Artificial Intelligence in Phishing Detection: Enhancing Cyber security

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.

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.

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 growing reliance on cloud platforms has introduced a heightened need for robust cybersecurity measures. Traditional security methods, while effective, struggle to keep pace with evolving cyber threats. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative tools in enhancing cloud security, offering dynamic and automated approaches to threat detection, prevention, and response. This paper explores the critical role of AI and ML in addressing key cybersecurity challenges within cloud environments. It reviews various AI/ML techniques such as supervised learning, deep learning, and reinforcement learning, demonstrating their effectiveness in identifying vulnerabilities and responding to sophisticated cyberattacks. Through a discussion of real-world case studies, the paper highlights the advantages of integrating AI/ML models in cloud security architectures. Additionally, the paper identifies existing limitations, such as adversarial attacks on AI systems and ethical concerns related to data privacy. Finally, it outlines future directions for leveraging AI/ML in creating proactive, adaptive, and secure cloud environments.

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 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.

ObjectiveThe present study tested the combination of an established and a validated food-choice research method (the ‘fake food buffet’) with a new food-matching technology to automate the data collection and analysis.DesignThe methodology combines fake-food image recognition using deep learning and food matching and standardization based on natural language processing. The former is specific because it uses a single deep learning network to perform both the segmentation and the classification at the pixel level of the image. To assess its performance, measures based on the standard pixel accuracy and Intersection over Union were applied. Food matching firstly describes each of the recognized food items in the image and then matches the food items with their compositional data, considering both their food names and their descriptors.ResultsThe final accuracy of the deep learning model trained on fake-food images acquired by 124 study participants and providing fifty-five food classes was 92·18 %, while the food matching was performed with a classification accuracy of 93 %.ConclusionsThe present findings are a step towards automating dietary assessment and food-choice research. The methodology outperforms other approaches in pixel accuracy, and since it is the first automatic solution for recognizing the images of fake foods, the results could be used as a baseline for possible future studies. As the approach enables a semi-automatic description of recognized food items (e.g. with respect to FoodEx2), these can be linked to any food composition database that applies the same classification and description system.

In the digital age, cyber threats have become increasingly sophisticated, necessitating innovative approaches to bolster security measures. Artificial Intelligence (AI) has emerged as a formidable tool in the realm of cyber security, offering advanced capabilities in threat detection, anomaly detection, and response automation. This article provides an overview of AI applications in cyber security, highlighting its role in mitigating risks and fortifying defense mechanisms. AI techniques such as machine learning, deep learning, and natural language processing empower security systems to analyze vast amounts of data in real-time, identifying patterns indicative of malicious activities. Through the utilization of AI-driven algorithms, cyber security platforms can proactively detect and neutralize cyber threats before they inflict substantial damage. Moreover, AI enables the automation of incident response processes, reducing response times and minimizing the impact of security breaches. Case studies from leading cyber security firms from the integral part of the studies and demonstrate the practical implementation of AI-driven solutions in safeguarding critical infrastructures against cyber threats. Resilience towards cyber-attacks and safeguarding sensitive data assets through leveraging AI technologies has been focused in the study.

Natural Language Processing (NLP) is an interdisciplinary field of computer science, artificial intelligence, and linguistics that focuses on the ability of computers to understand, process, generate, and simulate human language in order to achieve the ability to have natural conversations with humans. The underlying principles of natural language processing are at multiple levels, including linguistics, computer science, and statistics. It involves the study of language structure, semantics, grammar and pragmatics, as well as the statistical analysis and modeling of large-scale corpora. In the process of concrete implementation, it is necessary to process natural language at multiple levels. Based on this, this paper combined deep learning and natural language processing technology to conduct sentiment analysis on patients' comments, so as to recommend drugs that are more suitable for patients, thus achieving accurate drug prescribing and personalized recommendation.

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 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.

The value of text for small business default prediction: A Deep Learning approach

Traditional technical analysis methods face limitations in accurately predicting trends in today’s complex financial markets. Meanwhile, existing AI-driven approaches, while powerful in processing large datasets, often lack interpretability due to their black-box nature. This paper presents ElliottAgents, a multi-agent system that combines the Elliott wave principle with LLMs, showcasing the application of deep reinforcement learning (DRL) and natural language processing (NLP) in financial analysis. By integrating retrieval-augmented generation (RAG) and deep reinforcement learning (DRL), the system processes vast amounts of market data to identify Elliott wave patterns and generate actionable insights. The system employs a coordinated team of specialized agents, each responsible for specific aspects of analysis, from pattern recognition to investment strategy formulation. We tested ElliottAgents on both stock and cryptocurrency markets, evaluating its effectiveness in pattern identification and trend prediction across different time scales. Our experimental results demonstrate improvements in prediction accuracy when combining classical technical analysis with AI-driven approaches, particularly when enhanced by DRL-based backtesting process. This research contributes to the advancement of financial technology by introducing a scalable, interpretable framework that enhances market analysis capabilities, offering a promising new methodology for both practitioners and researchers.

Text summary has become essential in an era of information overload from various sources, including social media, news articles, emails, and text messages. Using the most recent developments in Deep Learning and Natural Language Processing, this project builds a model that can efficiently summarize large amounts of text into clear, concise summaries that improve comprehension and save time. The project is on abstractive text summarization using seq2seq and self-attention mechanism models like BART, PEGASUS, and T5 from Transformers and LSTM in Convolution Neural Networks. Utilizing the auto-regressive and bidirectional properties of the BART encoder-decoder framework, the proposed system design extensively integrates it to enhance summarization performance. The model is ready for sequential data processing after the code implementation, including text pretreatment and tokenization. Sophisticated optimization techniques are applied to improve model performance, and strategies like padding are employed to ensure consistent input sequence lengths. Testing outcomes validate the efficacy of BART's attention mechanisms, demonstrating the significance of the research as a breakthrough in precise and context-aware text summarization