Medication Recommendation System Based on Natural Language Processing for Patient Emotion Analysis

The advent of extensive digital datasets coupled with advancements in artificial intelligence (AI) is revolutionizing our ability to extract meaningful insights from complex patterns in natural sciences. In this context, the targeted classification of textual descriptions, particularly those detailing the granulometry of unconsolidated sediments or the fracturing state of rock masses, combining supervised deep learning and natural language processing (NLP) is a promising method to refine large-scale geological and hydrogeological models by enriching them with increased data volume.Several databases are replete with qualitative geological data such as borehole logs, which, while abundant, are not readily assimilated into quantitative hydrogeological modeling due to the extensive time required to process the written descriptions into operationally significant units like hydrofacies. This conversion typically necessitates expert analysis of each report but can be expedited through the application of NLP techniques rooted in AI.The primary objectives of this research are twofold: (i) to develop a robust classification model that leverages geological descriptions alongside grain size data, and (ii) to standardize a vast array of sparse and heterogeneous stratigraphic log data for integration into large-scale hydrogeological applications.The Po River alluvial plain in northern Italy (45,700 km²) serves as the pilot area for this study due to the homogeneous shallow subsurface geology, the dense borehole coverage and the availability of a pre-labelled training set. This research demonstrates the conversion of qualitative geological information from a very large dataset of stratigraphic logs (encompassing 387,297 text descriptions from 39,265 boreholes), into a dataset of semi-quantitative information. This transformation, primed for hydrogeological modeling, is facilitated by an operational classification system using a deep learning-based NLP algorithm to categorize complex geological and lithostratigraphic text descriptions according to grain size-based hydrofacies. A supervised text classification algorithm, founded on a Long-Short Term Memory (LSTM) architecture was meticulously developed, trained and validated using 86,611 pre-labelled entries encompassing all sediment types within the study region. The word embedding technique enhanced the model accuracy and learning efficiency by quantifying the semantic distances among geological terms.The outcome of this work is a novel dataset of semi-quantitative hydrogeological information, boasting a classification model accuracy of 97.4%. This dataset was incorporated into expansive modeling frameworks, enabling the assignment of hydrogeological parameters based on grain size data, integrating the uncertainty stemming from misclassification. This has markedly increased the spatial density of available information from 0.34 data points/km² to 8.7 data points/km². The study findings align closely with the existing literature, offering a robust spatial reconstruction of hydrofacies at different scales. This has significant implications for groundwater research, particularly in the realm of quantitative modeling at a regional scale.

This study aims to elucidate the pivotal role of Artificial Intelligence (AI) in driving the transformation of precision medicine, comprehensively analyzing how it reshapes healthcare systems from traditional diagnosis and treatment paradigms into personalized health management ecosystems. A comprehensive narrative review was conducted to systematically synthesize and critically evaluate the innovative applications, paradigm shifts, and future prospects of AI across the entire precision medicine value chain. A comprehensive literature search was performed across multiple databases up to April 30, 2025, with a focus on the clinical implementation and breakthroughs of technologies such as deep learning (DL), machine learning (ML), and natural language processing (NLP). AI technologies have significantly enhanced the accuracy and efficiency of disease diagnosis through medical image analysis, genomics, and multimodal data fusion. At the treatment level, AI enables the development of personalized therapeutic plans and drug dosing optimization, while revolutionarily accelerating the drug development pipeline from discovery to clinical trials. Integrated with wearable devices and telemedicine platforms, AI facilitates full-cycle health monitoring. However, the clinical translation of AI faces challenges, including an uneven evidence base, insufficient model generalizability, and ethical concerns regarding data privacy, algorithmic fairness, and interpretability. AI is a key driver of paradigm shift in precision medicine. To address existing challenges, future efforts should focus on generating more robust clinical evidence, adopting technologies like federated learning to ensure data privacy, and promoting the human-centered, collaborative framework of Symbiotic AI (SAI). By establishing sound ethical and governance structures, the deployment of AI technologies can be ensured to be not only efficient and advanced but also equitable and trustworthy, ultimately paving the way for an intelligent and inclusive healthcare ecosystem.

People around the world with speech and hearing impairment use a media of communication known as ‘Sign language’. In recent times, Sign language is omnipresent. However, there exists a challenge for people who do not know sign language, to communicate with people who can communicate exclusively using sign language. This gap can be bridged by using technologies of recent times to recognize gestures and design intuitive systems with deep learning. The aim of this paper is to recognise American Sign Language gestures dynamically and create an intuitive system which provides sign language translation to text and speech of various languages. The system uses Convolutional neural network, natural language processing, language translation and text-to-speech algorithms. It is capable of recognizing hand gestures dynamically and predicting the corresponding letters to form a desired sentence accurately.

The primary focus of technical communication (TC) in the past decade has been the system-assisted generation and utilization of standardized, structured, and classified content for dynamic output solutions. Nowadays, machine learning (ML) approaches offer a new opportunity to integrate unstructured data into existing knowledge bases without the need to manually organize information into topic-based content enriched with semantic metadata. To make the field of artificial intelligence (AI) more accessible for technical writers and content managers, cloud-based machine learning as a service (MLaaS) solutions provide a starting point for domain-specific ML modelling while unloading the modelling process from extensive coding, data processing and storage demands. Therefore, information architects can focus on information extraction tasks and on prospects to include pre-existing knowledge from other systems into the ML modelling process. In this paper, the capability and performance of a cloud-based ML service, IBM Watson, are analysed to assess their value for semantic context analysis. The ML model is based on a supervised learning method and features deep learning (DL) and natural language processing (NLP) techniques. The subject of the analysis is a corpus of scientific publications on the 2019 Coronavirus disease. The analysis focuses on information extractions regarding preventive measures and effects of the pandemic on healthcare workers.

Artificial Intelligence (AI) is playing an increasingly vital role in the field of financial statement analysis. AI methodologies, such as Deep Learning, Machine Learning, and Natural Language Processing, are being employed to enhance the analysis of financial data. This study explores the applications of AI in financial statement analysis, including Predictive Analytics, Anomaly Detection, Trend Analysis, Financial Report Automation, Risk Evaluation and Control, Comparative Analysis and Benchmarking, Portfolio Optimization, Risk Assessment and Management, Fraud Detection and Forensic Analysis, and Regulatory Reporting and Compliance. The chapter includes case studies, industrial insights, and practical applications of AI in financial statement analysis, highlighting the tangible benefits and opportunities for businesses in the finance sector. Additionally, the chapter addresses the challenges and potential future applications of AI in this domain.

The communicative effects of disintermediation caused by social media promote the expansion of personalist and emotional political discourses that reach the audience directly and evade the traditional journalistic filter. This phenomenon leads to new political communication tactics, but also exposes citizens to potentially fraudulent, contaminated or polarised content. In this context, framed in post-truth, the term ‘fake news’ gains relevance as a way of referring to disinformation and as a political and performative argument that can be weaponised. This research aims to analyse such use in the discourse of the former president Donald Trump during his presidential term (2017-2021), focussing on Twitter as the main platform in his political communication strategy online. To analyse this, we resort to a methodological triangulation of content, discourse, and sentiment analysis, with the latter combining both lexicon and artificial intelligence (AI) techniques through machine learning on the basis of deep learning and natural language processing, which is applied to his messages published with the term ‘fake news’ (N = 768). The analysis of the sample, provided here in an open dataset, employs self-developed software that allows each unit of analysis to be filtered and coded around its predominant themes, sentiments, and words. The main results confirm that Trump’s attribution of ‘fake news’ focusses on three main topics: the media (53%), politics (40%) and his cabinet (33%). It also shows how the former president resorts to a personalist agenda, focussed on the defence of his proposals and his team (80%) by delegitimizing his opponents and the press, with a negative tone (72%) loaded with derogatory terms, confirming a weaponised strategy of the term ‘fake news’ as a political argument of disinformation and disintermediation.

The rapid evolution of financial technology (fintech) platforms has exponentially increased the volume and sophistication of financial transactions, concurrently elevating the risk and complexity of fraudulent activities. This necessitates a paradigm shift in fraud detection methodologies towards more agile, accurate, and predictive solutions. This paper presents a comprehensive study on the transformative potential of advanced Artificial Intelligence (AI) algorithms in enhancing fintech fraud detection mechanisms. By leveraging cutting-edge AI techniques including deep learning, machine learning, and natural language processing, this research aims to develop a robust fraud detection framework capable of identifying, analyzing, and preventing fraudulent transactions in real-time. Our methodology encompasses the deployment of several AI algorithms on extensive datasets comprising genuine and fraudulent financial transactions. Through a comparative analysis, we identify the most effective algorithms in terms of accuracy, efficiency, and scalability. Key findings reveal that deep learning models, particularly those employing neural networks, outperform traditional machine learning models in detecting complex and nuanced fraudulent activities. Furthermore, the integration of natural language processing enables the extraction and analysis of unstructured data, significantly enhancing the detection capabilities. Conclusively, this paper underscores the critical role of advanced AI algorithms in revolutionizing fintech fraud detection. It highlights the superior performance of AI-based models over conventional methods, offering fintech platforms a more dynamic and predictive approach to fraud prevention. This research not only contributes to the academic discourse on financial security but also provides practical insights for fintech companies striving to safeguard their operations against fraud. Keywords: Artificial Intelligence, Fintech, Fraud Detection, Ethical Ai, Regulatory Compliance, Data Privacy, Algorithmic Bias, Predictive Analytics, Blockchain Technology, Quantum Computing, Interdisciplinary Collaboration, Innovation, Transparency, Accountability, Continuous Learning, Ethical Principles, Real-Time Processing, Financial Sector.

The use of AI technologies in Smart Buildings is increasing as there are wide-scale benefits that can be derived from improving the efficiency of a building's operation and management. Buildings currently account for around 40% of global energy use, with AI technologies offering the opportunity to significantly reduce energy consumption through better automation, control, and reliability. These technologies can also be utilised to improve the safety and comfort of building occupants. This paper provides a literature review of research that has been conducted into AI technologies for use in Smart Buildings across the major AI topics including expert systems, fuzzy logic, genetic algorithms, machine learning, machine vision, natural language processing, neural networks, and pattern recognition. This process resulted in the determination that the volume of research conducted to-date is skewed towards some AI topics such as machine learning, neural networks and pattern recognition over other topics such as deep learning and natural language processing. It has also served to provide insights into which areas are likely to benefit the most from focusing future research efforts in that direction.

Cyberbullying has been a significant issue in online communities, posing threats to individuals' mental and social health. This paper examines the use of deep learning and natural language processing (NLP) methods for automated cyberbullying detection in text data. A complete preprocessing pipeline is applied, involving URL and mention removal, stop-word removal, and text normalization, to improve data quality for model training. For the detection issue, various architectures of deep learning are used, i.e., Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and hybrid models like CNN-LSTM and CNN-GRU. Tokenization and padding on the dataset ensure equal input length before training on the model. Evaluation on performance is done on accuracy, precision, recall, and F1-score with comparative analysis on all models. Experimental evidence reveals that hybrid models are more accurate than independent CNN and RNN architectures, with better classification accuracy and generalization. This highlights the potential of combining deep learning and NLP methods in building automated systems for cyberbullying identification. This work's findings can inform the development of AI-powered content moderation tools, leading to safer, more inclusive, and robust digital spaces.

Phishing attacks remain among the most regular cybersecurity threats since they exploit human weaknesses to penetrate systems unauthorized. Security procedures that exist today are unable to identify advanced cyberattacks effectively which leads to a requirement for updated protection measures. The combination of machine learning with deep learning and natural language processing methods within artificial intelligence creates an effective tool for detecting phishing threats. Algorithms based on artificial intelligence enable the analysis of large datasets for detecting anomalous email patterns and identifying dangerous URLs while recognizing unhonest website characteristics at a level of high precision. This research investigates how artificial intelligence (AI) detects phishing through its superior capability to identify threats in real time while improving accuracy by learning from experience and establishing fewer incorrect alerts. The text addresses main obstacles like adversarial attacks, data privacy challenges along with the requirement of continuous model maintenance. The incorporation of AI-based phishing detection systems into existing cyber security frameworks leads to defense improvements that lower both data breach possibilities and financial losses for organizations.

This chapter explores the transformative potential of generative models for advanced text generation, focusing on leveraging structural equation modeling techniques. With the rapid advancements in deep learning and natural language processing, generative models have emerged as powerful tools for creative writing, semantic coherence, and contextual understanding. This chapter provides a comprehensive overview of the foundations, methodologies, and applications of generative models in text generation. The chapter begins with an introduction to the evolution of generative models and highlights their significance in various domains. It lays the groundwork by explaining language modeling techniques and the architectures employed in text generation using deep learning algorithms. The subsequent sections delve into the core aspects of generative models for text generation.

Mobile Edge Computing (MEC) frameworks improve real-time data processing and system scalability by making networked game AI NPCs more responsive and flexible. MEC-based frameworks are tested for latency reduction and NPC real-time performance in complex and dynamic environments. Simulated and real-life user experiments evaluated the proposed system's response times, accuracy, and latency. Python simulations of network settings with different NPC concentrations and complexity produced massive datasets. NPC behaviour feedback was collected from 100 diverse users of various ages, genders, gaming experiences, and preferences. In low- to medium-density scenarios, the edge computing framework improved NPC responsiveness with low latency and high accuracy, enhancing player immersion. Due to the environment's complexity and NPC density, response times increased and accuracy decreased, requiring further optimisation for harsher conditions. Despite bugs and repetitive behaviours that suggested the Likert scale could be improved, the qualitative results praised the NPCs' lively conversation and realistic movements. Edge computing improves game AI and NPC realism with adaptive responses and real-time data processing. Scaling NPC densities and integrating edge computing with game architectures require more research. Next, improve NPC AI algorithms, reduce computational complexity and scalability, and expand testing environment game scenarios. Edge computing and AI techniques like deep learning and natural language processing can create immersive and engaging gaming experiences. This may present new gaming industry challenges and opportunities for innovation. Edge computing's real-time data processing and adaptive responses may change video game non-player characters.

The increasing complexity of higher education requires innovative approaches to improve student engagement and learning results. Conventional teaching approaches frequently lack real-time insights into student performance, resulting in ineffective interventions. This study presents a machine learning-based methodology for analysing both qualitative and quantitative student data, accurately predicting academic achievement. The suggested approach integrates deep learning and natural language processing, surpassing traditional techniques. The experimental findings indicate an accuracy of 94.5%, precision of 93.8%, recall of 94.2%, and an F1-score of 94.0%. The technology offers enhanced predictive capabilities compared to traditional models, delivering data-driven solutions for underperforming students. This research stands out itself from earlier studies by including qualitative insights with quantitative measures, hence enhancing the interpretability of learning analytics. The results encourage higher education institutions in creating AI-driven personalised learning paths, so enhancing retention rates and academic performance while addressing ethical issues in AI deployment.

Social media platforms have become one of the primary communication channels worldwide. Unfortunately, these platforms are also exploited for harmful activities, with cyber bullying being a significant concern, particularly among younger users. This research presents a novel model aimed at detecting instances of cyber bullying using deep learning techniques and Natural language processing (NLP). The research employs three datasets from Twitter, Instagram, and Facebook to predict cyber bullying behaviors through the Long Short-Term Memory (LSTM) algorithm. The findings demonstrate that the proposed model effectively identifies bullying instances, offering improvements over previous detection approaches. The model achieved accuracies of 96.64%, 94.49%, and 91.26% for the Twitter, Instagram, and Facebook datasets, respectively, highlighting its robustness across different social media platforms.

In the highly competitive e-commerce landscape, retaining customers and predicting their behaviors are critical for sustainable growth. This review explores the advancement of machine learning (ML) frameworks for customer retention and propensity modeling, emphasizing their transformative potential in e-commerce platforms. Customer retention is vital, as retaining an existing customer is significantly more cost-effective than acquiring a new one. Propensity modeling further complements retention strategies by predicting customer actions, such as purchases or churn, enabling businesses to tailor their marketing efforts effectively. Advanced ML techniques, including deep learning, reinforcement learning, and natural language processing (NLP), are reshaping how businesses approach these challenges. These models leverage diverse data sources, such as transaction history, browsing behavior, and customer feedback, to identify actionable insights. Key advancements in feature engineering, real-time data processing, and hyperparameter optimization have enhanced the accuracy and scalability of these frameworks, making them indispensable for e-commerce platforms handling vast datasets. Despite these advancements, challenges persist, such as ensuring data privacy, addressing the interpretability of complex models, and achieving real-time scalability. Successful implementations, including case studies from leading e-commerce platforms, demonstrate the potential of ML to improve customer engagement, increase lifetime value, and drive business growth. Looking forward, integrating AI and ML for enhanced personalization, leveraging real-time predictive analytics, and addressing ethical considerations like bias and fairness are crucial for advancing these frameworks. This review provides a comprehensive overview of state-of-the-art ML techniques for customer retention and propensity modeling, highlighting their practical applications and future prospects in e-commerce. By adopting these innovations, e-commerce platforms can achieve a competitive edge, fostering long-term customer loyalty and business success.