Open Access

ARTICLE

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 >

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ARTICLE

Muhammad S. Alam^1,5,*, Farhan B. Mohamed^1,3, Ali Selamat², Faruk Ahmed⁴, AKM B. Hossain^6,7
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 >

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REVIEW

Darakhshan Syed^*, Ghulam Muhammad, Safdar Rizvi
Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 437-476, 2024, DOI:10.32604/iasc.2024.050681
Abstract Cloud Computing has the ability to provide on-demand access to a shared resource pool. It has completely changed the way businesses are managed, implement applications, and provide services. The rise in popularity has led to a significant increase in the user demand for services. However, in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization. This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms. Specifically, metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic. More >

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ARTICLE

Umer Nauman¹, Yuhong Zhang², Zhihui Li³, Tong Zhen^1,3,*
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 >

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ARTICLE

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 >

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ARTICLE

Jiawei Qin^1,2, Hua Zhang^1,*, 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
（This article belongs to the Special Issue: AI Powered Human-centric Computing with Cloud/Fog/Edge)
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 >

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100

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ARTICLE

Asad Raza^1,*, 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 >

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ARTICLE

Badriyya B. Al-onazi¹, Jaber S. Alzahrani², Najm Alotaibi³, Hussain Alshahrani⁴, Mohamed Ahmed Elfaki⁴, Radwa Marzouk⁵, Heba Mohsen⁶, Abdelwahed Motwakel^7,*
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 >

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ARTICLE

Youshen Jiang¹, Tongqing Zhou¹, Zhilin Wang², Zhiping Cai^1,*, 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 >

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