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  • Open Access

    ARTICLE

    Analyzing Human Trafficking Networks Using Graph-Based Visualization and ARIMA Time Series Forecasting

    Naif Alsharabi1,*, Akashdeep Bhardwaj2,*

    Journal of Cyber Security, Vol.7, pp. 135-163, 2025, DOI:10.32604/jcs.2025.064019 - 18 June 2025

    Abstract In a world driven by unwavering moral principles rooted in ethics, the widespread exploitation of human beings stands universally condemned as abhorrent and intolerable. Traditional methods employed to identify, prevent, and seek justice for human trafficking have demonstrated limited effectiveness, leaving us confronted with harrowing instances of innocent children robbed of their childhood, women enduring unspeakable humiliation and sexual exploitation, and men trapped in servitude by unscrupulous oppressors on foreign shores. This paper focuses on human trafficking and introduces intelligent technologies including graph database solutions for deciphering unstructured relationships and entity nodes, enabling the comprehensive More >

  • Open Access

    ARTICLE

    Short-Term Electricity Load Forecasting Based on T-CFSFDP Clustering and Stacking-BiGRU-CBAM

    Mingliang Deng1, Zhao Zhang1,*, Hongyan Zhou2, Xuebo Chen2

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1189-1202, 2025, DOI:10.32604/cmc.2025.064509 - 09 June 2025

    Abstract To fully explore the potential features contained in power load data, an innovative short-term power load forecasting method that integrates data mining and deep learning techniques is proposed. Firstly, a density peak fast search algorithm optimized by time series weighting factors is used to cluster and analyze load data, accurately dividing subsets of data into different categories. Secondly, introducing convolutional block attention mechanism into the bidirectional gated recurrent unit (BiGRU) structure significantly enhances its ability to extract key features. On this basis, in order to make the model more accurately adapt to the dynamic changes… More >

  • Open Access

    ARTICLE

    Demand Forecasting of a Microgrid-Powered Electric Vehicle Charging Station Enabled by Emerging Technologies and Deep Recurrent Neural Networks

    Sahbi Boubaker1,*, Adel Mellit2,3,*, Nejib Ghazouani4, Walid Meskine5, Mohamed Benghanem6, Habib Kraiem7,8

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2237-2259, 2025, DOI:10.32604/cmes.2025.064530 - 30 May 2025

    Abstract Electric vehicles (EVs) are gradually being deployed in the transportation sector. Although they have a high impact on reducing greenhouse gas emissions, their penetration is challenged by their random energy demand and difficult scheduling of their optimal charging. To cope with these problems, this paper presents a novel approach for photovoltaic grid-connected microgrid EV charging station energy demand forecasting. The present study is part of a comprehensive framework involving emerging technologies such as drones and artificial intelligence designed to support the EVs’ charging scheduling task. By using predictive algorithms for solar generation and load demand… More >

  • Open Access

    ARTICLE

    Methodology for Detecting Non-Technical Energy Losses Using an Ensemble of Machine Learning Algorithms

    Irbek Morgoev1, Roman Klyuev2,*, Angelika Morgoeva1

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1381-1399, 2025, DOI:10.32604/cmes.2025.064502 - 30 May 2025

    Abstract Non-technical losses (NTL) of electric power are a serious problem for electric distribution companies. The solution determines the cost, stability, reliability, and quality of the supplied electricity. The widespread use of advanced metering infrastructure (AMI) and Smart Grid allows all participants in the distribution grid to store and track electricity consumption. During the research, a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings. This model is an ensemble meta-algorithm (stacking) that generalizes the algorithms of random… More >

  • Open Access

    ARTICLE

    Predicting Short-Term Wind Power Generation at Musalpetti Wind Farm: Model Development and Analysis

    Namal Rathnayake1, Jeevani Jayasinghe2,3, Rashmi Semasinghe2, Upaka Rathnayake4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2287-2305, 2025, DOI:10.32604/cmes.2025.064464 - 30 May 2025

    Abstract In this study, a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions. Using data on wind speed, air temperature, nacelle position, and actual power, lagged features were generated to capture temporal dependencies. Among 24 evaluated models, the ensemble bagging approach achieved the best performance, with R2 values of 0.89 at 0 min and 0.75 at 60 min. Shapley Additive exPlanations (SHAP) analysis revealed that while wind speed is the primary driver for short-term predictions, air temperature and nacelle position become more More >

  • Open Access

    ARTICLE

    Multi-Timescale Optimization Scheduling of Distribution Networks Based on the Uncertainty Intervals in Source-Load Forecasting

    Huanan Yu, Chunhe Ye, Shiqiang Li*, He Wang, Jing Bian, Jinling Li

    Energy Engineering, Vol.122, No.6, pp. 2417-2448, 2025, DOI:10.32604/ee.2025.061214 - 29 May 2025

    Abstract With the increasing integration of large-scale distributed energy resources into the grid, traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load. Accounting for these issues, this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks. First, the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts, based on which, the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed. Subsequently, a multi-timescale optimization framework was constructed, incorporating the generation and load forecast uncertainties. More >

  • Open Access

    ARTICLE

    Pitcher Performance Prediction Major League Baseball (MLB) by Temporal Fusion Transformer

    Wonbyung Lee, Jang Hyun Kim*

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5393-5412, 2025, DOI:10.32604/cmc.2025.065413 - 19 May 2025

    Abstract Predicting player performance in sports is a critical challenge with significant implications for team success, fan engagement, and financial outcomes. Although, in Major League Baseball (MLB), statistical methodologies such as sabermetrics have been widely used, the dynamic nature of sports makes accurate performance prediction a difficult task. Enhanced forecasts can provide immense value to team managers by aiding strategic player contract and acquisition decisions. This study addresses this challenge by employing the temporal fusion transformer (TFT), an advanced and cutting-edge deep learning model for complex data, to predict pitchers’ earned run average (ERA), a key More >

  • Open Access

    ARTICLE

    A Hybrid Deep Learning Method for Forecasting Reservoir Water Level from Sentinel-2 Satellite Images

    Hoang Thi Minh Chau1,2,3, Tran Thi Ngan4,*, Nguyen Long Giang5, Tran Manh Tuan6, Tran Kim Chau7

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4915-4937, 2025, DOI:10.32604/cmc.2025.062784 - 19 May 2025

    Abstract Global climate change, along with the rapid increase of the population, has put significant pressure on water security. A water reservoir is an effective solution for adjusting and ensuring water supply. In particular, the reservoir water level is an essential physical indicator for the reservoirs. Forecasting the reservoir water level effectively assists the managers in making decisions and plans related to reservoir management policies. In recent years, deep learning models have been widely applied to solve forecasting problems. In this study, we propose a novel hybrid deep learning model namely the YOLOv9_ConvLSTM that integrates YOLOv9,… More >

  • Open Access

    ARTICLE

    Smart Grid Peak Shaving with Energy Storage: Integrated Load Forecasting and Cost-Benefit Optimization

    Cong Zhang1,2, Chutong Zhang2, Lei Shen1, Renwei Guo2, Wan Chen1, Hui Huang2, Jie Ji2,*

    Energy Engineering, Vol.122, No.5, pp. 2077-2097, 2025, DOI:10.32604/ee.2025.064175 - 25 April 2025

    Abstract This paper presents a solution for energy storage system capacity configuration and renewable energy integration in smart grids using a multi-disciplinary optimization method. The solution involves a hybrid prediction framework based on an improved grey regression neural network (IGRNN), which combines grey prediction, an improved BP neural network, and multiple linear regression with a dynamic weight allocation mechanism to enhance prediction accuracy. Additionally, an improved cuckoo search (ICS) algorithm is designed to empower the neural network model, incorporating a gamma distribution disturbance factor and adaptive inertia weight to balance global exploration and local exploitation, achieving… More >

  • Open Access

    ARTICLE

    Bidirectional LSTM-Based Energy Consumption Forecasting: Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

    Sheik Mohideen Shah1, Meganathan Selvamani1, Mahesh Thyluru Ramakrishna2,*, Surbhi Bhatia Khan3,4,5, Shakila Basheer6, Wajdan Al Malwi7, Mohammad Tabrez Quasim8

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2907-2926, 2025, DOI:10.32604/cmc.2025.063809 - 16 April 2025

    Abstract Efficient energy management is a cornerstone of advancing cognitive cities, where AI, IoT, and cloud computing seamlessly integrate to meet escalating global energy demands. Within this context, the ability to forecast electricity consumption with precision is vital, particularly in residential settings where usage patterns are highly variable and complex. This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory (LSTM) network. Leveraging a dataset containing over two million multivariate, time-series observations collected from a single household over nearly four years, our model addresses the limitations of traditional time-series forecasting… More >

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