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  • Open Access

    ARTICLE

    Multi-Attribute Selection Procedures Based on Regret and Rejoice for the Decision-Maker

    Hanan Abdullah Mengash*, Manel Farouk Ayadi

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2777-2795, 2022, DOI:10.32604/cmc.2022.015434

    Abstract Feelings influence human beings’ decision-making; therefore, incorporation of feeling factors in decision-making is very important. Regret and rejoice are very important emotional feelings that can have a great impact on decision-making if they are considered together. While regret has received most of the attention in related research, rejoice has been less considered even though it can greatly influence people’s preferences in decision-making. Furthermore, systematically incorporating regret and rejoice in the multi-criteria decision-making (MCDM) modeling frameworks for decision-making has received little research attention. In this paper, we introduce a new multi-attribute selection procedure that incorporates both regret and rejoice to select… More >

  • Open Access

    ARTICLE

    Deep Learning Based License Plate Number Recognition for Smart Cities

    T. Vetriselvi1, E. Laxmi Lydia2, Sachi Nandan Mohanty3,4, Eatedal Alabdulkreem5, Shaha Al-Otaibi6, Amal Al-Rasheed6, Romany F. Mansour7,*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 2049-2064, 2022, DOI:10.32604/cmc.2022.020110

    Abstract Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective. Precise controlling and management of traffic conditions, increased safety and surveillance, and enhanced incident avoidance and management should be top priorities in smart city management. At the same time, Vehicle License Plate Number Recognition (VLPNR) has become a hot research topic, owing to several real-time applications like automated toll fee processing, traffic law enforcement, private space access control, and road traffic surveillance. Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates.… More >

  • Open Access

    ARTICLE

    Secure Rotation Invariant Face Detection System for Authentication

    Amit Verma1, Mohammed Baljon2, Shailendra Mishra2,*, Iqbaldeep Kaur1, Ritika Saini1, Sharad Saxena3, Sanjay Kumar Sharma4

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1955-1974, 2022, DOI:10.32604/cmc.2022.020084

    Abstract Biometric applications widely use the face as a component for recognition and automatic detection. Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and rotation. This problem has been investigated, and a novice algorithm, namely RIFDS (Rotation Invariant Face Detection System), has been devised. The objective of the paper is to implement a robust method for face detection taken at various angle. Further to achieve better results than known algorithms for face detection. In RIFDS Polar Harmonic Transforms (PHT) technique is combined with Multi-Block Local Binary Pattern (MBLBP) in a hybrid… More >

  • Open Access

    ARTICLE

    Data Traffic Reduction with Compressed Sensing in an AIoT System

    Hye-Min Kwon1, Seng-Phil Hong2, Mingoo Kang1, Jeongwook Seo1,*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1769-1780, 2022, DOI:10.32604/cmc.2022.020027

    Abstract To provide Artificial Intelligence (AI) services such as object detection, Internet of Things (IoT) sensor devices should be able to send a large amount of data such as images and videos. However, this inevitably causes IoT networks to be severely overloaded. In this paper, therefore, we propose a novel oneM2M-compliant Artificial Intelligence of Things (AIoT) system for reducing overall data traffic and offering object detection. It consists of some IoT sensor devices with random sampling functions controlled by a compressed sensing (CS) rate, an IoT edge gateway with CS recovery and domain transform functions related to compressed sensing, and a… More >

  • Open Access

    ARTICLE

    A Novel Database Watermarking Technique Using Blockchain as Trusted Third Party

    Ahmed S. Alghamdi1, Surayya Naz2, Ammar Saeed3, Eesa Al Solami1, Muhammad Kamran1,*, Mohammed Saeed Alkatheiri1

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1585-1601, 2022, DOI:10.32604/cmc.2022.019936

    Abstract With widespread use of relational database in various real-life applications, maintaining integrity and providing copyright protection is gaining keen interest of the researchers. For this purpose, watermarking has been used for quite a long time. Watermarking requires the role of trusted third party and a mechanism to extract digital signatures (watermark) to prove the ownership of the data under dispute. This is often inefficient as lots of processing is required. Moreover, certain malicious attacks, like additive attacks, can give rise to a situation when more than one parties can claim the ownership of the same data by inserting and detecting… More >

  • Open Access

    ARTICLE

    An Efficient Energy Aware Routing Mechanism for Wireless Body Area Networks

    Wejdan Wasel Aljaghthami*, Mohammad Haseeb Zafar, Afraa Zuhair Attiah

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1111-1126, 2022, DOI:10.32604/cmc.2022.019912

    Abstract The accelerated development of wireless network technology has resulted in the emergence of Wireless Body Area Network (WBAN), which is a technology commonly used in the medical field. WBAN consists of tiny sensor nodes that interconnect with each other and set in the human body to collect and transmit the patient data to the physician, to monitor the patients remotely. These nodes typically have limited battery energy that led to a shortage of network lifetime. Therefore, energy efficiency is considered one of the most demanding challenges in routing design for WBAN. Many proposed routing mechanisms in WBAN did not cover… More >

  • Open Access

    ARTICLE

    Two-Stage Production Planning Under Stochastic Demand: Case Study of Fertilizer Manufacturing

    Chia-Nan Wang1, Shao-Dong Syu1,2,*, Chien-Chang Chou3, Viet Tinh Nguyen4, Dang Van Thuy Cuc5

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1195-1207, 2022, DOI:10.32604/cmc.2022.019890

    Abstract Agriculture is a key facilitator of economic prosperity and nourishes the huge global population. To achieve sustainable agriculture, several factors should be considered, such as increasing nutrient and water efficiency and/or improving soil health and quality. Using fertilizer is one of the fastest and easiest ways to improve the quality of nutrients inland and increase the effectiveness of crop yields. Fertilizer supplies most of the necessary nutrients for plants, and it is estimated that at least 30%–50% of crop yields is attributable to commercial fertilizer nutrient inputs. Fertilizer is always a major concern in achieving sustainable and efficient agriculture. Applying… More >

  • Open Access

    ARTICLE

    Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease

    A. Sheryl Oliver1, P. Suresh2, A. Mohanarathinam3, Seifedine Kadry4, Orawit Thinnukool5,*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 2031-2047, 2022, DOI:10.32604/cmc.2022.019876

    Abstract Early diagnosis and detection are important tasks in controlling the spread of COVID-19. A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays. However, these methods suffer from biased results and inaccurate detection of the disease. So, the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network (OCOA-DDCNN) for COVID-19 prediction using CT images in IoT environment. The proposed methodology works on the basis of two stages such as pre-processing and prediction. Initially, CT scan images generated from prospective COVID-19 are collected… More >

  • Open Access

    ARTICLE

    Improving Supply Chain Performance Through Supplier Selection and Order Allocation Problem

    Chia-Nan Wang1, Ming-Cheng Tsou2,*, Chih-Hung Wang3, Viet Tinh Nguyen4, Pham Ngo Thi Phuong5

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1667-1681, 2022, DOI:10.32604/cmc.2022.019833

    Abstract Suppliers play the vital role of ensuring the continuous supply of goods to the market for businesses. If businesses do not maintain a strong bond with their suppliers, they may not be able to secure a steady supply of goods and products for their customers. As a result of failure to deliver products, the production and business activities of the business can be delayed which leads to the loss of customers. Normally, each trading enterprise will have a variety of commodity supply chains with multiple suppliers. Suppliers play an important role and contribute to the value of the entire supply… More >

  • Open Access

    ARTICLE

    A Transfer Learning-Enabled Optimized Extreme Deep Learning Paradigm for Diagnosis of COVID-19

    Ahmed Reda*, Sherif Barakat, Amira Rezk

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1381-1399, 2022, DOI:10.32604/cmc.2022.019809

    Abstract Many respiratory infections around the world have been caused by coronaviruses. COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate. There is a high need for computer-assisted diagnostics (CAD) in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud systems. Machine learning (ML) has been used to examine chest X-ray frames. In this paper, a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes, a pneumonia patient, a COVID-19 patient, or a normal… More >

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