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  • Open Access


    Identification and Visualization of Spatial and Temporal Trends in Textile Industry

    Umair Yousaf1, Muhammad Asif1, Shahbaz Ahmed1, Noman Tahir1, Azeem Irshad2, Akber Abid Gardezi3, Muhammad Shafiq4,*, Jin-Ghoo Choi4, Habib Hamam5,6,7,8

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4165-4181, 2023, DOI:10.32604/cmc.2023.026607

    Abstract The research volume increases at the study rate, causing massive text corpora. Due to these enormous text corpora, we are drowning in data and starving for information. Therefore, recent research employed different text mining approaches to extract information from this text corpus. These proposed approaches extract meaningful and precise phrases that effectively describe the text's information. These extracted phrases are commonly termed keyphrases. Further, these key phrases are employed to determine the different fields of study trends. Moreover, these key phrases can also be used to determine the spatiotemporal trends in the various research fields. In this research, the progress… More >

  • Open Access


    Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

    Kisaezehra1, Muhammad Umer Farooq1,*, Muhammad Aslam Bhutto2, Abdul Karim Kazi1

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 911-927, 2023, DOI:10.32604/iasc.2023.031359

    Abstract The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety (OHS) is of prime importance. Like in other developing countries, this industry pays very little, rather negligible attention to OHS practices in Pakistan, resulting in the occurrence of a wide variety of accidents, mishaps, and near-misses every year. One of the major causes of such mishaps is the non-wearing of safety helmets (hard hats) at construction sites where falling objects from a height are unavoidable. In most cases, this leads to serious brain injuries in people present at… More >

  • Open Access


    Arithmetic Optimization with Deep Learning Enabled Churn Prediction Model for Telecommunication Industries

    Vani Haridasan*, Kavitha Muthukumaran, K. Hariharanath

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3531-3544, 2023, DOI:10.32604/iasc.2023.030628

    Abstract Customer retention is one of the challenging issues in different business sectors, and various firms utilize customer churn prediction (CCP) process to retain existing customers. Because of the direct impact on the company revenues, particularly in the telecommunication sector, firms are needed to design effective CCP models. The recent advances in machine learning (ML) and deep learning (DL) models enable researchers to introduce accurate CCP models in the telecommunication sector. CCP can be considered as a classification problem, which aims to classify the customer into churners and non-churners. With this motivation, this article focuses on designing an arithmetic optimization algorithm… More >

  • Open Access


    Automotive Service Quality Investigation Using a Grey-DEMATEL Model

    Phi-Hung Nguyen*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4779-4800, 2022, DOI:10.32604/cmc.2022.030745

    Abstract In today’s fast-challenging business environment, automobile manufacturers are required to supply customers with high-quality vehicles at competitive prices. However, existing research on factors influencing service quality lacks a detailed and systematic understanding, and there is no consensus study on causal relationship and measuring the weights of service quality factors in the automotive manufacturing industry. This study provides an integrated technique for evaluating the automotive service quality in the context of VinFast-the Vietnamese leading brand. First, the Grey Theory System (GTS) is utilized to estimate the subjective views of the decision maker (DM) and overcome incomplete and vague decision information. Then,… More >

  • Open Access


    Mutated Leader Sine-Cosine Algorithm for Secure Smart IoT-Blockchain of Industry 4.0

    Mustufa Haider Abidi*, Hisham Alkhalefah, Muneer Khan Mohammed

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5367-5383, 2022, DOI:10.32604/cmc.2022.030018

    Abstract In modern scenarios, Industry 4.0 entails invention with various advanced technology, and blockchain is one among them. Blockchains are incorporated to enhance privacy, data transparency as well as security for both large and small scale enterprises. Industry 4.0 is considered as a new synthesis fabrication technique that permits the manufacturers to attain their target effectively. However, because numerous devices and machines are involved, data security and privacy are always concerns. To achieve intelligence in Industry 4.0, blockchain technologies can overcome potential cybersecurity constraints. Nowadays, the blockchain and internet of things (IoT) are gaining more attention because of their favorable outcome… More >

  • Open Access


    Performance Evaluation of Food and Beverage Listed Companies in Vietnam

    Jung-Fa Tsai1, Ngoc Huyen Nguyen1, Ming-Hua Lin2,*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3575-3593, 2022, DOI:10.32604/cmc.2022.030476

    Abstract During the last decade, the food and beverage industry has been one of the most significant and prioritized industries that contributed to the economic growth in Vietnam. Therefore, how to enhance the performance of food and beverage firms has become a critical factor for Vietnam’s economic development. This research aims to use the data envelopment analysis (DEA) and the Malmquist productivity index (MPI) to assess changes in operational performance and productivity of listed lead food and beverage firms in Vietnam during the period between 2015 and 2020. The obtained results reveal that Vietnamese food and beverage firms were generally inefficient… More >

  • Open Access


    Intelligent Intrusion Detection System for Industrial Internet of Things Environment

    R. Gopi1, R. Sheeba2, K. Anguraj3, T. Chelladurai4, Haya Mesfer Alshahrani5, Nadhem Nemri6,*, Tarek Lamoudan7

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1567-1582, 2023, DOI:10.32604/csse.2023.025216

    Abstract Rapid increase in the large quantity of industrial data, Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation, data sensing and collection, real-time data processing, and high request arrival rates. The classical intrusion detection system (IDS) is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity. To resolve these issues, this paper designs a new Chaotic Cuckoo Search Optimization Algorithm (CCSOA) with optimal wavelet kernel extreme learning machine (OWKELM) named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform. The CCSOA-OWKELM technique focuses on the design of feature selection with classification… More >

  • Open Access


    Behavior of Delivery Robot in Human-Robot Collaborative Spaces During Navigation

    Kiran Jot Singh1, Divneet Singh Kapoor1,*, Mohamed Abouhawwash2,3, Jehad F. Al-Amri4, Shubham Mahajan5, Amit Kant Pandit5

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 795-810, 2023, DOI:10.32604/iasc.2023.025177

    Abstract Navigation is an essential skill for robots. It becomes a cumbersome task for the robot in a human-populated environment, and Industry 5.0 is an emerging trend that focuses on the interaction between humans and robots. Robot behavior in a social setting is the key to human acceptance while ensuring human comfort and safety. With the advancement in robotics technology, the true use cases of robots in the tourism and hospitality industry are expanding in number. There are very few experimental studies focusing on how people perceive the navigation behavior of a delivery robot. A robotic platform named “PI” has been… More >

  • Open Access


    Deep Convolutional Neural Network Based Churn Prediction for Telecommunication Industry

    Nasebah Almufadi1, Ali Mustafa Qamar1,2,*

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1255-1270, 2022, DOI:10.32604/csse.2022.025029

    Abstract Currently, mobile communication is one of the widely used means of communication. Nevertheless, it is quite challenging for a telecommunication company to attract new customers. The recent concept of mobile number portability has also aggravated the problem of customer churn. Companies need to identify beforehand the customers, who could potentially churn out to the competitors. In the telecommunication industry, such identification could be done based on call detail records. This research presents an extensive experimental study based on various deep learning models, such as the 1D convolutional neural network (CNN) model along with the recurrent neural network (RNN) and deep… More >

  • Open Access


    Crack Detection in Composite Materials Using McrowDNN

    R. Saveeth1,*, S. Uma Maheswari2

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 983-1000, 2022, DOI:10.32604/iasc.2022.023455

    Abstract In the aerospace industry, composite materials are becoming more common. The presence of a crack in an aircraft makes it weaker and more dangerous, and it can lead to complete fracture and catastrophic failure. To predict the position and depth of a crack, various methods have been developed. For aircraft repair, crack diagnosis is extremely important. Even then, due to uncertainties arising from sources such as environmental conditions, packing, and intrinsic material property changes, accurate diagnosis in real engineering applications remains a challenge. Deep learning (DL) approaches have demonstrated powerful recognition potential in a variety of fields in recent years.… More >

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