Abstract

Cloud resource brokerage is a fundamental challenge in cloud computing, requiring the efficient selection and allocation of services from multiple providers to optimize performance, sustainability, and cost-effectiveness. Traditional approaches often struggle with balancing conflicting objectives, such as minimizing the response time, reducing energy consumption, and maximizing broker profits. This paper presents NSGA-III-GKM++, an advanced multi-objective optimization model that integrates the NSGA-III evolutionary algorithm with an enhanced K-means++ clustering technique to improve the convergence speed, solution diversity, and computational efficiency. The proposed framework is extensively evaluated using Deb–Thiele–Laumanns–Zitzler (DTLZ) and Unconstrained Function (UF) benchmark problems and real-world cloud brokerage scenarios. Comparative analysis against NSGA-II, MOPSO, and NSGA-III-GKM demonstrates the superiority of NSGA-III-GKM++ in achieving high-quality tradeoffs between performance and cost. The results indicate a 20% reduction in the response time, 15% lower energy consumption, and a 25% increase in the broker’s profit, validating its effectiveness in real-world deployments. Statistical significance tests further confirm the robustness of the proposed model, particularly in terms of hypervolume and Inverted Generational Distance (IGD) metrics. By leveraging intelligent clustering and evolutionary computation, NSGA-III-GKM++ serves as a powerful decision support tool for cloud brokerage, facilitating optimal service selection while ensuring sustainability and economic feasibility.

Abstract

Financial markets exhibit high volatility due to various external factors, making stock price prediction a complex yet crucial task for investors and financial institutions. Accurate forecasting not only enhances decision making but also mitigates financial risks. This paper introduces SMAPF-HNNA, an advanced framework that integrates multiple neural network (NN) architectures for robust time-series analysis and stock price forecasting. The proposed approach leverages Convolutional Neural Networks (CNNs) for automatic feature extraction, followed by the application of diverse NN models, including Simple Recurrent Neural Networks (SRNNs), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Units (GRU), Bidirectional GRU (BiGRU), and Multilayer Perceptron (MLP) for precise stock price prediction. The framework is rigorously evaluated on multiple benchmark datasets, including NYSE, S&P 500, NASDAQ, and SSE, through extensive training and testing phases. Experimental results demonstrate that the hybrid CNN-MLP model outperforms other architectures across all datasets, achieving exceptionally low error rates with five key regression metrics. The model yields mean squared error (MSE) values between 0.000031 and 0.000004, root mean squared error (RMSE) between 0.0020 and 0.0056, mean absolute error (MAE) between 0.0018 and 0.0042, mean absolute percentage error (MAPE) between 0.12% and 0.32%, and R-squared (R) values ranging from 0.9995 to 0.9999, while maintaining low computational complexity across datasets. These results highlight the potential of SMAPF-HNNA as a highly accurate and computationally efficient solution for stock market prediction, addressing the limitations of previous methods. The proposed framework offers valuable insights for researchers and practitioners, paving the way for more reliable financial market forecasting models.

Abstract

Effective portfolio management is crucial in today’s fast-moving and unpredictable financial landscape. This paper introduces a powerful and adaptive investment framework that fuses classical portfolio theory with cutting-edge artificial intelligence (AI) to optimize portfolio performance during volatile market conditions. Our methodology seamlessly integrates K-means clustering to identify asset groupings based on correlation structures of technical indicators, mean-variance optimization (MVO) to achieve an ideal risk-return trade-off, and advanced Machine Learning (ML) and reinforcement learning (RL) techniques to dynamically adjust asset allocations and simulate market behavior. The proposed framework is rigorously evaluated on historical stock data from 60 prominent stocks listed on NASDAQ, NYSE, and S&P 500 indices between 2021 and 2024, a period marked by significant economic shocks, global uncertainty, and structural market shifts. Our experimental results show that our framework consistently outperforms traditional strategies and recent state of the art models, achieving superior metrics including Sharpe ratio, Sortino ratio, annual return, maximum drawdown, and Calmar ratio. We also assess the computational efficiency of the approach, ensuring its feasibility for real-world deployment. This work demonstrates the transformative potential of AI-driven portfolio optimization in empowering investors to make smarter, faster, and more resilient financial decisions amid uncertainty.

Abstract

Emergency departments (EDs) face increasing
patient demand, leading to overcrowding and resource strain.
Accurate forecasting of ED visits is critical for optimizing
hospital operations and ensuring efficient resource allocation.
This paper proposes a hybrid model combining Variational
Autoencoder (VAE) and Gated Recurrent Unit (GRU) to enhance
patient flow predictions. The VAE extracts meaningful
latent features while handling missing data, whereas the GRU
captures complex temporal dependencies, improving forecasting
accuracy. Compared to traditional models such as LSTM,
GRU, and 1D CNN, our hybrid VAE-GRU model demonstrates
superior predictive performance. Experimental results, based
on real-world hospital data, highlight the model’s effectiveness
in reducing prediction errors and improving decision-making
in dynamic ED environments. Additionally, we compare the
proposed model with ARIMA-ML, emphasizing the tradeoffs
between computational efficiency and prediction accuracy.
The findings suggest that hybrid deep learning approaches
can significantly enhance healthcare resource management,
reducing patient waiting times and improving overall hospital
efficiency.

Abstract

Emergency departments (EDs) must quickly assess
and prioritize patients, especially during crises when demand

exceeds capacity. Traditional triage methods, such as the Jump-
START protocol for pediatric cases and the START (Simple

Triage and Rapid Treatment) method for adults, are commonly
used but may lack precision under high-pressure situations.
This paper proposes a hybrid approach combining ensemble
models—XGBoost, AdaBoost, and CatBoost—with synthetic data
augmentation using Generative Adversarial Networks (GANs) to
enhance triage accuracy for critically ill patients.
Models were trained on real-world ED data, including vital
signs, symptoms, medical history, and demographics. GANs
generated synthetic critical cases to address class imbalance,
improving model sensitivity to high-risk profiles.

Results show that GAN-augmented models outperform base-
line models, with CatBoost offering the best balance between

accuracy and computational efficiency. This approach improves
patient prioritization, reduces delays, and supports better clinical
decision-making in resource-limited environments.
Index Terms—Emergency Department (ED), Patient Triage,

Machine Learning (ML), AdaBoost, XGBoost, CatBoost, Genera-
tive Adversarial Networks (GANs), Urgency Classification, Crisis

Situations.

Abstract

this paper presents a novel ensemble
methodology that integrates deep learning models to enhance
the accuracy and robustness of financial volatility forecasts. By
combining Convolutional Neural Networks (CNNs) and GRU
networks, the proposed approach captures both spatial and
temporal patterns in financial time series data. Empirical results
demonstrate the superiority of this ensemble model over
traditional forecasting methods in various financial markets.
Keywords: Volatility Forecasting, Deep Learning, Ensemble
Modeling, CNN, GRU, Financial Time Series

Abstract

Tourism is nowadays fully acknowledged as a leading industry contributing to boost the economic development of a country. This growing recognition has led researchers and policy makers to increasingly focus their attention on all those concerns related to optimally detecting, promoting and supporting territorial areas with a high tourist vocation, i.e., Local Tourism Systems. In this work, we propose to apply the biclustering data mining technique to detect Local Tourism Systems. By means of a two-dimensional clustering approach, we pursue the objective of obtaining more in-depth and granular information than conventional clustering algorithms. To this end, we formulate the objective as an optimization problem, and we solve it by means of Tabu-search. The obtained results are very promising and outperform those provided by classic clustering approaches.

Abstract

Recommendation systems have become essential for filtering the vast amounts of information available on the Internet. Traditional collaborative filtering methods face challenges such as data sparsity and scalability issues. To address these limitations, we propose ColBic, a novel collaborative filtering approach based on biclustering and Iterative Local Search (ILS). Our method improves the accuracy of the recommendation by grouping users and items into dense biclusters and refining them through iterative optimization. Experimental results on the MovieLens-100K and MovieLens-1M datasets demonstrate that ColBic outperforms traditional collaborative filtering methods in terms of accuracy and coverage.

Abstract

Code smells signal poor software design that can prevent maintainability and scalability. Identifying code smells is difficult because of the large volume of code, considerable detection expenses, and the substantial effort needed for manual tagging. Although current techniques perform well in within-project situations, they frequently struggle to adapt to cross-project environments that have varying data distributions. In this paper, we introduce CLADES (Cross-project Learning and Adaptation for Detection of Code Smells), a hybrid evolutionary approach consisting of three main modules: Initialization, Evolution, and Adaptation. The first module generates an initial population of decision tree detectors using labeled within-project data and evaluates their quality through fitness functions based on structural code metrics. The evolution module applies genetic operators (selection, crossover, and mutation) to create new offspring solutions. To handle cross-project scenarios, the adaptation module employs a clustering-based instance selection technique that identifies representative instances from new projects, which are added to the dataset and used to repair the decision trees through simulated annealing. These locally refined decision trees are then evolved using a genetic algorithm, thus enabling continuous adaptation to new project instances. The resulting optimized decision tree detectors are then employed to predict labels for the new unlabeled project instances. We assess CLADES across five open-source projects and we show that it has a better performance with respect to baseline techniques in terms of weighted F1-score and AUC-PR metrics. These results emphasize its capacity to effectively adjust to different project environments, facilitating precise and scalable detection of code smells while minimizing the need for manual review, contributing to more robust and maintainable software systems.

Abstract

Decomposition-based Many-Objective Evolutionary Algorithms (MaOEAs) usually adopt a set of pre-defined distributed weight vectors to guide the solutions towards the Pareto optimal Front (PF). However, when solving Many-objective Optimization Problems (MaOPs) with complex PFs, the effectiveness of MaOEAs with a fixed set of weight vectors may deteriorate which will lead to an imbalance between convergence and diversity of the solution set. To address this issue, we propose here an Adaptive Normal-Boundary Intersection Directions Decomposition-based Evolutionary Algorithm (ANBID-DEA), which adaptively updates the Normal-Boundary Intersection (NBI) directions used in MP-DEA. In our work, we assist the selection mechanism by progressively adjusting the NBI directions according to the distribution of the population to uniformly cover all the parts of the complex PFs (i.e., those that are disconnected, strongly convex, degenerate, etc.). Our proposed ANBID-DEA is compared with respect to five state-of-the-art MaOEAs on a variety of unconstrained benchmark problems with up to 15 objectives. Our results indicate that ANBID-DEA has a competitive performance on most of the considered MaOPs.