Blockchain-Powered NLP and ML Framework for Reliable Event Identification and Trust Validation

The development of computational data science techniques in natural language processing (NLP) and machine learning (ML) algorithms to analyze large and complex textual information opens new avenues to study intricate policy processes at a scale unimaginable even a few years ago. We apply these scalable NLP and ML techniques to analyze the United States Government’s regulation of the banking and financial services sector. First, we employ NLP techniques to convert the text of financial regulation laws into feature vectors and infer representative “topics” across all the laws. Second, we apply ML algorithms to the feature vectors to predict various attributes of each law, focusing on the amount of authority delegated to regulators. Lastly, we compare the power of alternative models in predicting regulators’ discretion to oversee financial markets. These methods allow us to efficiently process large amounts of documents and represent the text of the laws in feature vectors, taking into account words, phrases, syntax, and semantics. The vectors can be paired with predefined policy features, thereby enabling us to build better predictive measures of financial sector regulation. The analysis offers policymakers and the business community alike a tool to automatically score policy features of financial regulation laws to and measure their impact on market performance.

In this chapter, a brief overview of the role and applications of machine learning (ML) algorithms in future wireless cellular networks is presented, more specifically, in the context of self-organizing networks (SONs). SON is a promising and innovative concept, in which future networks are expected to analyze and use historical data in order to improve and adapt themselves to the network conditions. For this to be possible, however, algorithms that are capable of extracting patterns from data and learn from previous actions are necessary. This chapter highlights the utilization and possible applications of ML algorithms in future cellular networks. A brief introduction of ML and SON is presented, followed by an analysis of current state of the art solutions involving ML in SON. Lastly, guidelines on the utilization of intelligent algorithms in SON and future research trends in the area are highlighted and conclusions are drawn.

Hematology research has greatly benefited from the integration of diverse biological data resources and advanced machine learning (ML) frameworks. This integration has not only deepened our understanding of blood diseases such as leukemia and lymphoma, but also enhanced diagnostic accuracy and personalized treatment strategies. By applying ML algorithms to analyze large-scale biological data, researchers can more effectively identify disease patterns, predict treatment responses, and provide new perspectives for the diagnosis and treatment of hematologic disorders. Here, we provide an overview of the current landscape of biological data resources and the application of ML frameworks pertinent to hematology research.

In the era of digitalization, electronic gadgets such as Google Translate, Siri, and Alexa have at least one characteristic: They are all the products of natural language processing (NLP). “Natural Language” refers to a human language used for daily communication, such as English, Hindi, Bengali, etc. Natural languages, as opposed to artificial languages such as computer languages and mathematical nomenclature, have evolved as they have been transmitted from generation to generation and are challenging to explain with clear limits in the first instance. In natural language processing, artificial intelligence (Singh et al., 2021), linguistics, information processing, and cognitive science are all related fields (NLP). NLP aims to use intelligent computer techniques to process human language. However, NLP technologies such as voice recognition, language comprehension, and machine translation exist. With such limited obvious exclusions, machine learning algorithms in NLP sometimes lacked sufficient capacity to consume massive amounts of training data. In addition, the algorithms, techniques, and infrastructural facilities lack enough strength. Humans design features in traditional machine learning, and feature engineering is a limitation that requires significant human expertise. Simultaneously, the accompanying superficial algorithms lack depiction capability and, as a result, the ability to generate layers of duplicatable concepts that would naturally separate intricate aspects in forming visible linguistic data. Deep learning overcomes the challenges mentioned earlier by using deep, layered modelling architectures, often using neural networks and the corresponding full-stack learning methods. Deep learning has recently enhanced natural language processing by using artificial neural networks based on biological brain systems and Backpropagation. Deep learning approaches that use several processing layers to develop hierarchy data representations have produced cutting-edge results in various areas. This chapter introduces natural language processing (NLP) as an AI component. The history of NLP is next. Distributed language representations are the core of NLP's profound learning revolution. After the survey, the boundaries of deep learning for NLP are investigated. The paper proposes five NLP scientific fields.

Creating Better Disclosure Norms through Machine Learning Algorithms

Machine Learning (ML) and Deep Learning (DL) techniques have achieved remarkable results in many domains resulting in excellent performances in fields including Natural Language Processing (NLP), Computer Vision (CV) and Speech Recognition (SR). In light of these achievements, the application of ML algorithms to the industry world is appealing and very interesting results have already been produced by the scientific community. However, industrial data are often significantly different from those commonly employed in the disciplines where DL has had a larger impact. Industrial datasets are often limited in size and are constituted by large amounts of unlabelled data. In contrast, in CV and NLP, large deep neural networks can benefit from large labelled datasets, that can thus be used to train DL models in a fully-supervised fashion. In this work, we adopt a self-supervised approach to process heterogeneous dataset characterized by a relatively large number of unlabelled data and few labelled ones. We apply the proposed approach to two industrial time-series data and show that leveraging unlabelled data results in improved performance on classification downstream tasks.

Machine learning algorithms are designed to identify efficiently and to predict accurately patterns within multivariate data. They provide analysts computational tools to aid predictive modelling and the interpretation of interactions between data and the phenomena under investigation. The analysis of large volumes of disparate multivariate geospatial data using machine learning algorithms therefore offers great promise to industry and research in the geosciences. Geoscience data are frequently characterised by a restriction in the number and distribution of direct observations, irreducible noise in these data and a high degree of intraclass variability and interclass similarity. The choice of machine learning algorithm, or algorithms and the details of how algorithms are applied must therefore be appropriate to the context of geoscience data. With this knowledge, I aim to employ machine learning as a means of understanding the spatial distribution of complex geological phenomena. I conduct a rigorous and comprehensive comparison of machine learning algorithms, representing the five general machine learning strategies, for supervised lithology classification applications. I also develop and test a novel method for obtaining robust estimates of the uncertainty associated with machine learning algorithm categorical predictions. The insights gained from these experiments leads to the further development and comparison of new methods for the incorporation of spatial-contextual information into machine learning supervised classifiers. In using machine learning algorithms for geoscience applications, I have developed bestpractice methodologies that address the challenges facing geoscientists for geospatial supervised classification. Guidelines are established that detail the preparation and integration of disparate spatial data, the optimisation of trained classifiers for a given application and the robust statistical and spatial evaluation of outputs. I demonstrate, through a case study in a region that is prospective for economic mineralisation, the combination of supervised and unsupervised machine learning algorithms for the critical appraisal of pre-existing geological maps and formulation of meaningful interpretations of geological phenomena. The experiments conducted as part of my research confirm the efficacy of machine learning algorithms to generate accurate geological maps representing a variety of terranes. I identify and explore key aspects of the spatial and statistical istributions of geoscience data that affect machine learning algorithm performance. My research clearly identifies Random Forests‚Äöv묢 as a good first-choice algorithm for the prediction of classes representing lithologies using commonly available multivariate geological and geophysical data. Furthermore, Random Forests prediction uncertainty is shown to be closely related to ambiguous and/or erroneous classifications and, thus provides a practical means of indicating variable levels of confidence. Spatial-contextual information is best incorporated into machine learning supervised classifiers via the pre-processing of input variables and/or the post-regularisation of classifications. My findings indicate that a trade-off between optimal predictive models and interpretable explanatory models exists, whereby, intuitively interpretable models are not necessarily the most accurate. The practical application of machine learning algorithms requires the implementation of three key stages: (1) data pre-processing; (2) algorithm training; and (3) prediction evaluation. This methodology provides the foundation for generating accurate and geologically meaningful predictions with minimal user intervention and assists in the formulation of robust interpretations of complex geological phenomena. For example, classifications obtained by Random Forests are useful for critically appraising interpreted geological maps. Clusters produced by Self-Organising Maps indicate the presence of discrete, spatially contiguous and geologically significant sub-classes within individual lithological units, which represent regions of contrasting primary composition and alteration styles. My results may be widely applied to a broad range of practical geoscience challenges such as ore deposit targeting, geo-hazard risk assessment, engineering and construction projects, hydrological and environmental modelling and ecological studies. The applications of machine learning algorithms detailed in this thesis align well with state-of-the-art Big Data online infrastructure and virtual laboratories currently emerging in Australia.

ObjectiveEnhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice.MethodsResearches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized.ResultsBased on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics.ConclusionResearchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.

Spam detection is an evolving issue in line with the increasing volume of data and the evolution of spam techniques. In recent years, the application of machine learning (ML) algorithms has become an effective solution to enhance the accuracy and efficiency of spam detection systems. This study aims to analyze various machine learning algorithms applied in spam detection systems through a literature review. Several popular algorithms used in spam detection include Naive Bayes, Support Vector Machine (SVM), Neural Network, Recurrent Neural Network (RNN), and Transformer-based models. Each algorithm has its strengths and weaknesses that affect its performance in handling spam detection issues, depending on the characteristics of the data and the application requirements. Based on the data obtained, the Naive Bayes algorithm achieved 88% accuracy, 85% precision, 90% recall, and 87% F1-score. In contrast, SVM showed higher results with 93% accuracy, 92% precision, 94% recall, and 93% F1-score. Neural Network reached 96% accuracy, 95% precision, 97% recall, and 96% F1-score, while Recurrent Neural Network (RNN) achieved 95% accuracy, 94% precision, 96% recall, and 95% F1-score. Transformer-based models provided the best results with 97% accuracy, 96% precision, 98% recall, and 97% F1-score. This study adopts a literature analysis method by reviewing various articles and research that discuss the application of these algorithms in spam detection. In conclusion, the selection of the appropriate algorithm should be adjusted to the characteristics of the dataset, the complexity of the problem, and the availability of computational resources, as each algorithm has its own strengths and weaknesses in the context of spam detection.

With the evolution of fifth generation (5G) network technologies, network maintenance strategies have become increasingly complex, necessitating the use of predictive analysis enabled by Machine Learning (ML) algorithms. This paper emphasizes exploring how ML algorithms can further enhance predictive maintenance in 5G and future networks. It reviews the current literature on this interdisciplinary topic, identifying key ML models such as Decision Trees, Neural Networks, and Support Vector Machines, and discussing their benefits and limitations. Special attention is given to the methodologies in applying these models, handling of data stages, and the training process. Major challenges in implementing ML in the context of network maintenance, such as data privacy, data gathering, model training, and generalizability, are discussed. Furthermore, the research aims to go beyond predicting maintenance needs to introduce a proactive approach in improving overall network performance and pre-empting potential issues based on ML predictions. The paper also discusses possible future trends including advancements in ML algorithms, Automated Machine Learning (AutoML), Explainable AI, and others. The objective is to provide a comprehensive understanding of the current ML-based predictive maintenance field and outline possibilities for future research. The study finds that the application of ML algorithms continues to show promise in transforming the landscape of network management by improving predictive maintenance and proactive performance enhancement strategies. It remains a challenging yet important area in the context of 5G networks.

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

In Mild Cognitive Impairment (MCI), identifying a high risk of conversion to Alzheimer’s Disease Dementia (AD) is a primary goal for patient management. Machine Learning (ML) algorithms are widely employed to pursue data-driven diagnostic and prognostic goals. An agreement on the stability of these algorithms –when applied to different biomarkers and other conditions– is far from being reached. In this study, we compared the different prognostic performances of three supervised ML algorithms fed with multimodal biomarkers of MCI subjects obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Random Forest, Gradient Boosting, and eXtreme Gradient Boosting algorithms predict MCI conversion to AD. They can also be simultaneously employed –with the voting procedure– to improve predictivity. AD prediction accuracy is influenced by the nature of the data (i.e., neuropsychological test scores, cerebrospinal fluid AD-related proteins and APOE ε4, cerebral structural MRI (sMRI) data). In our study, independent of the applied ML algorithms, sMRI data showed the lowest accuracy (0.79) compared to other classes. Multimodal data were helpful in the algorithms’ performances by combining clinical and biological measures. Accordingly, using the three ML algorithms, the highest accuracy (0.90) was reached by employing neuropsychological and AD-related biomarkers. Finally, the feature selection procedure indicated that the most critical variables in the respective classes were the ADAS-Cog-13 scale, the medial temporal lobe and hippocampus atrophy, and the ratio between phosphorylated Tau and Aβ42 proteins. In conclusion, our data support the notion that using multiple ML algorithms and multimodal biomarkers helps make more accurate and solid predictions.

Machine learning is one of the predominant methods used in computer science and has been widely and successfully applied in many areas such as computer vision, pattern recognition, natural language processing, cyber security etc. In cyber security, the application of machine learning algorithms for network intrusion detection system (NIDS) has seen promising results for anomaly detection mostly with the adoption of deep learning and is still growing. However, machine learning algorithms are vulnerable to adversarial attacks resulting in significant performance degradation. Adversarial attacks are security threats that aim to deceive the learning algorithm by manipulating its predictions, and Adversarial machine learning is a research area that studies both the generation and defense of such attacks. Researchers have extensively worked on the adversarial machine learning in computer vision but not many works in Intrusion detection system. However, failure in this critical Intrusion detection area could compromise the security of an entire system, and need much attention. This paper provides a review of the advancement in adversarial machine learning based intrusion detection and explores the various defense techniques applied against. Finally discuss their limitations for future research direction in this emerging area.KeywordsAdversarial attackCyber securityIntrusion detectionMachine learningDeep learningPoisoning attackEvasion attack

Three-dimensional (3D) printing has become a widely used manufacturing technology, but predicting the mechanical and physical properties of printed parts remains a critical challenge. In particular, tensile strength, hardness, and surface roughness are essential for assessing product quality and reliability. Addressing this gap requires advanced computational approaches. Machine Learning (ML) algorithms have the potential to enhance automation and provide accurate predictions of product quality in 3D printing. The primary objective of this study is to review, classify, and analyze recent research on the application of ML algorithms for predicting mechanical (tensile strength, hardness) and physical (surface roughness) properties in 3D-printed materials. This review aims to identify current research trends, highlight existing gaps in the literature, and propose potential directions for future investigations in this rapidly evolving interdisciplinary field. For this purpose, a literature review was conducted in the Web of Science database using keywords such as “3D printing”, “machine learning”, “tensile strength”, “hardness”, and “roughness. The review systematically examines the material types, ML algorithms, printing parameters, and testing methods reported in the literature, and the frequency of these parameters is visualized through graphs to illustrate research trends. The findings reveal that ML algorithms achieve high accuracy in predicting tensile strength, hardness, and surface roughness, providing valuable insights for process optimization. However, current research still shows limited evidence for direct improvements in efficiency and error reduction, indicating important directions for future work.

The screening and diagnosis of osteoporosis in patients with type 2 diabetes mellitus (T2DM) based on bone mineral density remains challenging because of the limited availability and accessibility of dual-energy X-ray absorptiometry. We aimed to develop and validate models to predict the risk of osteoporosis in postmenopausal women with T2DM based on machine learning (ML) algorithms. This retrospective study included 303 postmenopausal women with T2DM. To develop prediction models for osteoporosis, we applied nine ML algorithms combined with demographic, clinical, and laboratory data. The least absolute shrinkage and selection operator were used to perform feature selection. We used the bootstrap resampling technique for model training and validation. To test the performance of the models, we calculated indices including the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, calibration curve, and decision curve analysis. Furthermore, we conducted fivefold cross-validation for parameter optimization and model validation. Feature importance was assessed using the SHapley additive explanation (SHAP). We identified 10 independent predictors as the most valuable features. An AUROC of 0.616-1.000 was observed for nine ML algorithms. The extreme gradient boosting (XGBoost) model exhibited the best performance, outperforming conventional risk assessment tools and registering 0.993 in the training set, 0.798 in the validation set, and 0.786 in the test set for fivefold cross-validation. Using SHAP, we found that the explanatory variables contributed to the model and their relationship with osteoporosis occurrence. Furthermore, we developed a user-friendly tool for calculating the risk of osteoporosis. With the integration of demographic and clinical risk factors, ML algorithms can accurately predict osteoporosis. The XGBoost model showed ideal performance. With the incorporation of these models in the clinic, patients may benefit from early osteoporosis diagnosis and treatment.