Control Methods in Hyperbolic PDEs

Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neuro-fuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems.

This study compared machine learning (ML) and deep learning (DL) techniques for credit card fraud detection.We evaluated 16 combinations of ML algorithms and cross-validation methods across diverse datasets.The Random Forest classifier with repeated K-fold cross-validation achieved the highest accuracy 99.0% and F1 score 99.1% among all models.The top performing deep learning model, the Artificial Neural Network (ANN), achieved an accuracy of 91.3% and F1 score of 91.1%, while a hybrid model combining these approaches reached 98.9% accuracy and F1 score.The Random Forest Classifier continued to be the best option.Our findings suggest the Random Forest classifier with repeated K-fold crossvalidation, tested against a 21 combinations of other machine learning models, deep learning models, and a hybrid model as the most reliable method for credit card fraud detection in balanced datasets, offering valuable insights for enhancing security precautions and financial system defense against various banking sector frauds.

In so far as everyone needs food, farming helps to provide for this basic requirement. Despite making up only 15.4% of the country's GDP, the agriculture sector is crucial to India since it creates a large number of job possibilities. In addition to providing useful information, the use of machine learning is improving farming by preventing or minimizing losses. Machine learning includes situations in which a machine may learn and make choices based on predetermined criteria. It may be classed as an area of artificial intelligence (AI). Artificial Neyral Networks (ANN), one of hottest artificial intelligence approaches of the present day, are extended by deep learning, a popular and contemporary data analysis tool in the last ten years. As the world's population rises, agriculture becomes more and more significant.This article discusses modern machine learning applications in agriculture and how they address issues before during, and after agricultural production.Numerous deep learning and machine learning methods, databases performances were reviewed in the publications. They grouped the approaches they looked at according to the most popular techniques, the kinds of data used, the performance metrics, and the results of these analyses.To detect tomato leaf diseases, machine learning techniques and image processing are used, albe it their accuracy is lower. Initially, tomato leaf diseases are automatically identified utilizing traditional image processing and machine learning techniques, yet the accuracy is lower.

ObjectivePituitary adenomas are the most common type of pituitary disorders, which usually occur in young adults and often affect the patient’s physical development, labor capacity and fertility. Clinical free texts noted in electronic medical records (EMRs) of pituitary adenomas patients contain abundant diagnosis and treatment information. However, this information has not been well utilized because of the challenge to extract information from unstructured clinical texts. This study aims to enable machines to intelligently process clinical information, and automatically extract clinical named entity for pituitary adenomas from Chinese EMRs.MethodsThe clinical corpus used in this study was from one pituitary adenomas neurosurgery treatment center of a 3A hospital in China. Four types of fine-grained texts of clinical records were selected, which included notes from present illness, past medical history, case characteristics and family history of 500 pituitary adenoma inpatients. The dictionary-based matching, conditional random fields (CRF), bidirectional long short-term memory with CRF (BiLSTM-CRF), and bidirectional encoder representations from transformers with BiLSTM-CRF (BERT-BiLSTM-CRF) were used to extract clinical entities from a Chinese EMRs corpus. A comprehensive dictionary was constructed based on open source vocabularies and a domain dictionary for pituitary adenomas to conduct the dictionary-based matching method. We selected features such as part of speech, radical, document type, and the position of characters to train the CRF-based model. Random character embeddings and the character embeddings pretrained by BERT were used respectively as the input features for the BiLSTM-CRF model and the BERT-BiLSTM-CRF model. Both strict metric and relaxed metric were used to evaluate the performance of these methods.ResultsExperimental results demonstrated that the deep learning and other machine learning methods were able to automatically extract clinical named entities, including symptoms, body regions, diseases, family histories, surgeries, medications, and disease courses of pituitary adenomas from Chinese EMRs. With regard to overall performance, BERT-BiLSTM-CRF has the highest strict F1 value of 91.27% and the highest relaxed F1 value of 95.57% respectively. Additional evaluations showed that BERT-BiLSTM-CRF performed best in almost all entity recognition except surgery and disease course. BiLSTM-CRF performed best in disease course entity recognition, and performed as well as the CRF model for part of speech, radical and document type features, with both strict and relaxed F1 value reaching 96.48%. The CRF model with part of speech, radical and document type features performed best in surgery entity recognition with relaxed F1 value of 95.29%.ConclusionsIn this study, we conducted four entity recognition methods for pituitary adenomas based on Chinese EMRs. It demonstrates that the deep learning methods can effectively extract various types of clinical entities with satisfying performance. This study contributed to the clinical named entity extraction from Chinese neurosurgical EMRs. The findings could also assist in information extraction in other Chinese medical texts.

This paper presents a meshfree collocation method that uses deep learning to determine the basis functions as well as their corresponding weights. This method is shown to be able to approximate elliptic, parabolic, and hyperbolic partial differential equations for both forced and unforced systems, as well as linear and nonlinear partial differential equations. By training a homogeneous network and particular network separately, new forcing functions are able to be approximated quickly without the burden of retraining the full network. The network is demonstrated on several numerical examples including a nonlinear elasticity problem. In addition to providing meshfree approximations to strong form partial differential equations directly, this technique could also provide a foundation for deep learning methods to be used as preconditioners to traditional methods, where the deep learning method will get close to a solution and traditional solvers can finish the solution.

<p class="0abstract">The research aimed to conduct an extensive study of machine learning and deep learning methods in cybersecurity. To accomplish the objectives, the research carried out a qualitative study based on secondary data collection to review the available studies and literature. The research has examined three machine learning methods and three deep learning methods to study the most popular techniques used in cybersecurity. During the research, the working mechanism of each method was studied along with their strengths and weaknesses. Also, a comparative discussion has been made to examine the most effective method for cybersecurity. Limitations in the current literature were also identified, and future direction is also given to target and develop the weak areas of machine learning and deep learning methods.<em> </em><strong></strong></p>

Cardiovascular diseases (CVDs) remain a leading cause of global mortality, underscoring the urgent need for early and accurate diagnosis. Electrocardiograms (ECGs) are a widely used, noninvasive tool for detecting cardiac abnormalities, but manual interpretation can be time- consuming and prone to error. This study explores advanced machine learning (ML) and deep learning (DL) methods to improve the detection of CVDs from ECG images, leveraging their ability to analyze complex patterns with high precision. Models such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and ensemble frameworks are utilized, alongside traditional ML models like XGBoost, CatBoost, and LightGBM. The performance of these models is evaluated based on precision, recall, F1-score, and accuracy. Results show that DL models, particularly an ensemble of CNN and ViT, achieve superior accuracy , outperforming standalone ML methods. Furthermore, an ensemble of XGBoost, CatBoost, and LightGBM achieves competitive results, while Optuna-tuned XGBoost further enhances ML performance. The findings highlight the potential of combining ML and DL techniques for robust and reliable CVD detection in ECG images. This research demonstrates the promise of AI-driven solutions in advancing medical diagnostics and supports the integration of automated systems for early detection of cardiovascular diseases.

: Complex physical phenomena involving chemically reacting systems or the transport of heat or fluids are often modeled by coupled systems of time-dependent, nonlinear partial differential equations. The difficulties in understanding the stability of the differential equation systems and in designing efficient, accurate numerical methods for their solution are widely recognized and were the focus of this research. We have worked on four general aspects of the analysis and numerical approximation of systems of partial differential equations. These areas of research are: (1) modeling aspects and stability analysis for nonlinear time-dependent partial differential equations; (2) use and analysis of finite element or finite difference methods to discretize coupled systems of nonlinear differential equations; (3) development of adaptive or local grid refinement capabilities to resolve local phenomena in large-scale applications; and (4) development of data structures, preconditioners, and efficient solution algorithms for large-scale problems on new computer architectures. Emphasis has been placed upon multiphase or multicomponent, transport-dominated flow processes with dynamic local phenomena. The research also involved a mix of analysis, algorithm development, and large-scale computation using newer computer architectures.

This study aims to investigate the topics of customer satisfaction and complaints and explore their role in review sentiment prediction by using deep learning and interpretable machine learning methods. Specifically, this study first uses a BERT-based method for topic clustering and embedding, a factor analysis for topic feature extraction and dimensionality reduction and an oversampling method for the imbalance problem. Further, five types of machine learning methods are utilised to forecast review sentiment. To improve the interpretability of the machine learning model, we employ an interpretable framework to explore the role of topic features in review sentiment prediction. The results indicate that customers’ satisfaction mainly focuses on perfect conference facilities and delicious Chinese cuisine, while customers mainly complain about the quality of service, the attitude of staff and the accessibility of the hotel. In addition, ‘air quality and traffic’ is the most important factor influencing the sentiment in reviews. These findings suggest that hotel managers should focus more on the accommodation experience of customers rather than the value-added experience.

The COVID-19 disease is one of the most severe health disasters encountered in the world recently. COVID-19 disease is a highly fatal disease that spreads quickly through the respiratory tract. The most accurate method for diagnosing COVID-19 disease is Reverse Transcription Polymerase Chain Reaction (RT-PCR). Although this method gives more precise results, it is extremely costly in terms of cost and processing time. To overcome this issue, many studies are being carried out, particularly on diagnosis via lung images. Deep learning, artificial intelligence, and machine learning methods are applied in image enhancement studies, and powerful results are obtained. Within the scope of this study, texture analysis methods, which are part of the image enhancement methods, are examined. Among these methods, the local binary pattern (LBP) method has a special place. Due to the extremely high success rate of the LBP method, many successful derivative methods have been derived from this method. The paper includes the developed quadstar local binary pattern (QS-LBP) method derived from the standard LBP method. Moreover, machine learning (ML) algorithms are used to process the features extracted from two different models using the QS-LBP method. Extremely high analysis values have been obtained from the hybrid method consisting of the developed models and ML methods, accuracy is 99.21%, kappa is 96.2%, ROC area is 99.8%, F-measure is 99.6 %, precision is 99.5%, and finally recall is 99.8%, respectively. Using the hybrid technique suggested in the paper, diagnosing COVID-19 disease in lung images has been carried out exceedingly successfully.

The concept of passwords predates computers by a significant amount. They served as a way to verify the authenticity or identify a person. Passwords, such as email addresses, social media login credentials, and Internet banking information, are commonly used to secure private information in the modern world. Brute-forcing is one of these methods, which uses a powerful computer system to search through all possible combinations of alphanumeric characters to crack the password. Steganography and cryptography are regarded as a robust Privacy protection solution. On the other hand, steganography typically deals with text concealment in passwords. If the steganography password is robust, brute force may not work since the strong password may be discovered over several months or years. However, consumers create weak passwords due to inappropriate password policy rules set up by password strength meters. In this work, we use deep learning and machine learning methods, such as decision trees and logistic regression, Xgboost, Multilayer Perceptron (MLP), and Keras model, to categorize the steganography password in any one of the three categories (weakest, average, and most robust). This allows us to calculate the steganography password strength. Additionally, we have used explainable AI to interpret the models’ strengths and weaknesses. The algorithm with the highest accuracy, precision, recall, and f-measure scores has been the best. These were the model performance results: 81.9% for Logistic Regression, 81.2% for Decision Tree, 99.7% for Xgboost, 98.2% for MLP, and 99.7% for Keras. Compared to the other models, Xgboost and Keras performed better.

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has 350 images. Three fully connected (FC) layers were utilized for feature extraction, namely fc6, fc7, and fc8. The classifiers employed were support vector machine (SVM), k-nearest neighbors (KNN), and Naive Bayes. The study demonstrated that the most effective feature extraction layer was fc6, achieving an accuracy of 90.7% with SVM. SVM outperformed KNN and Naive Bayes, exhibiting an accuracy of 90.7%, sensitivity of 83.5%, specificity of 93.7%, and F1-score of 83.5%. This research successfully addressed the challenges in classifying cassava species by leveraging deep learning and machine learning methods, specifically with SVM and the fc6 layer of AlexNet. The proposed approach holds promise for enhancing plant classification techniques, benefiting researchers, farmers, and environmentalists in plant species identification, ecosystem monitoring, and agricultural management.

Multi-objective parameter optimization of large-scale offshore wind Turbine's tower based on data-driven model with deep learning and machine learning methods

Lung ground-glass nodules (GGNs) represent a critical early imaging manifestation of lung adenocarcinoma, and exploring the relationship between their CT imaging features and oncogenic driver genes holds significant promise for precision diagnosis and personalized treatment. In recent years, artificial intelligence (AI) technologies, particularly deep learning and machine learning methods, have demonstrated remarkable potential in the integrative analysis of radiomic and genomic data. This review summarizes the current advances in AI applications for extracting CT imaging features of lung GGNs, identifying oncogenic driver genes, and analyzing their correlations. Key AI-driven techniques enabling the construction of a bridge between imaging phenotypes and genetic alterations are discussed, alongside challenges such as data heterogeneity, limited annotated datasets, and interpretability. Future research directions emphasize the development of robust, explainable AI models and multi-omics integration to enhance early lung cancer diagnosis and therapeutic strategies. By providing a comprehensive overview of the intersection between AI, radiomics, and genomics in lung GGN adenocarcinoma, this article aims to offer theoretical insights and technical references to advance early detection and precision oncology.

Frauds are notoriously challenging to detect because of their constant change and lack of trends.Fraudsters take advantage of recent technological advancements.They somehow manage to get over security procedures, which results in millions of dollars being lost.Analyzing and identifying unusual activity with the aid of data mining technologies is one way to locate fraudulent transactions.transactions.This paper evaluates a number of deep learning and machine learning methods, such as auto encoders, convolutional neural networks, restricting Boltzmann machines, and deep belief networks.All three databases-the German, Australian, and European-will be used.ROC curve, Matthews Correlation Coefficient, & area under the curve measure.