AbstractMental health disorders have a global prevalence of 25%, according to the WHO, and this is exacerbated by factors such as stigma, geographical location, and a worldwide shortage of practitioners. Mental health chatbots have been developed to address these barriers, but these systems lack key features such as emotion recognition, personalisation, multilingual support, and ethical appropriateness. This paper introduces an innovative mental health support system that integrates BERT‐based emotional distress detection with a psychological first aid (PFA)‐generative pre‐trained transformer (PFA‐GPT) model, providing an emotion‐aware PFA chatbot. The methodology leverages deep learning models, utilising bidirectional encoder representations from transformers (BERT) for emotional distress detection and fine‐tuning GPT‐3.5 on therapy transcripts for PFA chatbot development. The findings demonstrate BERT's superior accuracy (93%) for emotional distress detection compared to bidirectional long short‐term memory. The multilingual PFA chatbot developed using the PFA‐GPT model demonstrated superior BERT scores (exceeding 83%) and proficiently provided ethical PFA. A proof of concept has been developed to illustrate the integration of the emotional distress detection model with the novel generative conversational agent for PFA. This integrated approach holds significant potential in overcoming existing barriers to mental health support and has the potential to transform mental health support, offering timely and accessible care through AI‐powered psychological interventions.

AbstractBrain networks provided powerful tools for the analysis and diagnosis of epilepsy. This paper performed a pairwise comparative analysis on the brain networks of Benign Childhood Epilepsy with Centrotemporal Spikes (BECTS): spike group (spike), non‐spike group (non‐spike), and control group (control). In this study, fragments with and without interictal spikes in electroencephalograms of 13 BECTS children during non‐rapid eye movement sleep stage I (NREMI) were selected to construct dynamic brain function networks to explore the functional connectivity (FC). Graph theory and statistical analysis were exploited to investigate changes in FC across different brain regions in different frequency bands. From this study, we can draw the following conclusions: (1) Both spike and non‐spike have lower energy in each brain region on the γ band. (2) With the increase of the frequency band, the FC strength of spike, non‐spike and control groups are all weakened. (3) Spikes are correlated with brain network efficiency and the small‐world property. (4) Spikes increase the FC of temporal, parietal and occipital regions except in the γ band and the absence of spikes weakens the FC of the entire brain region.

AbstractGarbage management is exceptionally critical and poses enormous environmental challenges. It has always been a vital issue in municipal corporations. However, municipal agencies have developed and used garbage management systems. Garbage forecasting still plays a crucial role in the management system and helps improve or create a garbage management system. This research examines the information from 212 cities to suggest a helpful regression model for garbage forecasting and control. To establish a connection between the variables, the descriptive study employs statistical techniques to learn about the composition of data collected from municipal corporations and conduct correlation analysis. Population and garbage depend highly on one another, as evidenced by their correlation coefficient of 0.922,144. The primary research is used to build an alternate hypothesis that shows the chosen variables are highly dependent on one another. The dataset is scaled and divided into a training and testing 80:20 ratio during the pre‐processing data phase. This research aims to do a regression analysis with daily garbage production, urban area, and population as independent variables. This research initiates a variety of regression models, including multiple linear regression (MLR), artificial neural network (ANN), decision tree regression (DTR), and random forest regression (RFR). The MLR model's R2 value of 0.85 indicates that it has the potential to accurately forecast daily garbage production based on just two independent variables and a single dependent variable. Random Forest Regression (RFR) with (MSE: 100,078.749 & MAE: 182.212) shows that it has the lowest MSE among all the models, which provides the most accurate predictions on average and the fit values of 8.85 and 316.23 obtained from the error distribution with a bin value 25. The estimated results from each model are compared to the test data values on line graphs and Taylor plots. The mean square error and the mean absolute error in the analysis and the Taylor plot show that the RFR model is best suited for predicting daily garbage production in a city. This research, therefore, provides a Random Forest model that is optimal for such challenges and is recommended for this class of problem.

AbstractHealth insurance plays a vital role during medical emergencies in the coverage against medical expenses. Insurance fraud is an international challenge that affects most economies worldwide. Government and private companies offer many insurance schemes. The successful implementation of numerous health insurance programs offered for the public by and large are often threatened by corruption, fraud, and numerous other data‐related issues. Further the procedure for acclaiming the insurance money is not only critical in terms of verification of claims but tedious and time consuming also. To help redress these problems, blockchain technology can be utilised as is it offers improved security, transparency, auditability, privacy, accountability along with many other advantages. The goal is to create and implement a blockchain‐based solution for efficient functioning of insurance system and to prevent such health insurance systems from going bankrupt. The authors have proposed an insurance claim model, MedBlockSure using blockchain architecture for creating interoperability between the insurer, the hospital and the insurance company. The model will aid in maintaining transparency between the insurer and the company while eliminating the requirement of middlemen or agents. The conceptual view of the proposed system using sequence and use case diagrams and data management framework and smart claim processing system is demonstrated.

AbstractLow‐light object detection is needed for ensuring security, enabling surveillance, and enhancing safety in diverse applications, including autonomous vehicles, surveillance systems, and search and rescue operations. A comprehensive study on low‐light object detection is presented using state‐of‐the‐art you only look once (YOLO) models, including YOLOv3, YOLOv5, YOLOv6, and YOLOv8, aiming to enhance detection performance under challenging low‐light conditions. The ExDark dataset is a dataset that consists of adequate low‐light images, modified to simulate realistic low‐light scenarios, and employed for evaluation. The deep learning algorithm optimises YOLO's architecture for low‐light detection by adapting the network structure and training strategies while preserving the algorithm's integrity. The experimental results show that YOLOv8 consistently outperforms baseline models, achieving significant improvements in accuracy and robustness in low‐light scenarios. The deep learning algorithm that acquired the best score, YOLOv8s, had a mean average precision score of 0.5513. This work contributes to the field of low‐light object detection, offering promising solutions for real‐world applications like nighttime surveillance and autonomous navigation in low‐light conditions, addressing the growing demand for advanced low‐light object detection.

AbstractThe firing of spatial cells in the entorhinal‐hippocampal structure is believed to enable the formation of a cognitive map for the environment. Inspired by the spatial cognitive mechanism of the rat's brain, the authors proposed a real‐time cognitive map construction method based on the entorhinal‐hippocampal working mechanism. Firstly, based on the physiological properties of the rat's brain, the authors constructed an entorhinal‐hippocampal CA3 neurocomputational model for path integration. Then, the transformation relationship between the cell plate and the real environment is used to solve the robot's position. Path integration inevitably generates cumulative errors, which require loop‐closure detection and pose optimisation to eliminate errors. To solve the problem that the RatSLAM algorithm is slow in pose optimisation, the authors proposed a pose optimisation method based on a multi‐layer CA1 place cell to improve the speed of pose optimisation. To validate the method, the authors designed simulation experiments, dataset experiments, and physical experiments. The experimental results showed that compared to other brain‐like SLAM algorithms, the authors’ method possesses outstanding performance in path integration accuracy and map construction speed. As a result, the authors’ method can endow mobile robots with the ability to quickly and accurately construct cognitive maps in complex and unknown environments.

AbstractMixed sentiment classification (MSC) technology has a significant research value and application potential in understanding and analysing sentimental interactions. In the process of identifying and analysing complex sentiments, it is still necessary to overcome the difficulties of multi‐dimensional sentiment recognition and improve sensitivity to subtle sentimental differences. Therefore, a multi‐modal fusion attention sentiment analysis based on MSC to address this challenge is proposed. Firstly, the sentiment analysis fusion strategy based on multi‐modal fusion is studied, which can fully utilise the information of multi‐modal inputs such as text, audio, and video, thereby gaining a more comprehensive understanding and recognition of sentiments. Secondly, a sentiment analysis model based on multi‐modal fusion attention is constructed, which focuses on the key information of multi‐modal inputs to achieve an accurate recognition of mixed sentiments. The experimental results show that the proposed method outperforms existing sentiment analysis methods on both datasets, with F1 values of 83.17 and 84.19, accuracy of 39.15 and 39.98, and errors of 0.516 and 0.524, respectively. The accuracy range is 95.38%–99.89%, verifying the superiority of the method in sentiment analysis. It can be seen that this method provides a more effective and reliable MSC solution, which has practical significance for improving the accuracy and recall of sentiment analysis.