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A situation based predictive approach for cybersecurity intrusion detection and prevention using machine learning and deep learning algorithms in wireless sensor networks of industry 4.0
No full textText–image multimodal fusion model for enhanced fake news detection
No full textIn the era of rapid internet expansion and technological progress, discerning real from fake news poses a growing challenge, exposing users to potential misinformation. The existing literature primarily focuses on analyzing individual features in fake news, overlooking multimodal feature fusion recognition. Compared to single-modal approaches, multimodal fusion allows for a more comprehensive and enriched capture of information from different data modalities (such as text and images), thereby improving the performance and effectiveness of the model. This study proposes a model using multimodal fusion to identify fake news, aiming to curb misinformation. The framework integrates textual and visual information, using early fusion, joint fusion and late fusion strategies to combine them. The proposed framework processes textual and visual information through data cleaning and feature extraction before classification. Fake news classification is accomplished through a model, achieving accuracy of 85% and 90% in the Gossipcop and Fakeddit datasets, with F1-scores of 90% and 88%, showcasing its performance. The study presents outcomes across different training periods, demonstrating the effectiveness of multimodal fusion in combining text and image recognition for combating fake news. This research contributes significantly to addressing the critical issue of misinformation, emphasizing a comprehensive approach for detection accuracy enhancement
Goal-oriented practices in youth mental health and wellbeing settings:A scoping review and thematic analysis of empirical evidence
No full textINTRODUCTION: Goal-oriented practices involve practitioners working collaboratively with clients to identify, develop and focus on objectives for the therapeutic work. It has been suggested that the key mechanism underpinning goal-oriented practices with young people is the development of epistemic trust via the foundation of open communication, along with shared decision-making: including young people in decisions about their care. However, goal-oriented work in practice is variable in scope and content, with no research consensus on what it entails, the mechanisms of change and reported outcomes.METHOD: This research aims to map the extent, range and nature of the evidence-base for goal-oriented practices, including gaps, through a synthesis of the available empirical evidence from the past 20 years. A scoping review of 9783 studies published from 2003 onwards was conducted.RESULTS: In total, 116 studies were identified for inclusion in the review, focusing on goal-oriented practices specifically related to the mental health and wellbeing of children and young people aged 0-18 (including caregivers as relevant). Alongside presentation of the key elements of the included studies, three themes were developed relating to the features of mental health and wellbeing goal-oriented practice in the contexts of the studies: Conceptual and Empirical Constructs of Goal-Oriented Practices, Quality and Making 'Good' Goals, and The Socio-Cultural Contexts of Goal-Oriented Practice.CONCLUSION: Several areas for future research are identified that will build on this evidence, and further understanding in this area. Work towards the development of best practice principles will move practice towards transparency in the understanding and delivery of goal-oriented practices.</p
Using non-invasive brain stimulation to alleviate emotion dysregulation and aid smoking cessation: a concurrent tdcs-fmri study
No full textAcceptability of a Self-Help Programme to Address the Use of Indecent Images of Children
No full textThe use of indecent images of children (IIOC) is of continued concern and growing prevalence. A multi-agency approach to this online crime is necessary, as it cannot be eradicated by law enforcement alone. Previous research has examined the pathways to offending for this population, and prevention strategies that could be used to deter offending in the first instance, or to stop behaviour once it has begun. The current qualitative study aimed to explore acceptability of a prevention initiative; a self-help focussed service (a website and calls with a professional). Semi-structured interviews with eight individuals who had accessed IIOC and engaged in this self-help focussed service were conducted. Transcripts were analysed using qualitative framework approach, using the Theoretical Framework of Acceptability (TFA) domains as deductive codes. All seven domains of the TFA were identified in the transcripts, indicating the applicability of the framework. Overall, participants reported mixed acceptability of and satisfaction with the self-help programme and calls with practitioners, but high perceived effectiveness in the service supporting them stop their illegal behaviours. The implications of the study are considered in line with improvements that could be made to this existing self-help prevention initiative
What are the right configurations of just-in-time and just-in-case when supply chain shocks increase?
Get PDFThe use of drama in A Level chemistry: a study into the effects of simulation-role-play on the quality of learning of organic reaction mechanisms
No full textBackground: Much of the research into the use of drama in science education has been qualitative, with its primary focus being on its affective value amongst students aged 6–14 years, with a smaller fraction on effectiveness in terms of academic achievement and understanding of chemistry amongst older school students (16–18 years).Purpose: This article reports on a study that compared the effectiveness of drama, as a pedagogy, with a didactic practice examinations-questions-based approach on students’ understanding of chemistry organic reaction mechanisms as measured by examination question scores.Sample: Data were collected in seven 11–18 schools and a Further Education (FE) college in England, with a total of 236 students, aged 16–18 years, studying Advanced (A) Level chemistry. Each institution had two different classes studying chemistry concurrently.Design and methods: The research was a quasi-experimental intervention with one class of A Level chemistry students in each institution being taught an aspect of organic reaction mechanisms using drama and the second, non-drama, class being taught the same material using practice examination questions. Postintervention, all students completed previously unseen A Level examination questions and, in one phase of the study, a diagnostic question designed to probe deeper understanding. The responses were subject to statistical analysis.Results: In all cases there were no statistically significant differences between the test scores of the two groups for the answers to the examination questions. However, answers to a diagnostic question, probing deep level understanding, showed a statistically significant difference between the two groups in favour of the drama group.Conclusions: Whilst drama is at least as effective as the use of practice examination questions in the teaching and learning of organic reaction mechanisms it was statistically significantly more effective in terms of the development of deeper level conceptual understanding
Optimising air quality prediction in smart cities with hybrid particle swarm optimization‐long‐short term memory‐recurrent neural network model
Get PDFIn smart cities, air pollution is a critical issue that affects individual health and harms the environment. The air pollution prediction can supply important information to all relevant parties to take appropriate initiatives. Air quality prediction is a hot area of research. The existing research encounters several challenges that is, poor accuracy and incorrect real‐time updates. This research presents a hybrid model based on long‐short term memory (LSTM), recurrent neural network (RNN), and Curiosity‐based Motivation method. The proposed model extracts a feature set from the training dataset using an RNN layer and achieves sequencing learning by applying an LSTM layer. Also, to deal with the overfitting issues in LSTM, the proposed model utilises a dropout strategy. In the proposed model, input and recurrent connections can be dropped from activation and weight updates using the dropout regularisation approach, and it utilises a Curiosity‐based Motivation model to construct a novel motivational model, which helps in the reconstruction of long short‐term memory recurrent neural network. To minimise the prediction error, particle swarm optimisation is implemented to optimise the LSTM neural network's weights. The authors utilise an online Air Pollution Monitoring dataset from Salt Lake City, USA with five air quality indicators for comparison, that is, SO2, CO, O3, and NO2, to predict air quality. The proposed model is compared with existing Gradient Boosted Tree Regression, Existing LSTM, and Support Vector Machine based Regression Model. Experimental analysis shows that the proposed method has 0.0184 (Root Mean Square Error (RMSE)), 0.0082 (Mean Absolute Error), 2002*109 (Mean Absolute Percentage Error), and 0.122 (R2‐Score). The experimental findings demonstrate that the proposed LSTM model had RMSE performance in the prescribed dataset and statistically significant superior outcomes compared to existing methods
