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  • Open Access

    ARTICLE

    Floating Waste Discovery by Request via Object-Centric Learning

    Bingfei Fu*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1407-1424, 2024, DOI:10.32604/cmc.2024.052656 - 18 July 2024

    Abstract Discovering floating wastes, especially bottles on water, is a crucial research problem in environmental hygiene. Nevertheless, real-world applications often face challenges such as interference from irrelevant objects and the high cost associated with data collection. Consequently, devising algorithms capable of accurately localizing specific objects within a scene in scenarios where annotated data is limited remains a formidable challenge. To solve this problem, this paper proposes an object discovery by request problem setting and a corresponding algorithmic framework. The proposed problem setting aims to identify specified objects in scenes, and the associated algorithmic framework comprises pseudo… More >

  • Open Access

    ARTICLE

    Enhancing AI System Privacy: An Automatic Tool for Achieving GDPR Compliance in NoSQL Databases

    Yifei Zhao, Zhaohui Li, Siyi Lv*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 217-234, 2024, DOI:10.32604/cmc.2024.052310 - 18 July 2024

    Abstract The EU’s Artificial Intelligence Act (AI Act) imposes requirements for the privacy compliance of AI systems. AI systems must comply with privacy laws such as the GDPR when providing services. These laws provide users with the right to issue a Data Subject Access Request (DSAR). Responding to such requests requires database administrators to identify information related to an individual accurately. However, manual compliance poses significant challenges and is error-prone. Database administrators need to write queries through time-consuming labor. The demand for large amounts of data by AI systems has driven the development of NoSQL databases.… More >

  • Open Access

    ARTICLE

    YOLO-Based Damage Detection with StyleGAN3 Data Augmentation for Parcel Information-Recognition System

    Seolhee Kim1, Sang-Duck Lee2,*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 195-215, 2024, DOI:10.32604/cmc.2024.052070 - 18 July 2024

    Abstract Damage to parcels reduces customer satisfaction with delivery services and increases return-logistics costs. This can be prevented by detecting and addressing the damage before the parcels reach the customer. Consequently, various studies have been conducted on deep learning techniques related to the detection of parcel damage. This study proposes a deep learning-based damage detection method for various types of parcels. The method is intended to be part of a parcel information-recognition system that identifies the volume and shipping information of parcels, and determines whether they are damaged; this method is intended for use in the… More >

  • Open Access

    ARTICLE

    Network Security Enhanced with Deep Neural Network-Based Intrusion Detection System

    Fatma S. Alrayes1, Mohammed Zakariah2, Syed Umar Amin3,*, Zafar Iqbal Khan3, Jehad Saad Alqurni4

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1457-1490, 2024, DOI:10.32604/cmc.2024.051996 - 18 July 2024

    Abstract This study describes improving network security by implementing and assessing an intrusion detection system (IDS) based on deep neural networks (DNNs). The paper investigates contemporary technical ways for enhancing intrusion detection performance, given the vital relevance of safeguarding computer networks against harmful activity. The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset, a popular benchmark for IDS research. The model performs well in both the training and validation stages, with 91.30% training accuracy and 94.38% validation accuracy. Thus, the model shows good learning and generalization capabilities with minor losses of… More >

  • Open Access

    ARTICLE

    Cloud-Edge Collaborative Federated GAN Based Data Processing for IoT-Empowered Multi-Flow Integrated Energy Aggregation Dispatch

    Zhan Shi*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 973-994, 2024, DOI:10.32604/cmc.2024.051530 - 18 July 2024

    Abstract The convergence of Internet of Things (IoT), 5G, and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing. While generative adversarial networks (GANs) are instrumental in resource scheduling, their application in this domain is impeded by challenges such as convergence speed, inferior optimality searching capability, and the inability to learn from failed decision making feedbacks. Therefore, a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges. The proposed algorithm facilitates real-time, energy-efficient data processing by More >

  • Open Access

    ARTICLE

    A Prediction-Based Multi-Objective VM Consolidation Approach for Cloud Data Centers

    Xialin Liu1,2,3,*, Junsheng Wu4, Lijun Chen2,3, Jiyuan Hu5

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1601-1631, 2024, DOI:10.32604/cmc.2024.050626 - 18 July 2024

    Abstract Virtual machine (VM) consolidation aims to run VMs on the least number of physical machines (PMs). The optimal consolidation significantly reduces energy consumption (EC), quality of service (QoS) in applications, and resource utilization. This paper proposes a prediction-based multi-objective VM consolidation approach to search for the best mapping between VMs and PMs with good timeliness and practical value. We use a hybrid model based on Auto-Regressive Integrated Moving Average (ARIMA) and Support Vector Regression (SVR) (HPAS) as a prediction model and consolidate VMs to PMs based on prediction results by HPAS, aiming at minimizing the More >

  • Open Access

    ARTICLE

    Classified VPN Network Traffic Flow Using Time Related to Artificial Neural Network

    Saad Abdalla Agaili Mohamed*, Sefer Kurnaz

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 819-841, 2024, DOI:10.32604/cmc.2024.050474 - 18 July 2024

    Abstract VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world. However, increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorize VPN network data. We present a novel VPN network traffic flow classification method utilizing Artificial Neural Networks (ANN). This paper aims to provide a reliable system that can identify a virtual private network (VPN) traffic from intrusion attempts, data exfiltration, and denial-of-service assaults. We compile a broad dataset of labeled VPN traffic flows from various apps and usage patterns. Next, we create an ANN architecture that can… More >

  • Open Access

    ARTICLE

    Automatic Rule Discovery for Data Transformation Using Fusion of Diversified Feature Formats

    G. Sunil Santhosh Kumar1,2,*, M. Rudra Kumar3

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 695-713, 2024, DOI:10.32604/cmc.2024.050143 - 18 July 2024

    Abstract This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost, a machine learning algorithm renowned for its efficiency and performance. The framework proposed herein utilizes the fusion of diversified feature formats, specifically, metadata, textual, and pattern features. The goal is to enhance the system’s ability to discern and generalize transformation rules from source to destination formats in varied contexts. Firstly, the article delves into the methodology for extracting these distinct features from raw data and the pre-processing steps undertaken to prepare the data for the model. Subsequent sections expound… More >

  • Open Access

    REVIEW

    Caching Strategies in NDN Based Wireless Ad Hoc Network: A Survey

    Ahmed Khalid1, Rana Asif Rehman1, Byung-Seo Kim2,*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 61-103, 2024, DOI:10.32604/cmc.2024.049981 - 18 July 2024

    Abstract Wireless Ad Hoc Networks consist of devices that are wirelessly connected. Mobile Ad Hoc Networks (MANETs), Internet of Things (IoT), and Vehicular Ad Hoc Networks (VANETs) are the main domains of wireless ad hoc network. Internet is used in wireless ad hoc network. Internet is based on Transmission Control Protocol (TCP)/Internet Protocol (IP) network where clients and servers interact with each other with the help of IP in a pre-defined environment. Internet fetches data from a fixed location. Data redundancy, mobility, and location dependency are the main issues of the IP network paradigm. All these… More >

  • Open Access

    ARTICLE

    Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model

    Qin Qian1, Mingjing Lu1,2,*, Anhai Zhong1, Feng Yang1, Wenjun He1, Min Li1

    Energy Engineering, Vol.121, No.8, pp. 2167-2190, 2024, DOI:10.32604/ee.2024.049430 - 19 July 2024

    Abstract The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics, engineering quality, and well conditions. These relationships, nonlinear in nature, pose challenges for accurate description through physical models. While field data provides insights into real-world effects, its limited volume and quality restrict its utility. Complementing this, numerical simulation models offer effective support. To harness the strengths of both data-driven and model-driven approaches, this study established a shale oil production capacity prediction model based on a machine learning combination model. Leveraging fracturing development data from 236 wells… More >

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