± HEALTH Adequate self-harm surveillance is a key part of suicide prevention. Our previous research demonstrated that…± HEALTH Breast cancer remains the most commonly diagnosed malignancy among women globally, with disproportionately hi…+ HEALTH Background The prevalence of depression is higher among older adults with chronic diseases and cognitive impa…± HEALTH Purpose To compare the information quality, accuracy, and readability of patient-directed responses generated…± HEALTH Objective Comparative evaluations of machine learning (ML) and logistic regression (LR) for clinical predicti…± HEALTH Introduction Alzheimer's disease (AD) is a progressive neurodegenerative disorder for which early diagnosis-p…+ HEALTH Background Accurate identification of ganglionated bowel is essential during laparoscopic pull-through for Hi…+ CLIMATE Accurate prediction of ash fusion temperatures (AFTs) is crucial for ensuring the operational efficiency and…
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Sadiq Khan agrees to have his texts and emails searched in Palantir legal battle

Sadiq Khan has agreed for his text messages and emails to be searched as part of a legal battle with Palantir, the high court has been told, after he prevented the US technology company from working with the Metropolitan police. The London mayor stepped in to block a £50m deal between Palantir and the Met in May, with the artificial intelligence developer suing Khan’s office over the decision. Legal representatives for the mayor said they “did not originally consider it necessary” for Khan to be among those to h…

The Guardian · Crime

Sadiq Khan agrees to have his texts and emails searched in Palantir legal battle
SnSe/SnO2 Heterojunction-Based Single-Sensor Virtual Electronic Nose with Low Reaction Barrier for Trace Identification of Volatile Sulfur Compounds toward Illicit Methamphetamine Trafficking Traceability
Evidence-backed gain

SnSe/SnO2 Heterojunction-Based Single-Sensor Virtual Electronic Nose with Low Reaction Barrier for Trace Identification of Volatile Sulfur Compounds toward Illicit Methamphetamine Trafficking Traceability

Illicit methamphetamine (MA, ice) trafficking poses a severe global threat to public security, while non-contact on-site detection of MA remains a grand challenge due to its ultra-low saturated vapor pressure at room temperature (25 °C). Volatile sulfur compounds (VSCs), including hydrogen sulfide (H2S), methanethiol (CH3SH), and dimethyl sulfide (C2H6S, DMS), are stable characteristic markers released throughout the entire MA production, purification, storage, and transportation chain. Herein, we develop a sing…

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Mapping agricultural fragility in India through a yield gap vulnerability framework: a national-scale machine learning assessment
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Mapping agricultural fragility in India through a yield gap vulnerability framework: a national-scale machine learning assessment

In India, regional yield gaps continue to widen despite increased agricultural productivity, owing to socioeconomic inequality and climate variability. This study develops a novel Yield Gap Vulnerability (YGV) framework to measure agricultural fragility at the district-level by integrating agricultural, hydrological, meteorological, and socioeconomic indicators to observed yield gaps for major cereals (rice and wheat) and nutri-crops (maize and millet). An integrated Machine Learning (ML) approach is used that c…

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

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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…

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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

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

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

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