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  • Open Access

    ARTICLE

    Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

    Muhammad S. Alam1,5,*, Farhan B. Mohamed1,3, Ali Selamat2, Faruk Ahmed4, AKM B. Hossain6,7

    Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 417-436, 2024, DOI:10.32604/iasc.2024.051999

    Abstract Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems. The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed. This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence. An annotated image dataset trains the proposed system and predicts the camera pose in real-time. The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera More >

  • Open Access

    ARTICLE

    Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

    Asad Raza1,*, Shahzad Memon1, Muhammad Ali Nizamani1, Mahmood Hussain Shah2

    Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 545-566, 2024, DOI:10.32604/iasc.2024.051779

    Abstract Smart Industrial environments use the Industrial Internet of Things (IIoT) for their routine operations and transform their industrial operations with intelligent and driven approaches. However, IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet. Traditional signature-based IDS are effective in detecting known attacks, but they are unable to detect unknown emerging attacks. Therefore, there is the need for an IDS which can learn from data and detect new threats. Ensemble Machine Learning (ML) and individual Deep Learning (DL) based IDS have been developed, and these individual models achieved… More >

  • Open Access

    ARTICLE

    Systematic Cloud-Based Optimization: Twin-Fold Moth Flame Algorithm for VM Deployment and Load-Balancing

    Umer Nauman1, Yuhong Zhang2, Zhihui Li3, Tong Zhen1,3,*

    Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 477-510, 2024, DOI:10.32604/iasc.2024.050726

    Abstract Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications. Nevertheless, existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets, such as preservation and server infrastructure, in a limited number of large-scale worldwide data facilities. Optimizing the deployment of virtual machines (VMs) is crucial in this scenario to ensure system dependability, performance, and minimal latency. A significant barrier in the present scenario is the load distribution, particularly when striving for improved energy consumption in a hypothetical grid computing framework. This design… More >

  • Open Access

    ARTICLE

    BArcherFuzzer: An Android System Services Fuzzier via Transaction Dependencies of BpBinder

    Jiawei Qin1,2, Hua Zhang1,*, Hanbing Yan2, Tian Zhu2, Song Hu1, Dingyu Yan2

    Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 527-544, 2024, DOI:10.32604/iasc.2024.047509

    Abstract By the analysis of vulnerabilities of Android native system services, we find that some vulnerabilities are caused by inconsistent data transmission and inconsistent data processing logic between client and server. The existing research cannot find the above two types of vulnerabilities and the test cases of them face the problem of low coverage. In this paper, we propose an extraction method of test cases based on the native system services of the client and design a case construction method that supports multi-parameter mutation based on genetic algorithm and priority strategy. Based on the above method, More >

  • Open Access

    ARTICLE

    Enhanced Arithmetic Optimization Algorithm Guided by a Local Search for the Feature Selection Problem

    Sana Jawarneh*

    Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 511-525, 2024, DOI:10.32604/iasc.2024.047126

    Abstract High-dimensional datasets present significant challenges for classification tasks. Dimensionality reduction, a crucial aspect of data preprocessing, has gained substantial attention due to its ability to improve classification performance. However, identifying the optimal features within high-dimensional datasets remains a computationally demanding task, necessitating the use of efficient algorithms. This paper introduces the Arithmetic Optimization Algorithm (AOA), a novel approach for finding the optimal feature subset. AOA is specifically modified to address feature selection problems based on a transfer function. Additionally, two enhancements are incorporated into the AOA algorithm to overcome limitations such as limited precision, slow More >

  • Open Access

    ARTICLE

    Improving VQA via Dual-Level Feature Embedding Network

    Yaru Song*, Huahu Xu, Dikai Fang

    Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 397-416, 2024, DOI:10.32604/iasc.2023.040521

    Abstract Visual Question Answering (VQA) has sparked widespread interest as a crucial task in integrating vision and language. VQA primarily uses attention mechanisms to effectively answer questions to associate relevant visual regions with input questions. The detection-based features extracted by the object detection network aim to acquire the visual attention distribution on a predetermined detection frame and provide object-level insights to answer questions about foreground objects more effectively. However, it cannot answer the question about the background forms without detection boxes due to the lack of fine-grained details, which is the advantage of grid-based features. In… More >

  • Open Access

    ARTICLE

    Chaotic Elephant Herd Optimization with Machine Learning for Arabic Hate Speech Detection

    Badriyya B. Al-onazi1, Jaber S. Alzahrani2, Najm Alotaibi3, Hussain Alshahrani4, Mohamed Ahmed Elfaki4, Radwa Marzouk5, Heba Mohsen6, Abdelwahed Motwakel7,*

    Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 567-583, 2024, DOI:10.32604/iasc.2023.033835

    Abstract In recent years, the usage of social networking sites has considerably increased in the Arab world. It has empowered individuals to express their opinions, especially in politics. Furthermore, various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different scales. This is attributed to business owners’ understanding of social media’s importance for business development. However, the Arabic morphology is too complicated to understand due to the availability of nearly 10,000 roots and more than 900 patterns that act as the basis for verbs and nouns. Hate speech… More >

  • Open Access

    ARTICLE

    Multi-Scale Location Attention Model for Spatio-Temporal Prediction of Disease Incidence

    Youshen Jiang1, Tongqing Zhou1, Zhilin Wang2, Zhiping Cai1,*, Qiang Ni3

    Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 585-597, 2024, DOI:10.32604/iasc.2023.030221

    Abstract Due to the increasingly severe challenges brought by various epidemic diseases, people urgently need intelligent outbreak trend prediction. Predicting disease onset is very important to assist decision-making. Most of the existing work fails to make full use of the temporal and spatial characteristics of epidemics, and also relies on multivariate data for prediction. In this paper, we propose a Multi-Scale Location Attention Graph Neural Networks (MSLAGNN) based on a large number of Centers for Disease Control and Prevention (CDC) patient electronic medical records research sequence source data sets. In order to understand the geography and… More >

  • Open Access

    ARTICLE

    Performance Study of Dynamic Intake and Exhaust Façades in Hot and Dry Climates: Iraq Case Study

    S. M. Hosseinalipour*, S. Asiaei*, Ammar A. Hussain Al-Taee

    Frontiers in Heat and Mass Transfer, Vol.22, No.3, pp. 747-767, 2024, DOI:10.32604/fhmt.2024.051541

    Abstract This paper is part of a series addressing the urgent need for effective technologies to reduce energy demand and mitigate climate impact. This study focused on the implementation and development of dynamic insulation technology for a sustainable and energy-efficient future in the region, especially in Iraq. The study assessed the energy efficiency of dynamic insulation technology by analyzing three wall models (static, dynamic, and modified) during the winter season. This paper expands the analysis to include a hot, dry summer scenario, providing valuable insights into the year-round performance of dynamic walls and enabling sustainable and More >

  • Open Access

    ARTICLE

    Experimental Study on Improving Performance and Productivity of Pyramid Solar Still Using Rotation Technique

    Ali Abdullah Abbas Baiee, Sasan Asiaei*, Sayed Mostafa Hosseinalipour*

    Frontiers in Heat and Mass Transfer, Vol.22, No.3, pp. 955-976, 2024, DOI:10.32604/fhmt.2024.051532

    Abstract Globally, potable water scarcity is pervasive problem. The solar distillation device is a straightforward apparatus that has been purposefully engineered to convert non-potable water into potable water. The experimental study is distinctive due to the implementation of a rotational mechanism within the pyramidal solar still (PSS), which serves to enhance the evaporation and condensation processes. The objective of this research study is to examine the impact of integrating rotational motion into pyramidal solar stills on various processes: water distillation, evaporation, condensation, heat transfer, and energy waste reduction, shadow effects, and low water temperature in saline… More >

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