Large language models (LLMs) increasingly mediate economic and organisational processes, from automated customer support and recruitment to investment advice and policy analysis. These systems are often assumed to embody rational decision making free from human error; yet they are trained on human language corpora that may embed cognitive and social biases. This study investigates whether advanced LLMs behave as rational agents or whether they reproduce human behavioural tendencies when faced with classic decision problems. Using two canonical experiments in behavioural economics, the ultimatum game and a gambling game, we elicit decisions from two state of the art models, Google Gemma7B and Qwen, under neutral and gender conditioned prompts. We estimate parameters of inequity aversion and loss-aversion and compare them with human benchmarks. The models display attenuated but persistent deviations from rationality, including moderate fairness concerns, mild loss aversion, and subtle gender conditioned differences.

Patients with semantic dementia (SD) present with remarkably consistent atrophy of neurons in the anterior temporal lobe and behavioural impairments, such as graded loss of category knowledge. While relearning of lost knowledge has been shown in acute brain injuries such as stroke, it has not been widely supported in chronic cognitive diseases such as SD. Previous research has shown that deep linear artificial neural networks exhibit stages of semantic learning akin to humans. Here, we use a deep linear network to test the hypothesis that relearning during disease progression rather than particular atrophy cause the specific behavioural patterns associated with SD. After training the network to generate the common semantic features of various hierarchically organised objects, neurons are successively deleted to mimic atrophy while retraining the model. The model with relearning and deleted neurons reproduced errors specific to SD, including prototyping errors and cross-category confusions. This suggests that relearning is necessary for artificial neural networks to reproduce the behavioural patterns associated with SD in the absence of \textit{output} non-linearities. Our results support a theory of SD progression that results from continuous relearning of lost information. Future research should revisit the role of relearning as a contributing factor to cognitive diseases.

The rise of the industrial metaverse has brought digital twins (DTs) to the forefront. Blockchain-powered non-fungible tokens (NFTs) offer a decentralized approach to creating and owning these cloneable DTs. However, the potential for unauthorized duplication, or counterfeiting, poses a significant threat to the security of NFT-DTs. Existing NFT clone detection methods often rely on static information like metadata and images, which can be easily manipulated. To address these limitations, we propose a novel deep-learning-based solution as a combination of an autoencoder and RNN-based classifier. This solution enables real-time pattern recognition to detect fake NFT-DTs. Additionally, we introduce the concept of dynamic metadata, providing a more reliable way to verify authenticity through AI-integrated smart contracts. By effectively identifying counterfeit DTs, our system contributes to strengthening the security of NFT-based assets in the metaverse.

Malnutrition and dehydration are strongly associated with increased cognitive and functional decline in people living with dementia (PLWD), as well as an increased rate of hospitalisations in comparison to their healthy counterparts. Extreme changes in eating and drinking behaviours can often lead to malnutrition and dehydration, accelerating the progression of cognitive and functional decline and resulting in a marked reduction in quality of life. Unfortunately, there are currently no established methods by which to objectively detect such changes. Here, we present the findings of an extensive quantitative analysis conducted on in-home monitoring data collected from 73 households of PLWD using Internet of Things technologies. The Coronavirus 2019 (COVID-19) pandemic has previously been shown to have dramatically altered the behavioural habits, particularly the eating and drinking habits, of PLWD. Using the COVID-19 pandemic as a natural experiment, we conducted linear mixed-effects modelling to examine changes in mean kitchen activity within a subset of 21 households of PLWD that were continuously monitored for 499 days. We report an observable increase in day-time kitchen activity and a significant decrease in night-time kitchen activity (t(147) = -2.90, p < 0.001). We further propose a novel analytical approach to detecting changes in behaviours of PLWD using Markov modelling applied to remote monitoring data as a proxy for behaviours that cannot be directly measured. Together, these results pave the way to introduce improvements into the monitoring of PLWD in naturalistic settings and for shifting from reactive to proactive care.

Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia which can negatively impact the Activities of Daily Living (ADL) and the independence of individuals. Detecting agitation episodes can assist in providing People Living with Dementia (PLWD) with early and timely interventions. Analysing agitation episodes will also help identify modifiable factors such as ambient temperature and sleep as possible components causing agitation in an individual. This preliminary study presents a supervised learning model to analyse the risk of agitation in PLWD using in-home monitoring data. The in-home monitoring data includes motion sensors, physiological measurements, and the use of kitchen appliances from 46 homes of PLWD between April 2019-June 2021. We apply a recurrent deep learning model to identify agitation episodes validated and recorded by a clinical monitoring team. We present the experiments to assess the efficacy of the proposed model. The proposed model achieves an average of 79.78% recall, 27.66% precision and 37.64% F1 scores when employing the optimal parameters, suggesting a good ability to recognise agitation events. We also discuss using machine learning models for analysing the behavioural patterns using continuous monitoring data and explore clinical applicability and the choices between sensitivity and specificity in-home monitoring applications.

If you own a business that relies heavily on machinery, and you're not doing any predictive maintenance yet, the chances are that you're suffering from downtime losses even while reading this. Carington Phahlamohlaka, Data Scientist at Altron Bytes Managed Solutions, says: "It's a real challenge to predict when you need to service your equipment and it's difficult to weigh the risks of lost productive time against those of a potential breakdown." This challenge is traditionally addressed in one of two ways: either reactively, by fixing the already existing failures, or proactively, where past experience is used to anticipate potential breakdowns. Unfortunately, neither of these approaches is effective enough. If you don't predict precisely when a machine or a piece of equipment is going to break down, the resulting downtime may be longer than anticipated, as you don't only need to replace a failed part, but you may also need to order it and ship it, and sometimes from overseas.

For the last few years, internet security has been based on a combination of anti-virus software, isolation techniques and encryption software. Government bodies and security companies would track traffic on the internet and look for suspicious materials based upon their signature. These techniques focused on running anti-malware software after the facts. They enabled the segregation between good data and malware. But if malware was undetected, it could lurk in the background of systems for months or even years and become active later in time. The consumer world is rapidly changing.

We live in a world where technology is taking over every business industry with its rapid services and hassle-free operations. Artificial Intelligence is one such technology, where its automated process and ability to integrate intelligence into operations is creating its global presence among each business industry. It has revolutionized a machine into a smart machine which can function as a human and achieve business goals efficiently. Artificial Intelligence aims at building such efficient machines and computers that can operate logically without the need for any human intervention. Its data-based algorithms and data processing enables automated processing and allows machine learning as well as deep learning to follow.

Artificial Intelligence (AI), machine learning, and predictive analytics are paving the way for intensive customer-centric data that can increase sales, generate leads, and enhance customer satisfaction. Big data has become a key driver for enterprises to enhance their sustainability in a competitive business world. With more data being produced and stored than ever before, the need for more efficient, effective, and precise processes has grown too. Predictive analytics is one such powerful process. Predictive analytics is the process of using data mining, statistics, and modelling to make predictions.

Banks and financial institutions invariably have to deal with large volumes of data in the form of customer information, transaction histories, monitoring, and reporting. Coming to think of it, it is no surprise that conventional data-processing applications are literally no good in managing such large volumes of data. This is precisely why Big-Data analytics, along with Artificial Intelligence technologies, is a necessity to allow financial institutions to function seamlessly. The banking system has evolved significantly over the last many years, and the adoption of Big-Data Analytics, Blockchain, and Artificial Intelligence is part of the most recent evolutionary trend. In this article, we look at how effective Big-Data processing and incorporation of Artificial Technology are in the banking sector, and how they've become a heightened necessity in the present day.