Intelligent Cloud Orchestration: A Hybrid Predictive and Heuristic Framework for Cost Optimization

The increasing prevalence of fraudulent applications on app stores, such as the Google Play Store, poses severe challenges to user privacy, financial security, and the reputation of legitimate developers. These fraudulent apps often exploit vulnerabilities by mimicking genuine applications, employing deceptive practices such as fake reviews, excessive permissions, and sudden rating spikes to appear trustworthy. Traditional static detection methods struggle to adapt to these evolving fraud strategies.This research introduces a novel hybrid approach that combines Decision Trees for feature importance ranking with Long Short-Term Memory (LSTM) networks for capturing temporal patterns in app behavior. The Decision Tree model identifies critical attributes such as permissions, app size, and user review sentiments that are most indicative of fraud. The LSTM model processes temporal data, such as sudden spikes in app downloads or ratings over time, to identify sequential patterns that are characteristic of fraudulent activity. The proposed system was evaluated on a comprehensive dataset containing app metadata, user reviews, and behavioral trends, demonstrating significant improvements in detection accuracy, precision, recall, and F1-score compared to traditional machine learning techniques like logistic regression and random forest. The integration of feature importance analysis and sequential modeling not only enhances detection accuracy but also provides interpretability, enabling developers and platform administrators to better understand fraudulent patterns. This hybrid approach offers a scalable, dynamic, and effective solution for safeguarding app stores and protecting users from malicious apps. Keywords Fraudulent Applications, Google Play Store, Fraud Detection, Decision Trees, Long Short-Term Memory (LSTM), Hybrid Model, Sequential Modeling, App Metadata, Temporal Patterns, User Reviews Analysis, Machine Learning, Deep Learning, Cybersecurity

Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault. This can be due to subsurface (geo)engineering activities such as fluid injections and geologic disposal of nuclear waste. Such activities are expected to rise in the future making it necessary to assess their short- and long-term safety. Here, a new machine learning (ML) approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is developed. The focus is on fault behavior near the injection borehole. To capture the temporal dependencies in the data, long short-term memory (LSTM) networks are utilized. To prevent error accumulation within the forecast window, four critical measures to train a robust LSTM model for predicting fault response are highlighted: (i) setting an appropriate value of LSTM lag, (ii) calibrating the LSTM cell dimension, (iii) learning rate reduction during weight optimization, and (iv) not adopting an independent injection cycle as a validation set. Several numerical experiments were conducted, which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid injection. The model also captured the decay in pressure and displacement during the injection shut-in period. Further, the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated, which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections.

Amazon Web Services (AWS) Elastic Compute Cloud (EC2) instances offer scalable computing resources crucial for various applications. Accurate prediction of CPU utilization is essential for efficient resource management and cost optimization in cloud environments. This study investigates the performance of machine learning models, specifically Long Short-Term Memory (LSTM) networks and AutoRegressive Integrated Moving Average (ARIMA) models, for forecasting CPU utilization of AWS EC2 instances in both development and production environments. By employing historical data from both environments, the research aims to extend predictive horizons and improve forecasting accuracy. We evaluate and compare model performance using Mean Squared Error (MSE) and fitting times. Results reveal that ARIMA models consistently outperform LSTM models in terms of MSE and computational efficiency, demonstrating superior performance in both environments. LSTM models, despite their potential, show higher variability and longer fitting times, especially with hyperparameter tuning. This paper highlights the critical role of model selection and tuning in enhancing forecasting capabilities and operational efficiency in cloud resource management. The findings contribute valuable insights for optimizing resource allocation and cost management in AWS cloud services.

Domestic passenger forecasting provides key input into decisions of daily operation management and infrastructure planning of airports and air navigation services and for aircraft ordering and design. Planning for the future is one of the most important keys to success, forecasting is the way. The goal of this study to predict the number of domestic passengers at Kualanamu International Airport. The time-series data were employed from Badan Pusat Statistik (BPS). The result is then discussed in the context of the potential use of the proposed for a new perspective for the predicting of domestic passengers at Kualanamu International Airport, Indonesia. The machine learning approach using long short term memory (LSTM) presents a useful way of observing the domestic passenger predict the passenger time series.

Accurate short-term electricity price forecasting is critical for ensuring market efficiency, grid reliability, and cost optimization in liberalized power markets such as Malaysia's. With the restructuring of the Malaysian Electricity Supply Industry (MESI 2.0) and increasing renewable energy penetration, price volatility has become more pronounced, creating new challenges for conventional forecasting techniques. This study explores the application of artificial intelligence (AI) and machine learning (ML) approaches—including Long Short-Term Memory (LSTM) networks, XGBoost, and hybrid deep learning models—for forecasting day-ahead System Marginal Prices (SMP) within Malaysia's Single Buyer Market framework. Historical pricing data, load demand, fuel mix, temperature, and solar irradiance were used as input features. The models were trained and validated using real SMP data from the Single Buyer Data Catalogue, with performance evaluated using RMSE, MAE, and MAPE metrics. Results indicate that LSTM outperforms other models in capturing non-linear temporal dependencies and sudden price spikes, achieving a forecasting accuracy improvement of up to 18% over baseline autoregressive models. The findings underscore the potential of AI-powered models to support grid operators, electricity retailers, and energy traders in managing risks, enhancing bidding strategies, and optimizing demand response participation. Furthermore, the study emphasizes the importance of model interpretability and data granularity in ensuring trustworthiness and operational deployment in Malaysia's evolving electricity market.

Machine learning (ML) has become a trending domain over the past few years, the accessibility of Graphical Processing Units (GPUs), Tensor Processing Units (TPUs) have given impetus for the same. Various applications like speech and face recognition, natural language processing, text analytics, big data analytics, regression analysis, pattern recognition and classification are based on the machine learning concept. Regression analysis evaluates the impact of a set of variables among themselves as well as the final formulation. Using this fitting of a particular theory for the real-world inputs can be evaluated. In this chapter regression analysis is performed on the COVID-19 data to predict the next values of the parameters. The Long Short Term Memory Networks (LSTMs) are used here for the prediction task, the LSTMs come under a special category of Neural Networks known as Recurrent Neural Networks (RNNs) which are used for this prediction task. The stateless and stateful implementation of LSTMs are designed and their performance is evaluated. The details of stateful and stateless architecture and their implementation in Keras framework is presented here. The results indicate that the LSTMs have better performance as compared to the RNNs.

Modern decision-making in fixed income asset management benefits from intelligent systems, which involve the use of state-of-the-art machine learning models and appropriate methodologies. We conduct the first study of bond yield forecasting using long short-term memory (LSTM) networks, validating its potential and identifying its memory advantage. Specifically, we model the 10-year bond yield using univariate LSTMs with three input sequences and five forecasting horizons. We compare those with multilayer perceptrons (MLP), univariate and with the most relevant features. To demystify the notion of black box associated with LSTMs, we conduct the first internal study of the model. To this end, we calculate the LSTM signals through time, at selected locations in the memory cell, using sequence-to-sequence architectures, uni and multivariate. We then proceed to explain the states’ signals using exogenous information, for what we develop the LSTM-LagLasso methodology. The results show that the univariate LSTM model with additional memory is capable of achieving similar results as the multivariate MLP using macroeconomic and market information. Furthermore, shorter forecasting horizons require smaller input sequences and vice-versa. The most remarkable property found consistently in the LSTM signals, is the activation/deactivation of units through time, and the specialisation of units by yield range or feature. Those signals are complex but can be explained by exogenous variables. Additionally, some of the relevant features identified via LSTM-LagLasso are not commonly used in forecasting models. In conclusion, our work validates the potential of LSTMs and methodologies for bonds, providing additional tools for financial practitioners.

Background: Cardiac arrhythmias are one of the leading causes of heart failure. In particular, atrial fibrillation (AFib) is a kind of arrhythmia that can lead to heart stroke and myocardial infarction. It is very important and crucial to predict AFib at an early stage to prevent heart disease. Electrocardiogram is one of the premium diagnostic tools which is used by most of the researchers for predicting irregular heartbeats. There are many works carried out in finding heart disease using machine learning classifiers. Aims and Objectives: Deep learning based hybrid Long Short Term Memory (LSTM) network is hybridized with Enhanced Whale Optimization (EWO) to minimize the network optimization and configuration issues faced in the existing models and proposed to increases the accuracy of predicting AFib. Materials and Methods: The proposed LSTM network is hybridized with a EWO technique for predicting AFib. This study uses a hybrid LSTM EWO network for classifying the various output labels of heart disease. EWO is used to predict the most relevant features from the raw dataset. Then, the LSTM model is used to predict the AFib of a patient from normal ECG data. Results: The DL based LSTM EWO achieves better results in all the performance metrics by analyzing the optimized features in feature space, training, and testing phase and successfully obtains better performance in an effective manner. LSTM improves the accuracy by reducing the number of units in the hidden layer which optimizes the network configuration. The proposed model achieves 96.12% accuracy which is 12.81% higher than RF, 15.01% higher than GB, 28.04% higher than CART, and 16.92% higher than SVM. Conclusion: The proposed model hybrid LSTM network integrated EWO for predicting the AFib. The EWO is applied for selecting the most appropriate features needed for the model to learn and produce improvised performance. The optimization and network configuration problems faced in the existing studies are avoided by choosing the suitable number of LSTM units and the size of the time window. This has been implemented through LSTM units and their window size. In addition, we made a statistical examination to prove the importance of proposed work against other models. It is observed that the experimental results attained with 96% of accuracy, better than conventional models.

Effective storm surge prediction is vital for safeguarding coastal communities and enhancing disaster preparedness, particularly as climate change amplifies the frequency and intensity of extreme events. Despite the growing application of Machine Learning (ML) in storm surge downscaling, systematic comparisons with high-resolution dynamical models and focused assessments of extreme events remain underexplored. This study bridges these gaps by comparing advanced dynamical modeling with ML techniques to improve storm surge forecasting in the Northern Adriatic Sea.High-resolution simulations were conducted using the SHYFEM-MPI model, leveraging optimized physical configurations and high-quality forcing datasets. This benchmark model demonstrated strong accuracy in representing storm surge dynamics and extremes, serving as a reference for evaluating ML-based approaches. To explore ML potential, models ranging from Multivariate Linear Regression (MLR) to the more advanced Long Short-Term Memory (LSTM) networks were developed and tested. A novel validation metric, the corrected mean absolute deviation (MADc) [1], and a tailored loss function (MADc2) were employed to improve model performance, particularly for extreme event prediction.Results highlighted that while MLR offered computational efficiency, it struggled to capture non-linear dynamics and extremes. In contrast, LSTM networks excelled at modeling temporal dependencies and non-linearities, particularly when trained using the MADc2 loss function. Training ML models on outputs from the dynamical model revealed that MADc2-based architectures aligned closely with observations, offering a cost-effective alternative to traditional downscaling when high-quality forcing data is unavailable. Moreover, direct training on observed data at key sites such as Punta della Salute and Trieste showed that ML models, including LSTM, could outperform the dynamical model on critical metrics, underscoring the value of observational data.This study underscores the promise of ML approaches in storm surge prediction, especially when integrated with high-quality data sources. By offering accurate predictions with significantly lower computational demands, ML techniques present a compelling case as efficient alternatives to traditional numerical models. As data accessibility and computational methods continue to advance, ML approaches may redefine the future of storm surge forecasting, enabling more sustainable and cost-effective solutions for coastal resilience. [1] Campos-Caba, R., Alessandri, J., Camus, P., Mazzino, A., Ferrari, F., Federico, I., Vousdoukas, M., Tondello, M., and Mentaschi, L. (2024). Assessing storm surge model performance: what error indicators can measure the model’s skill? Ocean Science 20, 1513-1526. https://doi.org/10.5194/os-20-1513-2024.

The expansion of online transactions, particularly online credit card transactions, has revolutionized the field of e-commerce and streamlined electronic payment systems. However, this growth has also given rise to a significant challenge in the form of credit card fraud. To combat this issue, banks and financial organizations have recognized the need for robust credit card fraud detection applications. Machine learning (ML) approaches have emerged as a valuable tool in this regard, as they offer the potential to accurately detect and prevent fraudulent transactions. Long Short-Term Memory (LSTM), a recurrent neural network, is used in this study’s evaluation of ML approaches for detecting credit card fraud (CCFD) in online transactions. The most effective LSTM architecture is chosen after a thorough examination based on its capacity to identify credit card fraud with high accuracy and precision. The suggested method makes use of LSTM and RFM analysis to comprehend customer behavior and ADASYN sampling to address class imbalance. The findings show that the selected LSTM architecture, in combination with RFM analysis and ADASYN, delivers great efficiency and efficacy in identifying credit card fraud, hence promoting safe online transactions.

In traditional financial performance evaluation models, parameter settings are often too large or too small, resulting in significant model errors. To address this issue, an improved artificial bee colony algorithm was proposed and applied to optimize the parameters of performance evaluation models. This method first constructs a corporate financial performance evaluation system, and then improves the artificial bee colony algorithm with differential evolution algorithm to optimize the parameters of the long short-term memory network, in order to improve the accuracy of the long short-term memory network in corporate financial performance evaluation. The results showed that the improvement of the ABC algorithm was effective. The improved ABC algorithm converged on the Ackley function in the 800th iteration, and the ABC algorithm converged in the 1400th iteration. The evaluation error of the proposed method is the lowest, with the algorithm having the lowest four errors of -0.0121, 0.0453, 0.0683, and 0.0047, respectively. Among the other algorithms, the comprehensive error of the financial performance evaluation model based on Long Short Term Memory (LSTM) network is relatively low, but still lower than the algorithm proposed in the study. The research proposes a long short-term memory network optimized based on improved artificial bee colony algorithm, which can accurately evaluate the financial performance of enterprises, help them review their own development level, and clarify their future development direction.

This paper explains prediction of share market trends of organizations using Artificial Neural Network (ANN). The Long Short Term Memory (LSTM) incorporated with a simple neural network gives the result of the movement of company's stock prices in the share market. LSTM is used for processing the time-series data. LSTM is a type of Recurrent Neural Network (RNN). In this work, layers of LSTM networks called stacked LSTM is a core component that process the huge volume of time series data. LSTM model works like a human brain because of the power to have a short term and long term memory. During data processing in the training stage, the model keeps a short term memory of the relation between the date and stock prices which is available in the data. It then starts keeping track of the relations from the successive dates and stock prices since the inception of the company. In this stage, the model tries to find a pattern or a trend in the stock price movement. This is kept in the long term memory. As the model processes further data, it finds an accurate pattern in the stock price movement. The exact date or a number of days is given as input and the stock price is given as output from the model

Wind power forecast based on improved Long Short Term Memory network

As a revolutionary tool leading to substantial changes across many areas, Machine Learning (ML) techniques have obtained growing attention in the field of hydrology due to their potentials to forecast time series. Moreover, a subfield of ML, Deep Learning (DL) is more concerned with datasets, algorithms and layered structures. Despite numerous applications of novel ML/DL techniques in discharge simulation, the uncertainty involved in ML/DL modeling has not drawn much attention, although it is an important issue. In this study, a framework is proposed to quantify uncertainty contributions of the sample set, ML approach, ML architecture and their interactions to multi-step time-series forecasting based on the analysis of variance (ANOVA) theory. Then a discharge simulation, using Recurrent Neural Networks (RNNs), is taken as an example. Long Short-Term Memory (LSTM) network, a state-of-the-art DL approach, was selected due to its outstanding performance in time-series forecasting, and compared with simple RNN. Besides, novel discharge forecasting architecture is designed by combining the expertise of hydrology and stacked DL structure, and compared with conventional design. Taking hourly discharge simulations of Anhe (China) catchment as a case study, we constructed five sample sets, chose two RNN approaches and designed two ML architectures. The results indicate that none of the investigated uncertainty sources are negligible and the influence of uncertainty sources varies with lead-times and discharges. LSTM demonstrates its superiority in discharge simulations, and the ML architecture is as important as the ML approach. In addition, some of the uncertainty is attributable to interactions rather than individual modeling components. The proposed framework can both reveal uncertainty quantification in ML/DL modeling and provide references for ML approach evaluation and architecture design in discharge simulations. It indicates uncertainty quantification is an indispensable task for a successful application of ML/DL.

Malaria remains a major public health burden in Nigeria, where climatic variability plays a critical role in shaping transmission dynamics. This study develops and evaluates climate-based predictive models for malaria incidence by integrating historical malaria surveillance data (2018–2023) with key meteorological variables, temperature, precipitation, humidity, and wind speed, across diverse ecological zones. Both traditional statistical and advanced machine learning (ML) approaches were employed to capture linear and nonlinear relationships between climate factors and malaria occurrence. Multiple Linear Regression (MLR) served as the baseline model, while Random Forest (RF), Support Vector Regression (SVR), Artificial Neural Network (ANN), Gradient Boosting Regression (GBR), XGBoost, and Long Short-Term Memory (LSTM) networks represented ML alternatives. Model performance was assessed using RMSE, MAE, R², and MAPE. Results revealed that ensemble-based ML models significantly outperformed MLR, with XGBoost emerging as the best performer (R² = 0.89; RMSE = 27.9; MAPE = 9.8%), followed closely by GBR and RF. The LSTM model effectively captured temporal dependencies (R² = 0.83), while MLR exhibited limited predictive ability (R² = 0.61). Regional analyses indicated that prediction accuracy was higher in areas with stable climatic conditions and reliable data reporting, whereas variability and data gaps in conflict-affected zones reduced performance. The findings highlight the superior predictive power and adaptability of ensemble ML methods for climate-driven malaria forecasting. The study offers an evidence-based framework for integrating these models into Nigeria’s early warning systems, supporting timely and geographically targeted malaria control interventions.