− MEDIA & ARTS The article is devoted to a comprehensive analysis of modern problems of protecting musical works in the cont…+ HEALTH Rationale and objectives To propose a new imaging biomarker, the pulmonary artery-to-vein volume difference (…± HEALTH Rationale and objectives The international application of artificial intelligence (AI) for lesion detection b…± HEALTH Rationale and objectives To address the challenge of preoperative prediction of synchronous liver metastasis…± HEALTH Objectives To conduct a nationwide survey among professionals working in oncology departments in China to inv…+ HEALTH The utilization of deep convolutional neural networks for the purpose of diagnosing diseases in the skin area…± HEALTH BACKGROUND . Gadolinium-based contrast agents remain essential for MRI but carry risks. Deep learning (DL) me…+ SCIENCE Gas Chromatography is a versatile separation technique widely used in analytical chemistry for constituent de…
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Evidence-backed gain

Forensic analysis of dominant versus non-dominant handwriting: Statistical and machine learning insights

Systematic, paired quantitative evidence on how handwriting changes when the non-dominant hand is used remains limited in forensic document examination, despite its frequent relevance in cases involving disguise and authorship concealment. This study presents the first large-scale within-writer analysis examining general and individual handwriting characteristics using statistical testing and predictive modeling. Handwriting samples were collected from 94 right-handed participants, each of whom produced the same…

Journal of Forensic Sciences · Crime

Forensic analysis of dominant versus non-dominant handwriting: Statistical and machine learning insights
Hybrid ensemble machine learning algorithms for landscape ecological vulnerability assessment to riverbank erosion
Evidence-backed gain

Hybrid ensemble machine learning algorithms for landscape ecological vulnerability assessment to riverbank erosion

Riverbank erosion is a catastrophic geomorphological hazard that poses severe ecological and socio-economic challenges across densely populated floodplains. This study advances a machine learning (ML) framework that integrates individual and bagging-classifier approaches using random forest (RF), multilayer perceptron (MLP) and bagging classifiers to assess landscape ecological vulnerability (LEV) to riverbank erosion. The site-specific environmental, climatic, geomorphological and ecological parameters were sel…

Crime
Deepfake Anthony Albanese used in celebrity scams duping Australians out of $7.4m, Asic warns
Evidence-backed problem

Deepfake Anthony Albanese used in celebrity scams duping Australians out of $7.4m, Asic warns

There has been a steep rise in scammers luring victims into phoney investment opportunities using deepfakes of celebrities and politicians, Australia’s corporate watchdog has warned. And Anthony Albanese is the figure most commonly co-opted. Real footage of the prime minister, overlaid with fake audio promising Australians can invest $4,000 to earn $40,000 a month, appears in one video online. “This is not just another scam product,” the deepfake Albanese says, falsely describing it as an “official platform” wit…

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A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions
Both readings

A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions

Given the continually rising frequency of cyberattacks, the adoption of artificial intelligence methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL), has become essential in the realm of cybersecurity. These techniques have proven to be effective in detecting and mitigating cyberattacks, which can cause significant harm to individuals, organizations, and even countries. Machine learning algorithms use statistical methods to identify patterns and anomalies in large data…

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A clinically validated framework for auditing AI chatbot behavior in mental health interactions
Both readings

A clinically validated framework for auditing AI chatbot behavior in mental health interactions

Millions of users turn to consumer artificial intelligence chatbots to discuss emotional, behavioral and mental-health concerns, creating an urgent need for rigorous and scalable safety evaluations. Here we introduce simulated (SIM) vulnerability-amplifying interaction loops (VAILs) (SIM-VAIL), a clinically validated framework for auditing chatbot behavior in mental-health contexts. SIM-VAIL simulates users with specific psychiatric vulnerabilities and conversational intents, engages them in multi-turn conversat…

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Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the-art techniques and future paradigms

Abstract The integration of artificial intelligence (AI) and machine learning (ML) into cybersecurity has driven a transformational shift, significantly enhancing the ability to detect, respond to, and mitigate complex cyber threats. Traditional defense mechanisms are increasingly inadequate against sophisticated attacks, necessitating the adoption of AI-driven security solutions. This review paper presents a novel, in-depth analysis of state-of-the-art AI and ML techniques applied to intrusion detection, malwar…

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Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the-art techniques and future paradigms

Applications of AI-Based Models for Online Fraud Detection and Analysis

Abstract Background Fraud is a prevalent offence that extends beyond financial loss, impacting victims emotionally, psychologically, and physically. Advances in online communication technologies continue to create new opportunities for fraud, and fraudsters increasingly using these channels for deception. With the progression of technologies like Generative Artificial Intelligence (GenAI), there is a growing concern that fraud will increase in scale using these advanced methods, with offenders employing deep-fak…

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Applications of AI-Based Models for Online Fraud Detection and Analysis

Next-Generation Machine Learning in Healthcare Fraud Detection: Current Trends, Challenges, and Future Research Directions

The growing complexity and size of healthcare systems have rendered fraud detection increasingly challenging; however, the current literature lacks a holistic view of the latest machine learning (ML) techniques with practical implementation concerns. The present study addresses this gap by highlighting the importance of machine learning (ML) in preventing and mitigating healthcare fraud, evaluating recent advancements, investigating implementation barriers, and exploring future research dimensions. To further ad…

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Next-Generation Machine Learning in Healthcare Fraud Detection: Current Trends, Challenges, and Future Research Directions

The Erosion of Cybersecurity Zero-Trust Principles Through Generative AI: A Survey on the Challenges and Future Directions

Generative artificial intelligence (AI) and persistent empirical gaps are reshaping the cyber threat landscape faster than Zero-Trust Architecture (ZTA) research can respond. We reviewed 10 recent ZTA surveys and 136 primary studies (2022–2024) and found that 98% provided only partial or no real-world validation, leaving several core controls largely untested. Our critique, therefore, proceeds on two axes: first, mainstream ZTA research is empirically under-powered and operationally unproven; second, generative-…

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The Erosion of Cybersecurity Zero-Trust Principles Through Generative AI: A Survey on the Challenges and Future Directions

An Introduction to Machine Learning Methods for Fraud Detection

Financial fraud represents a critical global challenge with substantial economic and social consequences. This comprehensive review synthesizes the current knowledge on machine learning approaches for financial fraud detection, examining their effectiveness across diverse fraud scenarios. We analyze various fraud types, including credit card fraud, financial statement fraud, insurance fraud, and money laundering, along with their specific detection challenges. The review outlines supervised, unsupervised, and hy…

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An Introduction to Machine Learning Methods for Fraud Detection

LLMs for Cybersecurity in the Big Data Era: A Comprehensive Review of Applications, Challenges, and Future Directions

This paper presents a systematic review of research (2020–2025) on the role of Large Language Models (LLMs) in cybersecurity, with emphasis on their integration into Big Data infrastructures. Based on a curated corpus of 235 peer-reviewed studies, this review synthesizes evidence across multiple domains to evaluate how models such as GPT-4, BERT, and domain-specific variants support threat detection, incident response, vulnerability assessment, and cyber threat intelligence. The findings confirm that LLMs, parti…

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LLMs for Cybersecurity in the Big Data Era: A Comprehensive Review of Applications, Challenges, and Future Directions

Leveraging transfer learning with deep learning for crime prediction

Crime remains a crucial concern regarding ensuring a safe and secure environment for the public. Numerous efforts have been made to predict crime, emphasizing the importance of employing deep learning approaches for precise predictions. However, sufficient crime data and resources for training state-of-the-art deep learning-based crime prediction systems pose a challenge. To address this issue, this study adopts the transfer learning paradigm. Moreover, this study fine-tunes state-of-the-art statistical and deep…

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Leveraging transfer learning with deep learning for crime prediction

CYBERSECURITY CHALLENGES IN THE ERA OF AI

Artificial Intelligence (AI) and cyber security, the environment has been transformed. Using technology, more elaborate cyber-attacks can be carried out, and automated, predictive defensive systems can be implemented. The more traditional and archaic forms of cyber security are becoming increasingly ineffective in the face of cyber threats and malware powered by AI. Automated phishing attacks, deepfake identity fraud, and machine learning adversarial attacks and breaches are just a few of the threats posed by th…

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CYBERSECURITY CHALLENGES IN THE ERA OF AI