Integrating machine learning with metaheuristic optimization for power consumption prediction

Integrating machine learning with region-based active contour models in medical image segmentation

This paper proposes a novel approach for achieving sustainable energy systems in unexpected sports event management by integrating machine learning and optimization algorithms. Specifically, we used reinforcement learning for peak load forecasting and bat evolutionary algorithm for optimization, since the energy management problem in sports events is typically non-linear. Machine learning algorithms, specifically reinforcement learning, are used to analyze historical data and provide accurate peak load forecasts. This information can then be used to optimize energy consumption during the event through the use of algorithms such as the bat evolutionary algorithm, which can effectively solve non-linear optimization problems. The integration of these algorithms in unexpected sports event management can lead to significant improvements in sustainability and cost-effectiveness. This paper presents a case study of the implementation of reinforcement learning and bat evolutionary algorithms in an unexpected sports event management scenario, demonstrating the effectiveness of the proposed approach in achieving sustainable energy systems and reducing overall energy consumption. Overall, this paper provides a roadmap for integrating machine learning and optimization algorithms, such as reinforcement learning and bat evolutionary algorithm, in unexpected sports event management to achieve sustainable energy systems, promoting a more sustainable future for the sports event industry and the planet as a whole.

Energy efficiency is the use of technology that requires less energy to perform the same task. It was considered to introduce optimized energy efficiency by using machine learning to reduce power consumption at communication base station (BTS) sites. This process involves reviewing relevant work to identify defects, characterizing and determining the power consumption of the cell site under investigation, developing a SIMULINK model for the cell site under investigation, and identifying the module. It also includes optimizing high power consumption; design a machine learning rule base to monitor the power consumption of the module. Train artificial neural network (ANN) on machine learning rules designed to reduce cell power consumption, thereby improving network performance. The next step is developing an algorithm to implement it, and finally, to design a power consumption model for the network under investigation. The results obtained after a large simulation show that the traditional maximum power consumed at the cell site is 5746 kW, while the power when machine learning is injected into the system is 4733 kW. Integrating machine learning into the system resulted in 4731 kW, an 8.9% performance improvement.

This paper presents an anomaly detection method for encrypted network traffic using integrated machine learning. A stream feature extraction technique is employed to extract key features such as the median value of stream packets, median value of stream bytes, contrast stream, port growth rate, and source IP growth rate from the encrypted traffic. These features are then fed into an anomaly detection model that combines a collaborative neural network and a random forest classifier. An improved Bagging method is used to fuse and identify the anomalous characteristics of the encrypted traffic by weighted summation. Experimental results using the Trace dataset demonstrate that the proposed method achieves high precision and zero false positives in detecting various types of anomalies under different attack scenarios. The proposed approach offers a promising solution for ensuring network security and protecting against threats in encrypted communication channels.

Escalating climate crises, characterized by rising sea levels, alongside excessive groundwater pumping, have severely exacerbated saltwater intrusion, posing a critical threat to coastal aquifers. These combined environmental stressors induce complex, non-linear dynamics in groundwater systems, making the exact prediction of regime shifts driven by tipping points increasingly challenging. To address these uncertainties, this study proposes a comprehensive data-driven approach designed to identify early warning signals (EWS) for approaching tipping points using Electrical Conductivity (EC) time-series data. The primary objective is to investigate the feasibility of utilizing complementary statistical indicators—Variance and Fisher Information (FI)—to assess system instability. We analyzed monitoring data from Incheon and Jeju, South Korea, to validate whether these metrics can effectively filter noise and detect genuine precursor signals. Empirical results demonstrate that our approach achieves significantly enhanced performance in distinguishing critical transitions compared to single-indicator methods. Ultimately, this study serves as a foundational step towards establishing an "Integrated Machine Learning" framework. By validating these statistical metrics as key features, we aim to incorporate them into advanced learning algorithms to further improve the robustness and predictive accuracy of coastal groundwater management systems against climate-induced risks.AcknowledgementThis work was supported by National Research Foundation of Korea(NRF) grant funded by the Ministry of Science and Technology (RS-2024-00356786).

Assessing the impact of rainfall, topography, and human disturbances on nutrient levels using integrated machine learning and GAMs models in the Choctawhatchee River Watershed.

Optimal configuration of double carbon energy system considering climate change

A temporal graph framework for intelligence extraction in social media networks

The most popular way for companies of all sizes to satisfy their IT needs is now via cloud computing. The modern computational technology system operates as a fundamental framework which optimizes resource management systems alongside optimization outputs. Many discussions have emerged about integrating Machine Learning (ML) into cloud resources during the recent period. A publicly accessible Kaggle cloud computing performance dataset helps this research explore how ML optimization affects energy consumption alongside performance efficiency. The dataset includes variables such as CPU use, memory usage, power consumption, and task information. The regression models XGBoost Regressor delivered better outcomes than Gradient Boosting Regressor since it used standardized features to reach optimal performance ratings of MAE (0.0979), RMSE (0.1134), <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{\mathbf{2}}$</tex> (0.9999) and Max Error (0.2977). The deployment of Azure AutoML enabled automatic selection of the best model which was validated by 5-fold cross-validation. XGBoost demonstrates better predictive power than other models, such as Support Vector Regression (SVR) and LSTM, because its evaluation metric values demonstrate superior performance ratings. Visualizations of residuals, pair plots and task-type distributions provided additional support for the conducted statistical analysis. Research results demonstrated that XGBoost provided superior performance to all other models for forecasting CPU usage in cloud environments, thus establishing itself as a powerful tool for sustainable cloud infrastructure management.

Identification and application of the best-suited machine learning algorithm based on thermal comfort data characteristic: A data-driven approach

The Internet of Things (IoT) has become a foundational element of the digital infrastructure, extending its connectivity across various sectors and embedding intelligence in everyday devices. This article introduces SecureNet, a pioneering approach that integrates Machine Learning (ML), Blockchain, and Federated Learning (FL) to enhance IoT security. To navigate this challenging train, an innovative framework that synergizes Machine Learning (ML), Blockchain technology, and Federated Learning (FL) to fortify IoT security. SecureNet is architected to deliver a robust defense mechanism for IoT ecosystems, providing resilience against increasingly sophisticated cyber threats, and ensuring the preservation of data integrity, privacy, and unwavering system reliability. This study explores the application of advanced ML techniques NSL-KDD dataset, implementing two highly effective classifiers: Random Forest and Logistic Regression. The Random Forest classifier exhibited an exceptional accuracy of 99.85%, while the Logistic Regression model demonstrated a near-perfect accuracy of 99.03%. These compelling results highlight the efficacy of ML in identifying and mitigating activities within network traffic. SecureNet leverages ML’s profound analytical capabilities for intelligent threat discernment, Blockchain’s immutable ledgers for unassailable data verification, and FL’s privacy-centric approach to distribute model training. These outcomes underscore the potential of ML models to enhance IoT security by accurately identifying malicious patterns and anomalies within network traffic.

Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.

The integration of machine learning (ML) and artificial intelligence (AI) techniques in life-course epidemiology offers remarkable opportunities to advance our understanding of the complex interplay between biological, social, and environmental factors that shape health trajectories across the lifespan. This perspective summarizes the current applications, discusses future potential and challenges, and provides recommendations for harnessing ML and AI technologies to develop innovative public health solutions. ML and AI have been increasingly applied in epidemiological studies, demonstrating their ability to handle large, complex datasets, identify intricate patterns and associations, integrate multiple and multimodal data types, improve predictive accuracy, and enhance causal inference methods. In life-course epidemiology, these techniques can help identify sensitive periods and critical windows for intervention, model complex interactions between risk factors, predict individual and population-level disease risk trajectories, and strengthen causal inference in observational studies. By leveraging the five principles of life-course research proposed by Elder and Shanahan—lifespan development, agency, time and place, timing, and linked lives—we discuss a framework for applying ML and AI to uncover novel insights and inform targeted interventions. However, the successful integration of these technologies faces challenges related to data quality, model interpretability, bias, privacy, and equity. To fully realize the potential of ML and AI in life-course epidemiology, fostering interdisciplinary collaborations, developing standardized guidelines, advocating for their integration in public health decision-making, prioritizing fairness, and investing in training and capacity building are essential. By responsibly harnessing the power of ML and AI, we can take significant steps towards creating healthier and more equitable futures across the life course.

Current research in data dissemination in Vehicular Ad Hoc Networks (VANETs) has examined different approaches and frameworks to enhance the effectiveness and dependability of information sharing between vehicles on the road. The integration of Machine Learning (ML) with Software-Defined Networking (SDN) has fundamentally transformed the field of network administration and security. This paper specifically addresses the challenges faced by traditional network architectures in effectively handling the increasing amount of data and complex applications. Software-Defined Networking (SDN), a cutting-edge framework, separates the control of network operations from the actual forwarding of data, offering a versatile and cost-effective solution. The combination of Software-Defined Networking (SDN) and Machine Learning (ML) allows for the extraction of valuable information from network data, leading to enhanced network management and the facilitation of predictive analytics. The aim of this study is to examine the feasibility and challenges of incorporating machine learning into software-defined networking (SDN), focusing particularly on practical applications. Integrating Machine Learning (ML) into Software-Defined Networking (SDN) presents challenges, including the requirement for robust algorithms to detect patterns and ensure security. It is crucial to carry out the tasks of developing and implementing machine learning models for real-time predictions and ensuring the robustness of the system. Research is essential to strike a balance between the transformative abilities of ML-SDN and the practical network environments. This helps to improve the resilience, security, and adaptability of network infrastructures in the digital age.

Recent advances on constraint-based models by integrating machine learning