased by 45%–90%) than the existing typhoon rainfall forecasting models that are based on SVM with a genetic algorithm, RF, artificial neural network, multilayer perceptron network, and deep neural network. Therefore, the current study recommends using cluster‐wise typhoon rainfall forecasting model with a grid‐search cross‐validation method for disaster prevention and mitigation.

Due to the rapid growth of android applications and mobile users in this technological era, there is a large increase in cyber attacks through mobile phones. During Pandemic period, mobile malware attacks are one of the top most cyber attacks observed in android mobile users to steal the user personal credentials by intrusion of adware, spyware, banking malware, SMS malware, riskware, viruses, Trojan horse, worms, keylogger and many more. Machine learning methods are very useful and amicable to detect mobile malwares. Automation of the mobile malware detection is the need of the hour and it is imperative to identify the most suitable machine learning techniques. This book entitled “Dynamic Analysis based Mobile Malware Classification using Supervised Machine Learning Methods” investigates the evaluation of supervised machine learning algorithms that are applied to detect and classify the mobile malwares. A systematic method of evaluation of supervised machine learning model to detect the malware data points and to classify them into binary classification as malware or benign is essential. The purpose of evaluating the supervised machine learning algorithms is to identify the best supervised machine learning model for mobile malware detection with high efficacy rate.  All important performance measures like Precision, Recall, F1 score, R2 score, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error are applied and the entire experiments are conducted using benchmark dataset taken from kaggle community. Nine Supervised Machine Learning methods such as Decision Tree, Random Forest, K-Nearest Neighbor, Support Vector Machine, Naïve Bayes, AdaBoost, Multi-layer Perceptron, Logistic Regression and Linear Discriminant Analysis are experimented and the results are discussed. +

Machine learning model is capable of achieving an incredible great accuracy by training a large amount of data with many dimensions of features in it. To accomplish such a goal, we are currently in desperate need of powerful computers with CPUs (Computation Processing Unit) and GPUs (Graphic Processing Unit) that are competent of the workload of the training process of those machine learning models. Due to the problem that there are multiple disparate computationally intensive tasks that are lengthy period of time during training phase of machine learning, devices like smartphone and personal computer cannot achieve great efficiency during the training phase of machine learning. Instead of enhancing computational performance of devices and improve the machine learning algorithm, another more feasible way to bypass such problem is to apply dimensionality reduction methods on raw data in hope of reducing the number of features that are passing into the machine learning model during the training phase. By doing so, we can shrink the originally large number of features to several dimensions that is accomplishable for our device to compute and meanwhile, sufficient enough to have a model with acceptable accuracy trained. The purpose of this study to explore and investigate the potential benefits and disadvantages of dimensionality reduction on multiple distinct machine learning algorithm by comparing the performances of each machine learning algorithms when passing the features of raw datasets in and when passing the dataset with the features that survived after dimensionality reduction. The performance will be measured by completing a typical machine learning task: handwriting digit classification. Among all the dimensionalities methods that have been discovered, a traditional and conventional dimensionality reduction method, PCA (Principal Components Analysis), is selected. Such method has been proven successful and efficient in generating the set of data with lower dimension features and apply on an image classification task using several supervised machine learning methods, including KNN (k-nearest neighbors), SVM (Support vector machine) and CNN (Convolutional Neural Network).

Background: The merging of machine learning and deep learning approaches offers a viable route for improving breast cancer prediction in this era of advanced technology and data-driven healthcare. This study focused on the use of supervised machine learning and deep learning algorithms for breast cancer prediction using Wisconsin breast cancer dataset. Methods: The features from this dataset were computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. Supervised machine learning algorithms, such as logistic regression, random forests, and support vector machines were compared with deep learning algorithms like convolutional and recurrent neural networks (RNN and CNN) for the prediction of breast cancer. The techniques combine clinical, genomic, and image-based data, that both aid in early diagnosis and individualized treatment suggestions. Results: The results of model comparison were very promising, with models obtaining excellent accuracy, sensitivity, and specificity in breast cancer diagnosis. The study used a pair plot and different classifiers with their accuracy scores on test dataset to determine which machine learning model was most suitable based on the relationship of variables represented in the plot. Support vector machine (SVM) has the highest accuracy of 98.8% and Artificial Neural Network (ANN) has lowest accuracy of 95.80% using confusion matrix and the accuracy metric. Conclusion: The study revealed that Support Vector Machine (SVM) has the highest prediction capability for Breast Cancer using Supervised Machine Learning and Deep Leaning. The new classifier performed better than the existing breast cancer prediction models in the literature.

Introduction/purpose: The brain wave application is widespread in recent years, especially in the applications that aid the impaired people suffered from amputation or paralysis. The objective of this research is to assess how well different supervised machine learning algorithms classify brain signals, with an emphasis on improving the precision and effectiveness of brain-computer interface applications. Method: In this work, brain signal data was analyzed using a number of well-known supervised learning models, such as Support Vector Machines (SVM) and Neural Networks (NN). The data set was taken from a previous study. Twenty five participants imagined moving their right arm (elbow and wrist) while the brain signals were recorded during that process. The dataset was prepared for the analysis by the application of meticulous preprocessing and feature extraction procedures. Then the resulting data were subjected to classification. Results: The study highlights how crucial feature selection and model modification are for maximizing classification results. Supervised machine learning methods have great potential for classifying brain signals, particularly SVM and NN. Conclusion: The use of SVM and NN has the potential to completely transform the creation of cutting-edge brain-computer interfaces. The integration of these models with real-time data should be investigated in future studies.

The massive development of science and technology in the industrial era 4.0 includes artificial intelligence, which is purposed to produce research output in the field of geology more accurately and can be completed in a short time using large amounts of data. Machine learning is a part of artificial intelligence that can provide learning processes on computers independently without explicit programming. The process of identifying rocks through classification can be done using machine learning. The study area is in the Manjimup region, Western Australia which consists of Volcanogenic Massive Sulphide (VMS) deposits. This study purposed to determine the classification of rock minerals using accuracy values from the evaluation of models generated using supervised machine learning based on spectral data, namely Short-Wavelength Infrared (SWIR), and Mid or Thermal Infrared (TIR) acquired from electromagnetic spectrum measurements to identify rock mineral features. The spectral data comes from five rock drilling data in the study area. The supervised machine learning method used to determine the best accuracy consists of 5 types of methods, which are K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Multi-layer Perceptron (MLP). Machine learning is completed by supervised method because the research data contains information about label data, which is the type of rock mineral so that it can produce a classification based on the level of accuracy for each type of rock mineral data. The SVM method produces the best accuracy on SWIR data with 82.5% accuracy and the MLP method produces the best accuracy on TIR data with 82% accuracy for rock mineral classification.

The study focuses on the advantages of using non-invasive approaches to detect epileptic seizures, aiming to prevent injuries from sudden falls and improve medical diagnosis and management. It presents an EEG Acquisition System (EAS) that integrates a multi-level amplifier with four machine learning algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multilayer Perceptron Neural Networks (MLPNN), and Convolutional Neural Network (CNN)—for signal classification. These methods are chosen for their accuracy and rapid processing, making them ideal for real-time use. The research analyzed data from 310 participants to detect epileptic patterns under various conditions. The results show that the SVM model achieved the highest accuracy at 96.37%±2.68 in the small data, followed by the CNN with 95.25%±2.25 in real-time detection, MLPNN at 89.87%, and KNN with 86.37% . This demonstrates CNN's strong performance in seizure detection, complemented by a detailed comparison of sensitivity and specificity across all models to assess their predictive efficiency. The system tells the scientist if there is an epilepsy , dementia ,or a normal brain activity, if the system detects the abnormal activities, it could map it on the brain map with a precision of 98%.

Liver disease is one of the most prominent causes of the increase in the death rate worldwide. These death rates can be reduced by early liver diagnosis. Computed tomography (CT) is a method for the analysis of liver images in clinical practice. To analyze a large number of liver images, radiologists face problems that sometimes lead to the wrong classifications of liver diseases, eventually resulting in severe conditions, such as liver cancer. Thus, a machine-learning-based method is needed to classify such problems based on their texture features. This paper suggests two different kinds of algorithms to address this challenging task of liver disease classification. Our first method, which is based on conventional machine learning, uses texture features for classification. This method uses conventional machine learning through automated texture analysis and supervised machine learning methods. For this purpose, 3000 clinically verified CT image samples were obtained from 71 patients. Appropriate image classes belonging to the same disease were trained to confirm the abnormalities in liver tissues by using supervised learning methods. Our proposed method correctly quantified asymmetric patterns in CT images using machine learning. We evaluated the effectiveness of the feature vector with the K Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifiers. The second algorithm proposes a semantic segmentation model for liver disease identification. Our model is based on semantic image segmentation (SIS) using a convolutional neural network (CNN). The model encodes high-density maps through a specific guided attention method. The trained model classifies CT images into five different categories of various diseases. The compelling results obtained confirm the effectiveness of the proposed model. The study concludes that abnormalities in the human liver could be discriminated and diagnosed by texture analysis techniques, which may also assist radiologists and medical physicists in predicting the severity and proliferation of abnormalities in liver diseases.

Emotion recognition plays a pivotal role in advancing Human-Computer Interaction (HCI) by enabling systems to understand and respond to human emotional states. Among various modalities, Speech Emotion Recognition (SER) stands out as a non-invasive, cost-effective, and temporally efficient approach for detecting emotions. This study leverages the RAVDESS dataset, which provides high-quality audio recordings for emotion classification. The proposed methodology involves preprocessing audio signals to remove noise, extracting temporal and spectral features in both time and frequency domains, and implementing machine learning models for multi-class emotion classification. The study evaluates and compares the performance of several classification models, including Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Decision Trees (DT). Experimental results demonstrate promising accuracy levels, highlighting the potential of these machine learning techniques in SER. This research contributes to the integration of SER within Brain-Computer Interfaces (BCI) and other emotion-aware applications, paving the way for enhanced interactive systems. Keywords – Speech Emotion Recognition -Machine Learning - Random Forest -Multilayer Perceptron - Support Vector Machine - Convolutional Neural Networks - Decision Tree - RAVDESS Dataset

Detection and classification of matrix cracking in laminated composites using guided wave propagation and artificial neural networks

In the field of medicine, with the introduction of computer systems that can collect and analyze massive amounts of data, many non-invasive diagnostic methods are being developed for a variety of conditions. In this study, our aim is to develop a non-invasive method of classifying respiratory sounds that are recorded by an electronic stethoscope and the audio recording software that uses various machine learning algorithms.In order to store respiratory sounds on a computer, we developed a cost-effective and easy-to-use electronic stethoscope that can be used with any device. Using this device, we recorded 17,930 lung sounds from 1630 subjects.We employed two types of machine learning algorithms; mel frequency cepstral coefficient (MFCC) features in a support vector machine (SVM) and spectrogram images in the convolutional neural network (CNN). Since using MFCC features with a SVM algorithm is a generally accepted classification method for audio, we utilized its results to benchmark the CNN algorithm. We prepared four data sets for each CNN and SVM algorithm to classify respiratory audio: (1) healthy versus pathological classification; (2) rale, rhonchus, and normal sound classification; (3) singular respiratory sound type classification; and (4) audio type classification with all sound types. Accuracy results of the experiments were; (1) CNN 86%, SVM 86%, (2) CNN 76%, SVM 75%, (3) CNN 80%, SVM 80%, and (4) CNN 62%, SVM 62%, respectively.As a result, we found out that spectrogram image classification with CNN algorithm works as well as the SVM algorithm, and given the large amount of data, CNN and SVM machine learning algorithms can accurately classify and pre-diagnose respiratory audio.

The Sixth International Symposium on Neural Networks (ISNN 2009)

This paper addresses electric motor fault diagnosis using supervised machine learning classification. A total of 15 distinct fault types are classified and multilabel strategies are used to classify concurrent faults. we explored, developed, and compared the performance of different types of binary (fault/non-fault), multi-class (fault type) and multi-label (single fault versus combination fault) classifiers. To evaluate the effectiveness of fault identification and classification, we used different supervised machine learning methods, including Random forest classification, support vector machine and neural network classification. Through experiment, we compared these methods over 4 classification regimes and finally summarize the most suitable machine learning algorithms for different aspects of health diagnosis in traction motors area.

Many lung diseases like Chronic Obstructive Pulmonary Disease (COPD) and Asthma are chronic diseases and worldwide considered as third main cause of death. COPD remains a forefront issue in global health, presenting significant challenges due to its complex nature and the imperative for precise classification to effectively guide treatment protocols. Traditional diagnostic methods, while effective, often come with limitations such as invasiveness, cost, and the requirement for specialized expertise. Recent advancements in machine learning (ML), particularly supervised ML algorithms, have shown promise in enhancing COPD classification through the analysis of clinical data, imaging, and physiological data. This review paper aims to systematically examine the current state of research on the application of supervised ML algorithms for the classification of COPD. We evaluate the effectiveness, accuracy, and clinical applicability of these algorithms by analyzing studies published in the last decade. Our review covers a range of supervised ML techniques, including K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), Extreme Gradient Boosting (EGB), emphasizing their effectiveness by employing evaluating metrics such as accuracy, specificity, AUC scores and sensitivity. The observations of this review indicate that supervised machine learning algorithms can significantly enhance the classification and prediction of COPD stages, offering prospects for customized treatment plans and enhanced patient prognosis.

In recent years, a significant amount of research has focused on analyzing the effectiveness of machine learning (ML) models for malware detection. These approaches have ranged from methods such as decision trees and clustering to more complex approaches like support vector machine (SVM) and deep neural networks. In particular, neural networks have proven to be very effective in detecting complex and advanced malware. This, however, comes with a caveat. Neural networks are notoriously complex. Therefore, the decisions that they make are often just accepted without questioning why the model made that specific decision. The black box characteristic of neural networks has challenged researchers to explore methods to explain black-box models such as SVM and neural networks and their decision-making process. Transparency and explainability give the experts and malware analysts assurance and trustworthiness about the ML models’ decisions. In addition, it helps in generating comprehensive reports that can be used to enhance cyber threat intelligence sharing. As such, this much needed analysis drives our work in this paper to explore the explainability and interpretability of ML models in the field of online malware detection. In this paper, we used the Shapley Additive exPlanations (SHAP) explainability technique to achieve efficient performance in interpreting the outcome of different ML models such as SVM Linear, SVM-RBF (Radial Basis Function), Random Forest (RF), Feed-Forward Neural Net (FFNN), and Convolutional Neural Network (CNN) models trained on an online malware dataset. To explain the output of these models, explainability techniques such as KernalSHAP, TreeSHAP, and DeepSHAP are applied to the obtained results.