Hongxin Luo, Xiaobing Zhou^*, Haiyan Ding, Liqing Wang

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3583-3593, 2023, DOI:10.32604/iasc.2023.036622

Abstract Online advertising has gained much attention on various platforms as a hugely lucrative market. In promoting content and advertisements in real life, the acquisition of user target actions is usually a multi-step process, such as impression→click→conversion, which means the process from the delivery of the recommended item to the user’s click to the final conversion. Due to data sparsity or sample selection bias, it is difficult for the trained model to achieve the business goal of the target campaign. Multi-task learning, a classical solution to this problem, aims to generalize better on the original task given several related tasks by… More >

Narit Hnoohom¹, Sakorn Mekruksavanich², Anuchit Jitpattanakul^3,4,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3371-3385, 2023, DOI:10.32604/iasc.2023.036551

Abstract Falls are the contributing factor to both fatal and nonfatal injuries in the elderly. Therefore, pre-impact fall detection, which identifies a fall before the body collides with the floor, would be essential. Recently, researchers have turned their attention from post-impact fall detection to pre-impact fall detection. Pre-impact fall detection solutions typically use either a threshold-based or machine learning-based approach, although the threshold value would be difficult to accurately determine in threshold-based methods. Moreover, while additional features could sometimes assist in categorizing falls and non-falls more precisely, the estimated determination of the significant features would be too time-intensive, thus using a… More >

Waleed Akbar, Afaq Muhammad, Wang-Cheol Song^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3743-3759, 2023, DOI:10.32604/iasc.2023.036440

Abstract Data is growing quickly due to a significant increase in social media applications. Today, billions of people use an enormous amount of data to access the Internet. The backbone network experiences a substantial load as a result of an increase in users. Users in the same region or company frequently ask for similar material, especially on social media platforms. The subsequent request for the same content can be satisfied from the edge if stored in proximity to the user. Applications that require relatively low latency can use Content Delivery Network (CDN) technology to meet their requirements. An edge and the… More >

Fadwa Alrowais¹, Radwa Marzouk^2,3, Fahd N. Al-Wesabi^4,*, Anwer Mustafa Hilal⁵

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3325-3342, 2023, DOI:10.32604/iasc.2023.036354

Abstract Sign language recognition can be treated as one of the efficient solutions for disabled people to communicate with others. It helps them to convey the required data by the use of sign language with no issues. The latest developments in computer vision and image processing techniques can be accurately utilized for the sign recognition process by disabled people. American Sign Language (ASL) detection was challenging because of the enhancing intraclass similarity and higher complexity. This article develops a new Bayesian Optimization with Deep Learning-Driven Hand Gesture Recognition Based Sign Language Communication (BODL-HGRSLC) for Disabled People. The BODL-HGRSLC technique aims to… More >

Haijian Shao¹, Edwin Ma², Ming Zhu¹, Xing Deng³, Shengjie Zhai^1,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3595-3606, 2023, DOI:10.32604/iasc.2023.036323

Abstract Accurate handwriting recognition has been a challenging computer vision problem, because static feature analysis of the text pictures is often inadequate to account for high variance in handwriting styles across people and poor image quality of the handwritten text. Recently, by introducing machine learning, especially convolutional neural networks (CNNs), the recognition accuracy of various handwriting patterns is steadily improved. In this paper, a deep CNN model is developed to further improve the recognition rate of the MNIST handwritten digit dataset with a fast-converging rate in training. The proposed model comes with a multi-layer deep arrange structure, including 3 convolution and… More >

Samah Alhazmi^1,*, Shahnawaz Khan², Mohammad Haider Syed¹

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3487-3499, 2023, DOI:10.32604/iasc.2023.036297

Abstract Quality education is one of the primary objectives of any nation-building strategy and is one of the seventeen Sustainable Development Goals (SDGs) by the United Nations. To provide quality education, delivering top-quality content is not enough. However, understanding the learners’ emotions during the learning process is equally important. However, most of this research work uses general data accessed from Twitter or other publicly available databases. These databases are generally not an ideal representation of the actual learning process and the learners’ sentiments about the learning process. This research has collected real data from the learners, mainly undergraduate university students of… More >

Richard Cruz¹, Johnson Kinyua^1,*, Charles Mutigwe²

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3405-3423, 2023, DOI:10.32604/iasc.2023.035906

Abstract The massive increase in the volume of data generated by individuals on social media microblog platforms such as Twitter and Reddit every day offers researchers unique opportunities to analyze financial markets from new perspectives. The meme stock mania of 2021 brought together stock traders and investors that were also active on social media. This mania was in good part driven by retail investors’ discussions on investment strategies that occurred on social media platforms such as Reddit during the COVID-19 lockdowns. The stock trades by these retail investors were then executed using services like Robinhood. In this paper, machine learning models… More >

Abdelwahed Motwakel^1,*, Fadwa Alrowais², Khaled Tarmissi³, Radwa Marzouk⁴, Abdullah Mohamed⁵, Abu Sarwar Zamani¹, Ishfaq Yaseen¹, Mohamed I. Eldesouki⁶

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3157-3173, 2023, DOI:10.32604/iasc.2023.034908

Abstract The paradigm shift towards the Internet of Things (IoT) phenomenon and the rise of edge-computing models provide massive potential for several upcoming IoT applications like smart grid, smart energy, smart home, smart health and smart transportation services. However, it also provides a sequence of novel cyber-security issues. Although IoT networks provide several advantages, the heterogeneous nature of the network and the wide connectivity of the devices make the network easy for cyber-attackers. Cyberattacks result in financial loss and data breaches for organizations and individuals. So, it becomes crucial to secure the IoT environment from such cyberattacks. With this motivation, the… More >

Hala J. Alshahrani¹, Khaled Tarmissi², Ayman Yafoz³, Abdullah Mohamed⁴, Manar Ahmed Hamza^5,*, Ishfaq Yaseen⁵, Abu Sarwar Zamani⁵, Mohammad Mahzari⁶

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3203-3219, 2023, DOI:10.32604/iasc.2023.034848

Abstract The term ‘executed linguistics’ corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related problems. The exponential generation of text data on the Internet must be leveraged to gain knowledgeable insights. The extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and end-users. The Automatic Text Summarization (ATS) process reduces the primary size of the text without losing any basic components of the data. The current study introduces an Applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning (ALTS-MLODL) model. The… More >

Hala J. Alshahrani¹, Khaled Tarmissi², Ayman Yafoz³, Abdullah Mohamed⁴, Abdelwahed Motwakel^5,*, Ishfaq Yaseen⁵, Amgad Atta Abdelmageed⁵, Mohammad Mahzari⁶

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3139-3155, 2023, DOI:10.32604/iasc.2023.034841

Abstract Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based concerns. Emails stay in the leading positions for business as well as personal use. This popularity grabs the interest of individuals with malevolent intentions—phishing and spam email assaults. Email filtering mechanisms were developed incessantly to follow unwanted, malicious content advancement to protect the end-users. But prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully advanced. Thus, this study provides a solution related to email message body text automatic classification into phishing… More >