Cryptocurrency Price Prediction using a Hybrid Deep Learning Approach with Explainable AI Integration

Cryptocurrency markets exhibit pronounced nonlinearity and abrupt regime shifts, making accurate price forecasting a formidable challenge. Conventional econometric models such as ARIMA and GARCH often fail to capture these complex dynamics under high-volatility conditions. In response, we propose a hybrid deep learning framework, a ConvLSTM-GRU pipeline, that combines one-dimensional convolutions for localized pattern extraction. LSTM layers for long-term dependency modelling, and GRU layers to selectively refine salient temporal features. Technical indicators (e.g., moving averages, Bollinger Bands, and RSI) and trading volume are integrated as input channels to enrich the feature space with signals that capture volatility and momentum. We then employ Keras Tuner's Hyperband algorithm to search over convolutional filter counts, recurrent unit sizes, dense layer widths, dropout rates, and learning rates, optimizing the network for minimal validation mean squared error. On a chronological split of BTCUSDT daily closing prices from August 2017 to July 2025, the tuned ConvLSTM-GRU achieves an R2 of 0.9922 on the held-out test set, outperforming standalone LSTM, GRU, CNN, and traditional machine learning baselines. Furthermore, we applied SHAP with ConvLSTM-GRU to improve decision-making transparency and trustworthiness

Scalable Resource Allocation for Cloud IaaS Using Energy Valley Algorithm

Cloud computing stands as the dominant model which provides adjustable computing resources that automatically respond to user needs. Efficient resource allocation in cloud systems needs sophisticated algorithms to manage dynamic workloads with reduced power usage while achieving maximum resource utilization. This research work examines the performance of nature-inspired metaheuristic algorithms for Infrastructure-as-a-Service (IaaS) cloud resource allocation using the Energy Valley Optimizer (EVO). The study conducts its experiments through CloudSim which provides a powerful cloud simulation system to develop practical cloud computing systems. Two experimental settings consist of 5 virtual machines (VMs) and 10 VMs under varying workload sizes from 100 to 500 tasks to 1000–5000 tasks. Performance assessment includes execution duration together with power consumption measurements in kilowatt-hours (KWh) units and resource utilization percentage. The obtained results demonstrate that EVO provide superior performance through EVO’s 15% shorter execution time and 20–25% lower power usage than Symbiotic Organisms Search (SOS) and Particle Swarm Optimization (PSO) and Cuckoo Search (CSA)

No Digital Omnibus! Why Europe Owes LMICs the Ethical Regulation of Generative AI in Healthcare

AI tools hold vast promises to reduce health inequities in LMICs, yet these geographies remain heavily dependent on the technological infrastructures and governance norms from high-income regions. This paper highlights that tech-driven world building, coupled with the lack of firm AI regulation in the US and China, has led to unregulated AI technologies being implemented in healthcare systems and practices in LMICs, often with negative effects. Europe has been seeking to reign in the unfettered powers of digital technology companies with its recent regulations, namely the EU AI Act, Digital Services Act, Digital Markets Act, and the proposed Digital Fairness Act. We argue that these ‘gold standard’ regulations could work as a multilateralist spine for LMICs to unlock the potential of generative AI safely and ethically. However, we also warn that Europe’s recent proposal of a ‘Digital Omnibus’ package will potentially eradicate the so-called ‘Brussels effect’. We therefore urge the EU to carefully consider the weakening of their previously set regulations in the name of competitiveness as this will have an impact on LMICs too. Finally, we make recommendations on how the EU can support the additional transfer of regulatory blueprints to LMICs

Optimizing UCS Prediction Models through XAI-Based Feature Selection in Soil Stabilization

Unconfined Compressive Strength (UCS) is a key parameter for the assessment of the stability and performance of stabilized soils, yet traditional laboratory testing is both time and resource intensive. In this study, an interpretable machine learning approach to UCS prediction is presented, pairing five models (Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), CatBoost, and K-Nearest Neighbors (KNN)) with SHapley Additive exPlanations (SHAP) for enhanced interpretability and to guide feature removal. A complete dataset of 12 geotechnical and chemical parameters, i.e., Atterberg limits, compaction properties, stabilizer chemistry, dosage, curing time, was used to train and test the models. R2, RMSE, MSE, and MAE were used to assess performance. Initial results with all 12 features indicated that boosting-based models (GB, XGB, CatBoost) exhibited the highest predictive accuracy (R2 = 0.93) with satisfactory generalization on test data, followed by RF and KNN. SHAP analysis consistently picked CaO content, curing time, stabilizer dosage, and compaction parameters as the most important features, aligning with established soil stabilization mechanisms. Models were then re-trained on the top 8 and top 5 SHAP-ranked features. Interestingly, GB, XGB, and CatBoost maintained comparable accuracy with reduced input sets, while RF was moderately sensitive and KNN was somewhat better owing to reduced dimensionality. The findings confirm that feature reduction through SHAP enables cost-effective UCS prediction through the reduction of laboratory test requirements without significant accuracy loss. The suggested hybrid approach offers an explainable, interpretable, and cost-effective tool for geotechnical engineering practice

Risk and protective factors for self-harm, suicidal thoughts, and suicide attempts in LGBTQ+ young people in the Republic of Ireland: the XXXX study (blinded for review)

Title and Background: Self-harm, suicidal thoughts, and suicide attempts (hereafter SH, ST, and SA) are increasingly prevalent among young people in Ireland and there is some evidence that Lesbian, Gay, Bisexual, Transgender, Queer (LGBTQ+) young people may experience particularly high rates. There are also gaps in our understanding of the risk and protective correlates of SH, SA, and ST in this population. Aim and objectives: This study aimed to provide an account of prevalence and the risk and protective correlates of SH, ST, and SA in LGBTQ+ young people in the Republic of Ireland (RoI). Methods: Data from a national survey of 1,191 LGBTQ+ young people aged between 14 and 25 years were analysed. This self-report epidemiological survey assessed lifetime incidence of SH, ST, and suicide attempt. Information on sociodemographic characteristics, mental health (including depression, anxiety, and stress), alcohol and/or drug use, self-esteem and resilience, supportive family and friends, in-person abuse, online abuse and LGBTQ+ bullying in school was collected. Following ethical approval, data were collected between in 2022 via an online survey. Findings: The prevalence of lifetime SH, ST and SA were 68.5% (n=758/1106), 75.5% (n=831/1100) and 32.6% (n=339/1041) respectively. Some gendered differences were identified, with transgender and cisgender-women reporting higher rates of SH compared to cisgender-men. Similarly, participants who reported physical and neurodevelopmental disabilities had comparatively higher odds of SH, ST and SA compared to those without. In general, anxiety and depression, but not stress were associated with increased odds of any suicide-related outcome explored. A lack of support of one’s LGBTQ+ identity from immediate family was further found to increase risk. Conclusions and impact: These findings highlight high rates of SH, ST, and SA experienced by LGBTQ+ young people. Several risk correlates were identified including gender, anxiety and depression, drug use, and abuse. Although many of these factors may be regarded as ‘universal determinants’ of suicidality, these risks may be qualitatively different for LGBTQ+ young people. Support of one’s gender or sexual identity is a protective factor. A combination of universal and tailored supports could help LGBTQ+ young people experiencing SH, ST and suicide attempt

Classification decision model in high dimensional microarray cancer data using Grey Wolf optimizer by feature subset selection

Microarray technique deceives with a tiny chip carrying thousands of genetic instructions. Microarray DNA technique allowed simultaneous gene evaluation. Pattern recognition methods are typically employed in feature selection data to compare health and cancer patient sample data. Multidimensional gene expression comprises mismatched, noisy, and redundant genes. This has been the biggest challenge for machine learning techniques. Because it disrupts the testing and training process and affects the effectiveness of the classification. To overcome these deficiencies, the gene (or) feature is very important. In this article, we use Grey Wolf Optimization (GWO) technique for choosing feature subset selection in high-dimensional microarray cancer data. The cardinality of feature subsets and the distinguishing capacity of such selected subsets are two competing objectives which are needed to be addressed to replicate the objective functions. Designed an objective function which is effective addresses these issues and considers five benchmark datasets for evaluating the proposed system’s efficiency. The results determine the proposed system efficiency, which can be traced out the gene space search and are also ready to locate better gene selection. The proposed approach is reporting a better classification accuracy score than other existing approaches like GA, NSGA, PSO, etc. The score was 96.55% for the Naive Bayes classifier and 100% for other classifiers on the Colon dataset. For the Leukemia dataset, 97.35% is the highest classification accuracy. Similarly, 100% for Lymphoma data, 93.33% for the WDBC dataset, and 93.48% for the DLBCL dataset

DIGITAL4Business: Mastering Advanced Digital Skills for European Business Transformation

The DIGITAL4Business project is a major European initiative aimed at addressing the advanced digital skills gap by delivering an innovative Master’s programme in digital competencies tailored to the demands of the European industry. This paper presents the DIGITAL4Business ’ structure, objectives, Master’s curriculum design, and findings from a comprehensive needs analysis that identifies critical skills requirements in cybersecurity, artificial intelligence, cloud computing, and data science. Developed by a consortium of academic and industry partners, the 60-ECTS-credit programme is formally titled Master’s Degree in Advanced Digital Technologies for Business and is jointly delivered by four leading European higher education institutions. It provides European businesses with a digitally proficient workforce capable of driving digital transformation across various sectors. Designed for broad accessibility with pathways for diverse learners to engage in cutting-edge Education 5.0, the flexible Joint Master’s curriculum comprises 14 modules and is accredited at European level. Key recommendations and future directions aim to optimise the impact of the programme on Europe’s digital transformation, with a focus on sustained adaptability to emerging technologies and collaborative partnerships

Gendered risks to children and adolescents assessed by Child & Adolescent Mental Health Services (CAMHS): Perspectives from network analysis

Background: Early exposure to risk and adversity is a potent predictor of mental health difficulties. Though risks vary by gender, little attention was paid towards the associations both within risks and of risks across genders. Objectives: We sought to identify networks of a wider range of risks (experiences and behaviors that might threaten the person's wellbeing and safety before the age of 18 years). And we aimed to have a better understanding of the specific risk configurations across genders and to develop potential clinical interventions. Participants and setting: This study explores network structures of early risks among 45,210 children and adolescents (aged 5 to 18) from longitudinal data in the UK. Methods: Network analysis was applied to investigate the associations among risks and to identify the central risks across genders. Results: Stable connections across genders in different assessments of risks (e.g., risks of self-harm and suicide). Risks related to violence could be core risks in all networks. Some gender differences in the context of early risks are also identified. For example, substance misuse and exhibiting violent or offending behavior are more closely associated among the male children that took the Brief Risk Assessment. Conclusions: Gendered associations between risks could be of value for both intervention and prevention. More attention should be paid to risks related to violence in clinical practice and policy making. Future study could record risks more precisely, utilize data from multiple time points and take more social-demographic factors into consideration to obtain integrated and comprehensive results

Values and beliefs in action research

Following a workshop at the CARN (Collaborative Action Research Network) 2022 conference, this research brought together five practitioner-researchers to consider our underpinning values and beliefs relating to action research. The ethical process of action research is driven by values, beliefs and reflexive practice; however, less is known about how engagement with action research might shape these values and beliefs. We co-designed a qualitative study comprising triad interviews whereby each co-researcher rotated into the role of interviewer, interviewee and observer. An iterative thematic analysis involving individual transcript coding and collaborative theme refinement generated six over-arching themes: Intention and action; Resistance and change; Researcher identity; Power and empowerment; Commitment; Equity, democracy and respect. We explored both how we each understand values and beliefs in the context of action research, and how we, as a research team, navigated those values and beliefs in practice. We argue that each of us has a personal responsibility to achieve quality action research; however, ways in which this can be externally supported requires thoughtful consideration