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  • Open Access

    ARTICLE

    Comparison of Objective Forecasting Method Fit with Electrical Consumption Characteristics in Timor-Leste

    Ricardo Dominico Da Silva1,2, Jangkung Raharjo1,3,*, Sudarmono Sasmono1,3

    Energy Engineering, Vol.122, No.12, pp. 5073-5090, 2025, DOI:10.32604/ee.2025.071545 - 27 November 2025

    Abstract The rapid development of technology has led to an ever-increasing demand for electrical energy. In the context of Timor-Leste, which still relies on fossil energy sources with high operational costs and significant environmental impacts, electricity load forecasting is a strategic measure to support the energy transition towards the Net Zero Emission (NZE) target by 2050. This study aims to utilize historical electricity load data for the period 2013–2024, as well as data on external factors affecting electricity consumption, to forecast electricity load in Timor-Leste in the next 10 years (2025–2035). The forecasting results are expected… More >

  • Open Access

    ARTICLE

    SP-Sketch: Persistent Flow Detection with Sliding Windows on Programmable Switches

    Yuqian Huang1, Luyi Chen2, Zilun Peng1, Lin Cui1,*

    CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 6015-6034, 2025, DOI:10.32604/cmc.2025.066717 - 30 July 2025

    Abstract Persistent flows are defined as network flows that persist over multiple time intervals and continue to exhibit activity over extended periods, which are critical for identifying long-term behaviors and subtle security threats. Programmable switches provide line-rate packet processing to meet the requirements of high-speed network environments, yet they are fundamentally limited in computational and memory resources. Accurate and memory-efficient persistent flow detection on programmable switches is therefore essential. However, existing approaches often rely on fixed-window sketches or multiple sketches instances, which either suffer from insufficient temporal precision or incur substantial memory overhead, making them ineffective… More >

  • Open Access

    ARTICLE

    E-SWAN: Efficient Sliding Window Analysis Network for Real-Time Speech Steganography Detection

    Kening Wang1,#, Feipeng Gao2,#, Jie Yang1,2,*, Hao Zhang1

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4797-4820, 2025, DOI:10.32604/cmc.2025.060042 - 06 March 2025

    Abstract With the rapid advancement of Voice over Internet Protocol (VoIP) technology, speech steganography techniques such as Quantization Index Modulation (QIM) and Pitch Modulation Steganography (PMS) have emerged as significant challenges to information security. These techniques embed hidden information into speech streams, making detection increasingly difficult, particularly under conditions of low embedding rates and short speech durations. Existing steganalysis methods often struggle to balance detection accuracy and computational efficiency due to their limited ability to effectively capture both temporal and spatial features of speech signals. To address these challenges, this paper proposes an Efficient Sliding Window… More >

  • Open Access

    ARTICLE

    Classification and Comprehension of Software Requirements Using Ensemble Learning

    Jalil Abbas1,*, Arshad Ahmad2, Syed Muqsit Shaheed3, Rubia Fatima4, Sajid Shah5, Mohammad Elaffendi5, Gauhar Ali5

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2839-2855, 2024, DOI:10.32604/cmc.2024.052218 - 15 August 2024

    Abstract The software development process mostly depends on accurately identifying both essential and optional features. Initially, user needs are typically expressed in free-form language, requiring significant time and human resources to translate these into clear functional and non-functional requirements. To address this challenge, various machine learning (ML) methods have been explored to automate the understanding of these requirements, aiming to reduce time and human effort. However, existing techniques often struggle with complex instructions and large-scale projects. In our study, we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier (FNRC). By combining the… More >

  • Open Access

    ARTICLE

    Prediction of the Wastewater’s pH Based on Deep Learning Incorporating Sliding Windows

    Aiping Xu1,2, Xuan Zou3, Chao Wang2,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1043-1059, 2023, DOI:10.32604/csse.2023.039645 - 26 May 2023

    Abstract To protect the environment, the discharged sewage’s quality must meet the state’s discharge standards. There are many water quality indicators, and the pH (Potential of Hydrogen) value is one of them. The natural water’s pH value is 6.0–8.5. The sewage treatment plant uses some data in the sewage treatment process to monitor and predict whether wastewater’s pH value will exceed the standard. This paper aims to study the deep learning prediction model of wastewater’s pH. Firstly, the research uses the random forest method to select the data features and then, based on the sliding window,… More >

  • Open Access

    ARTICLE

    Surge Fault Detection of Aeroengines Based on Fusion Neural Network

    Desheng Zheng1, Xiaolan Tang1,*, Xinlong Wu1, Kexin Zhang1, Chao Lu2, Lulu Tian3

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 815-826, 2021, DOI:10.32604/iasc.2021.017737 - 01 July 2021

    Abstract Aeroengine surge fault is one of the main causes of flight accidents. When a surge occurs, it is hard to detect it in time and take anti-surge measures correctly. Recently, people have been applying detection methods based on mathematical models and expert knowledge. Due to difficult modeling and limited failure-mode coverage of these methods, early surge detection cannot be achieved. To address these problems, firstly, this paper introduced the data of six main sensors related to the aeroengine surge fault, such as, total pressure at compressor (high pressure rotor) outlet (Pt3), high pressure compressor rotor More >

  • Open Access

    ARTICLE

    Random Forests Algorithm Based Duplicate Detection in On-Site Programming Big Data Environment

    Qianqian Li1, Meng Li2, Lei Guo3,*, Zhen Zhang4

    Journal of Information Hiding and Privacy Protection, Vol.2, No.4, pp. 199-205, 2020, DOI:10.32604/jihpp.2020.016299 - 07 January 2021

    Abstract On-site programming big data refers to the massive data generated in the process of software development with the characteristics of real-time, complexity and high-difficulty for processing. Therefore, data cleaning is essential for on-site programming big data. Duplicate data detection is an important step in data cleaning, which can save storage resources and enhance data consistency. Due to the insufficiency in traditional Sorted Neighborhood Method (SNM) and the difficulty of high-dimensional data detection, an optimized algorithm based on random forests with the dynamic and adaptive window size is proposed. The efficiency of the algorithm can be More >

  • Open Access

    ARTICLE

    Efficient Heavy Hitters Identification over Speed Traffic Streams

    Shuzhuang Zhang1, Hao Luo1, Zhigang Wu1, Yanbin Sun2, *, Yuhang Wang2, Tingting Yuan3

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 213-222, 2020, DOI:10.32604/cmc.2020.07496 - 30 March 2020

    Abstract With the rapid increase of link speed and network throughput in recent years, much more attention has been paid to the work of obtaining statistics over speed traffic streams. It is a challenging problem to identify heavy hitters in high-speed and dynamically changing data streams with less memory and computational overhead with high measurement accuracy. In this paper, we combine Bloom Filter with exponential histogram to query streams in the sliding window so as to identify heavy hitters. This method is called EBF sketches. Our sketch structure allows for effective summarization of streams over time-based More >

  • Open Access

    ARTICLE

    A Scalable Method of Maintaining Order Statistics for Big Data Stream

    Zhaohui Zhang*,1,2,3, Jian Chen1, Ligong Chen1, Qiuwen Liu1, Lijun Yang1, Pengwei Wang1,2,3, Yongjun Zheng4

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 117-132, 2019, DOI:10.32604/cmc.2019.05325

    Abstract Recently, there are some online quantile algorithms that work on how to analyze the order statistics about the high-volume and high-velocity data stream, but the drawback of these algorithms is not scalable because they take the GK algorithm as the subroutine, which is not known to be mergeable. Another drawback is that they can’t maintain the correctness, which means the error will increase during the process of the window sliding. In this paper, we use a novel data structure to store the sketch that maintains the order statistics over sliding windows. Therefore three algorithms have… More >

  • Open Access

    ARTICLE

    Differentially Private Real-Time Streaming Data Publication Based on Sliding Window Under Exponential Decay

    Lan Sun1, Chen Ge1, Xin Huang1, Yingjie Wu1,*, Yan Gao2

    CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 61-78, 2019, DOI:10.32604/cmc.2019.03744

    Abstract Continuous response of range query on steaming data provides useful information for many practical applications as well as the risk of privacy disclosure. The existing research on differential privacy streaming data publication mostly pay close attention to boosting query accuracy, but pay less attention to query efficiency, and ignore the effect of timeliness on data weight. In this paper, we propose an effective algorithm of differential privacy streaming data publication under exponential decay mode. Firstly, by introducing the Fenwick tree to divide and reorganize data items in the stream, we achieve a constant time complexity… More >

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