Kathiravan Srinivasan¹, Lalit Garg^2,*, Bor-Yann Chen³, Abdulellah A. Alaboudi⁴, N. Z. Jhanjhi⁵, Chang-Tang Chang⁶, B. Prabadevi¹, N. Deepa¹

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 17-30, 2021, DOI:10.32604/iasc.2021.018380

Abstract Expert Systems are interactive and reliable computer-based decision-making systems that use both facts and heuristics for solving complex decision-making problems. Generally, the cyclic voltammetry (CV) experiments are executed a random number of times (cycles) to get a stable production of power. However, presently there are not many algorithms or models for predicting the power generation stable criteria in microbial fuel cells. For stability analysis of Medicinal herbs’ CV profiles, an expert system driven by the augmented K-means clustering algorithm is proposed. Our approach requires a dataset that contains voltage-current relationships from CV experiments on the related subjects (plants/herbs). This new… More >

Mohammed Baz^1,*, Hatem Zaini¹, Hala S. El-sayed², Matokah AbuAlNaja³, Heba M. El-Hoseny⁴, Osama S. Faragallah⁵

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 97-111, 2021, DOI:10.32604/iasc.2021.018265

Abstract In the era of medical technology, automatic scan detection can be considered a charming tool in medical diagnosis, especially with rapidly spreading diseases. In light of the prevalence of the current Coronavirus disease (COVID-19), which is characterized as highly contagious and very complicated, it is urgent and necessary to find a quick way that can be practically implemented for diagnosing COVID-19. The danger of the virus lies in the fact that patients can spread the disease without showing any symptoms. Moreover, several vaccines have been produced and vaccinated in large numbers but, the outbreak does not stop. Therefore, it is… More >

Si-Yi Chen¹, Gang Xu², Xiu-Bo Chen¹, Haseeb Ahmad³, Yu-Ling Chen^4,*

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 273-284, 2021, DOI:10.32604/iasc.2021.018120

Abstract Quantum network coding can effectively improve the aggregate throughput of quantum networks and alleviate bottlenecks caused by topological constraints. Most of previous schemes are dedicated to the efficient teleportation of unknown quantum states in a quantum network. Herein a proposal for transmission of deterministic known states over quantum repeater network based on quantum measurements. We show that the new protocol offers advantages over three aspects. Firstly, the senders in our protocol obtain the knowledge of the quantum state to be transmitted, which enables the autonomy of quantum network transmission. Secondly, we study the quantum repeater network coding for long-distance deterministic… More >

Azeem Shera¹, Muhammad Waseem Iqbal^2,*, Syed Khuram Shahzad³, Madeeha Gul¹, Natash Ali Mian⁴, Muhammad Raza Naqvi⁵, Babar Ayub Khan¹

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 285-301, 2021, DOI:10.32604/iasc.2021.018009

Abstract Blind and visually impaired (BVI) users often have interface accessibility problems while using mobile applications. This study was conducted to reduce the cognitive effort required for interface navigation by identifying the accessibility issues according to the user’s mental model. The study evaluated the accessibility of smartphone screens to solve organizational, presentation, and behavioral (OPB) problems of using mobile applications. Usability evaluation of an application was conducted and validated with a specific focus on BVI user experience. A total of 56 BVI participants were included in the evaluation. Overall, four tasks to assess organization, avoidance of redundant information, serialization of content,… More >

Mamoona Majid¹, Muhammad Faisal Hayat², Farrukh Zeeshan Khan³, Muneer Ahmad^4,*, NZ Jhanjhi⁵, Mohammad Arif Sobhan Bhuiyan⁶, Mehedi Masud⁷, Mohammed A. AlZain⁸

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 373-386, 2021, DOI:10.32604/iasc.2021.017840

Abstract Choosing the right university program can be very challenging for students. This is especially the case in developing countries such as India and Pakistan, where university admission depends on not only the program of interest but also other factors such as the candidate’s financial standing. Since information on the Internet can be highly scattered, university candidates often need counseling from qualified people to decide their educational programs. Traditional database systems cannot effectively organize the large unstructured data related to university programs. It is challenging, then, for prospective students to acquire the information needed to make good decisions to consider factors… More >

Hao Wu^1,*, B. Nagaraj²

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 81-95, 2021, DOI:10.32604/iasc.2021.017654

Abstract English teaching has always attracted much attention. However, the processes of its transmission and acquirement is often divided into two separate parts, which seriously hinders the effective implementation of its objectives. Teachers attach particular importance to the choice of the curriculum structure and teaching material. Students are busy comprehending the assignments their teachers deem important. Under such a scenario, the effective acquisition of knowledge and the development of sustainable comprehensive abilities are ignored. The random forest algorithm in machine learning applications could play important role improving on the current English teaching system. A random forest model is constructed using a… More >

Qin Zhang¹, Xuyu Xiang^1,*, Jiaohua Qin¹, Yun Tan¹, Qiang Liu¹, Neal N. Xiong²

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 227-241, 2021, DOI:10.32604/iasc.2021.017648

Abstract With the rapid development of network media, the short text has become the main cover of information dissemination by quickly disseminating relevant entity information. However, the lack of context in the short text can easily lead to ambiguity, which will greatly reduce the efficiency of obtaining information and seriously affect the user’s experience, especially in the financial field. This paper proposed an entity disambiguation algorithm based on multi-word vector ensemble and decision to eliminate the ambiguity of entities and purify text information in information processing. First of all, we integrate a variety of unsupervised pre-trained word vector models as vector… More >

K. Kalaivani^*, M. Chinnadurai

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 1-15, 2021, DOI:10.32604/iasc.2021.017515

Abstract Fog computing extends the concept of cloud computing by providing the services of computing, storage, and networking connectivity at the edge between data centers in cloud computing environments and end devices. Having the intelligence at the edge enables faster real-time decision-making and reduces the amount of data forwarded to the cloud. When enhanced by fog computing, the Internet of Things (IoT) brings low latency and improves real time and quality of service (QoS) in IoT applications of augmented reality, smart grids, smart vehicles, and healthcare. However, both cloud and fog computing environments are vulnerable to several kinds of attacks that… More >

Ghassan Ahmed Ali^*

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 351-360, 2021, DOI:10.32604/iasc.2021.017453

Abstract Many living organisms respond to pandemics using strategies such as isolation. This is true, for example, of social insects, for whom the spread of disease can pose a high risk to colony survival. In light of such behaviors, the present study investigated a different way of developing strategies to mitigate the effects of the coronavirus pandemic. Specifically, we considered the strategies ants use to handle epidemics and limit disease spread within colonies. To enhance our understanding of these strategies, we explored ants’ social systems and how they specifically respond to infectious diseases. The early warning threshold system reflects the importance… More >

Gaigai Jin¹, Junsheng Zhou^1,*, Weiguang Qu¹, Yunfei Long², Yanhui Gu¹

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 161-173, 2021, DOI:10.32604/iasc.2021.017440

Abstract Event causality identification is an essential task for information extraction that has attracted growing attention. Early researchers were accustomed to combining the convolutional neural network or recurrent neural network models with external causal knowledge, but these methods ignore the importance of rich semantic representation of the event. The event is more structured, so it has more abundant semantic representation. We argue that the elements of the event, the interaction of the two events, and the context between the two events can enrich the event’s semantic representation and help identify event causality. Therefore, the effective semantic representation of events in event… More >