Home / Journals / CMC / Vol.75, No.3, 2023
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  • Open AccessOpen Access

    ARTICLE

    Fire Detection Algorithm Based on an Improved Strategy of YOLOv5 and Flame Threshold Segmentation

    Yuchen Zhao, Shulei Wu*, Yaoru Wang, Huandong Chen*, Xianyao Zhang, Hongwei Zhao
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5639-5657, 2023, DOI:10.32604/cmc.2023.037829 - 29 April 2023
    Abstract Due to the rapid growth and spread of fire, it poses a major threat to human life and property. Timely use of fire detection technology can reduce disaster losses. The traditional threshold segmentation method is unstable, and the flame recognition methods of deep learning require a large amount of labeled data for training. In order to solve these problems, this paper proposes a new method combining You Only Look Once version 5 (YOLOv5) network model and improved flame segmentation algorithm. On the basis of the traditional color space threshold segmentation method, the original segmentation threshold… More >

  • Open AccessOpen Access

    ARTICLE

    Biometric Finger Vein Recognition Using Evolutionary Algorithm with Deep Learning

    Mohammad Yamin1,*, Tom Gedeon2, Saleh Bajaba3, Mona M. Abusurrah4
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5659-5674, 2023, DOI:10.32604/cmc.2023.034005 - 29 April 2023
    Abstract In recent years, the demand for biometric-based human recognition methods has drastically increased to meet the privacy and security requirements. Palm prints, palm veins, finger veins, fingerprints, hand veins and other anatomic and behavioral features are utilized in the development of different biometric recognition techniques. Amongst the available biometric recognition techniques, Finger Vein Recognition (FVR) is a general technique that analyzes the patterns of finger veins to authenticate the individuals. Deep Learning (DL)-based techniques have gained immense attention in the recent years, since it accomplishes excellent outcomes in various challenging domains such as computer vision,… More >

  • Open AccessOpen Access

    ARTICLE

    A Sentence Retrieval Generation Network Guided Video Captioning

    Ou Ye1,2, Mimi Wang1, Zhenhua Yu1,*, Yan Fu1, Shun Yi1, Jun Deng2
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5675-5696, 2023, DOI:10.32604/cmc.2023.037503 - 29 April 2023
    Abstract Currently, the video captioning models based on an encoder-decoder mainly rely on a single video input source. The contents of video captioning are limited since few studies employed external corpus information to guide the generation of video captioning, which is not conducive to the accurate description and understanding of video content. To address this issue, a novel video captioning method guided by a sentence retrieval generation network (ED-SRG) is proposed in this paper. First, a ResNeXt network model, an efficient convolutional network for online video understanding (ECO) model, and a long short-term memory (LSTM) network… More >

  • Open AccessOpen Access

    ARTICLE

    A Review and Analysis of Localization Techniques in Underwater Wireless Sensor Networks

    Seema Rani1, Anju1, Anupma Sangwan1, Krishna Kumar2, Kashif Nisar3, Tariq Rahim Soomro3, Ag. Asri Ag. Ibrahim4,*, Manoj Gupta5, Laxmi Chand5, Sadiq Ali Khan6
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5697-5715, 2023, DOI:10.32604/cmc.2023.033007 - 29 April 2023
    Abstract In recent years, there has been a rapid growth in Underwater Wireless Sensor Networks (UWSNs). The focus of research in this area is now on solving the problems associated with large-scale UWSN. One of the major issues in such a network is the localization of underwater nodes. Localization is required for tracking objects and detecting the target. It is also considered tagging of data where sensed contents are not found of any use without localization. This is useless for application until the position of sensed content is confirmed. This article’s major goal is to review More >

  • Open AccessOpen Access

    REVIEW

    A Systematic Literature Review of Deep Learning Algorithms for Segmentation of the COVID-19 Infection

    Shroog Alshomrani*, Muhammad Arif, Mohammed A. Al Ghamdi
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5717-5742, 2023, DOI:10.32604/cmc.2023.038059 - 29 April 2023
    Abstract Coronavirus has infected more than 753 million people, ranging in severity from one person to another, where more than six million infected people died worldwide. Computer-aided diagnostic (CAD) with artificial intelligence (AI) showed outstanding performance in effectively diagnosing this virus in real-time. Computed tomography is a complementary diagnostic tool to clarify the damage of COVID-19 in the lungs even before symptoms appear in patients. This paper conducts a systematic literature review of deep learning methods for classifying the segmentation of COVID-19 infection in the lungs. We used the methodology of systematic reviews and meta-analyses (PRISMA) More >

  • Open AccessOpen Access

    ARTICLE

    RRCNN: Request Response-Based Convolutional Neural Network for ICS Network Traffic Anomaly Detection

    Yan Du1,2, Shibin Zhang1,2,*, Guogen Wan1,2, Daohua Zhou3, Jiazhong Lu1,2, Yuanyuan Huang1,2, Xiaoman Cheng4, Yi Zhang4, Peilin He5
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5743-5759, 2023, DOI:10.32604/cmc.2023.035919 - 29 April 2023
    Abstract Nowadays, industrial control system (ICS) has begun to integrate with the Internet. While the Internet has brought convenience to ICS, it has also brought severe security concerns. Traditional ICS network traffic anomaly detection methods rely on statistical features manually extracted using the experience of network security experts. They are not aimed at the original network data, nor can they capture the potential characteristics of network packets. Therefore, the following improvements were made in this study: (1) A dataset that can be used to evaluate anomaly detection algorithms is produced, which provides raw network data. (2)… More >

  • Open AccessOpen Access

    ARTICLE

    MEB-YOLO: An Efficient Vehicle Detection Method in Complex Traffic Road Scenes

    Yingkun Song1, Shunhe Hong1, Chentao Hu1, Pingan He2, Lingbing Tao1, Zhixin Tie1,3,*, Chengfu Ding4
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5761-5784, 2023, DOI:10.32604/cmc.2023.038910 - 29 April 2023
    Abstract Rapid and precise vehicle recognition and classification are essential for intelligent transportation systems, and road target detection is one of the most difficult tasks in the field of computer vision. The challenge in real-time road target detection is the ability to properly pinpoint relatively small vehicles in complicated environments. However, because road targets are prone to complicated backgrounds and sparse features, it is challenging to detect and identify vehicle kinds fast and reliably. We suggest a new vehicle detection model called MEB-YOLO, which combines Mosaic and MixUp data augmentation, Efficient Channel Attention (ECA) attention mechanism,… More >

  • Open AccessOpen Access

    ARTICLE

    Auto-Scaling Framework for Enhancing the Quality of Service in the Mobile Cloud Environments

    Yogesh Kumar1,*, Jitender Kumar1, Poonam Sheoran2
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5785-5800, 2023, DOI:10.32604/cmc.2023.039276 - 29 April 2023
    Abstract On-demand availability and resource elasticity features of Cloud computing have attracted the focus of various research domains. Mobile cloud computing is one of these domains where complex computation tasks are offloaded to the cloud resources to augment mobile devices’ cognitive capacity. However, the flexible provisioning of cloud resources is hindered by uncertain offloading workloads and significant setup time of cloud virtual machines (VMs). Furthermore, any delays at the cloud end would further aggravate the miseries of real-time tasks. To resolve these issues, this paper proposes an auto-scaling framework (ACF) that strives to maintain the quality… More >

  • Open AccessOpen Access

    ARTICLE

    Improving Targeted Multimodal Sentiment Classification with Semantic Description of Images

    Jieyu An*, Wan Mohd Nazmee Wan Zainon, Zhang Hao
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5801-5815, 2023, DOI:10.32604/cmc.2023.038220 - 29 April 2023
    Abstract Targeted multimodal sentiment classification (TMSC) aims to identify the sentiment polarity of a target mentioned in a multimodal post. The majority of current studies on this task focus on mapping the image and the text to a high-dimensional space in order to obtain and fuse implicit representations, ignoring the rich semantic information contained in the images and not taking into account the contribution of the visual modality in the multimodal fusion representation, which can potentially influence the results of TMSC tasks. This paper proposes a general model for Improving Targeted Multimodal Sentiment Classification with Semantic… More >

  • Open AccessOpen Access

    ARTICLE

    Graph Construction Method for GNN-Based Multivariate Time-Series Forecasting

    Wonyong Chung, Jaeuk Moon, Dongjun Kim, Eenjun Hwang*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5817-5836, 2023, DOI:10.32604/cmc.2023.036830 - 29 April 2023
    Abstract Multivariate time-series forecasting (MTSF) plays an important role in diverse real-world applications. To achieve better accuracy in MTSF, time-series patterns in each variable and interrelationship patterns between variables should be considered together. Recently, graph neural networks (GNNs) has gained much attention as they can learn both patterns using a graph. For accurate forecasting through GNN, a well-defined graph is required. However, existing GNNs have limitations in reflecting the spectral similarity and time delay between nodes, and consider all nodes with the same weight when constructing graph. In this paper, we propose a novel graph construction More >

  • Open AccessOpen Access

    ARTICLE

    Multi-Classification of Polyps in Colonoscopy Images Based on an Improved Deep Convolutional Neural Network

    Shuang Liu1,2,3, Xiao Liu1, Shilong Chang1, Yufeng Sun4, Kaiyuan Li1, Ya Hou1, Shiwei Wang1, Jie Meng5, Qingliang Zhao6, Sibei Wu1, Kun Yang1,2,3,*, Linyan Xue1,2,3,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5837-5852, 2023, DOI:10.32604/cmc.2023.034720 - 29 April 2023
    Abstract Achieving accurate classification of colorectal polyps during colonoscopy can avoid unnecessary endoscopic biopsy or resection. This study aimed to develop a deep learning model that can automatically classify colorectal polyps histologically on white-light and narrow-band imaging (NBI) colonoscopy images based on World Health Organization (WHO) and Workgroup serrAted polypS and Polyposis (WASP) classification criteria for colorectal polyps. White-light and NBI colonoscopy images of colorectal polyps exhibiting pathological results were firstly collected and classified into four categories: conventional adenoma, hyperplastic polyp, sessile serrated adenoma/polyp (SSAP) and normal, among which conventional adenoma could be further divided into… More >

  • Open AccessOpen Access

    ARTICLE

    Robust Watermarking Algorithm for Medical Volume Data Based on Polar Cosine Transform and 3D-DCT

    Pengju Zhang1, Jingbing Li1,2,*, Uzair Aslam Bhatti1,2, Jing Liu3, Yen-wei Chen4, Dekai Li1, Lei Cao1
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5853-5870, 2023, DOI:10.32604/cmc.2023.036462 - 29 April 2023
    Abstract The amount of 3D data stored and transmitted in the Internet of Medical Things (IoMT) is increasing, making protecting these medical data increasingly prominent. However, there are relatively few researches on 3D data watermarking. Moreover, due to the particularity of medical data, strict data quality should be considered while protecting data security. To solve the problem, in the field of medical volume data, we proposed a robust watermarking algorithm based on Polar Cosine Transform and 3D-Discrete Cosine Transform (PCT and 3D-DCT). Each slice of the volume data was transformed by PCT to obtain feature row… More >

  • Open AccessOpen Access

    ARTICLE

    Novel Machine Learning–Based Approach for Arabic Text Classification Using Stylistic and Semantic Features

    Fethi Fkih1,2,*, Mohammed Alsuhaibani1, Delel Rhouma1,2, Ali Mustafa Qamar1
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5871-5886, 2023, DOI:10.32604/cmc.2023.035910 - 29 April 2023
    Abstract Text classification is an essential task for many applications related to the Natural Language Processing domain. It can be applied in many fields, such as Information Retrieval, Knowledge Extraction, and Knowledge modeling. Even though the importance of this task, Arabic Text Classification tools still suffer from many problems and remain incapable of responding to the increasing volume of Arabic content that circulates on the web or resides in large databases. This paper introduces a novel machine learning-based approach that exclusively uses hybrid (stylistic and semantic) features. First, we clean the Arabic documents and translate them… More >

  • Open AccessOpen Access

    ARTICLE

    Improved Monarchy Butterfly Optimization Algorithm (IMBO): Intrusion Detection Using Mapreduce Framework Based Optimized ANU-Net

    Kunda Suresh Babu, Yamarthi Narasimha Rao*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5887-5909, 2023, DOI:10.32604/cmc.2023.037486 - 29 April 2023
    Abstract The demand for cybersecurity is rising recently due to the rapid improvement of network technologies. As a primary defense mechanism, an intrusion detection system (IDS) was anticipated to adapt and secure computing infrastructures from the constantly evolving, sophisticated threat landscape. Recently, various deep learning methods have been put forth; however, these methods struggle to recognize all forms of assaults, especially infrequent attacks, because of network traffic imbalances and a shortage of aberrant traffic samples for model training. This work introduces deep learning (DL) based Attention based Nested U-Net (ANU-Net) for intrusion detection to address these… More >

  • Open AccessOpen Access

    ARTICLE

    Multi-Attribute Couplings-Based Euclidean and Nominal Distances for Unlabeled Nominal Data

    Lei Gu*, Furong Zhang, Li Ma
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5911-5928, 2023, DOI:10.32604/cmc.2023.038127 - 29 April 2023
    Abstract Learning unlabeled data is a significant challenge that needs to handle complicated relationships between nominal values and attributes. Increasingly, recent research on learning value relations within and between attributes has shown significant improvement in clustering and outlier detection, etc. However, typical existing work relies on learning pairwise value relations but weakens or overlooks the direct couplings between multiple attributes. This paper thus proposes two novel and flexible multi-attribute couplings-based distance (MCD) metrics, which learn the multi-attribute couplings and their strengths in nominal data based on information theories: self-information, entropy, and mutual information, for measuring both More >

  • Open AccessOpen Access

    ARTICLE

    Memory-Occupied Routing Algorithms for Quantum Relay Networks

    Jiangyuan Yao1, Kaiwen Zou2, Zheng Jiang2, Shuhua Weng1, Deshun Li1,*, Yahui Li3, Xingcan Cao4
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5929-5946, 2023, DOI:10.32604/cmc.2023.031284 - 29 April 2023
    Abstract Quantum transmission experiments have shown that the successful transmission rate of entangled quanta in optical fibers decreases exponentially. Although current quantum networks deploy quantum relays to establish long-distance connections, the increase in transmission distance and entanglement switching costs still need to be considered when selecting the next hop. However, most of the existing quantum network models prefer to consider the parameters of the physical layer, which ignore the influence factors of the network layer. In this paper, we propose a meshy quantum network model based on quantum teleportation, which considers both network layer and physical… More >

  • Open AccessOpen Access

    ARTICLE

    Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

    Ibrahim Aliyu1, Tai-Won Um2, Sang-Joon Lee3, Chang Gyoon Lim4,*, Jinsul Kim1,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5947-5964, 2023, DOI:10.32604/cmc.2023.037202 - 29 April 2023
    Abstract In the quest to minimize energy waste, the energy performance of buildings (EPB) has been a focus because building appliances, such as heating, ventilation, and air conditioning, consume the highest energy. Therefore, effective design and planning for estimating heating load (HL) and cooling load (CL) for energy saving have become paramount. In this vein, efforts have been made to predict the HL and CL using a univariate approach. However, this approach necessitates two models for learning HL and CL, requiring more computational time. Moreover, the one-dimensional (1D) convolutional neural network (CNN) has gained popularity due… More >

  • Open AccessOpen Access

    ARTICLE

    A Whale Optimization Algorithm with Distributed Collaboration and Reverse Learning Ability

    Zhedong Xu*, Yongbo Su, Fang Yang, Ming Zhang
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5965-5986, 2023, DOI:10.32604/cmc.2023.037611 - 29 April 2023
    Abstract Due to the development of digital transformation, intelligent algorithms are getting more and more attention. The whale optimization algorithm (WOA) is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization problems. However, with the increased dimensions, higher requirements are put forward for algorithm performance. The double population whale optimization algorithm with distributed collaboration and reverse learning ability (DCRWOA) is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization problems. In the DCRWOA algorithm, the novel double population search strategy is constructed. More >

  • Open AccessOpen Access

    ARTICLE

    A Speech Cryptosystem Using the New Chaotic System with a Capsule-Shaped Equilibrium Curve

    Mohamad Afendee Mohamed1, Talal Bonny2, Aceng Sambas3, Sundarapandian Vaidyanathan4, Wafaa Al Nassan2, Sen Zhang5, Khaled Obaideen2, Mustafa Mamat1, Mohd Kamal Mohd Nawawi6,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5987-6006, 2023, DOI:10.32604/cmc.2023.035668 - 29 April 2023
    Abstract In recent years, there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle, butterfly, heart and apple. This paper describes a new 3-D chaotic dynamical system with a capsule-shaped equilibrium curve. The proposed chaotic system has two quadratic, two cubic and two quartic nonlinear terms. It is noted that the proposed chaotic system has a hidden attractor since it has an infinite number of equilibrium points. It is also established that the proposed chaotic system exhibits multi-stability with two coexisting chaotic attractors for the same parameter values but… More >

  • Open AccessOpen Access

    ARTICLE

    PCATNet: Position-Class Awareness Transformer for Image Captioning

    Ziwei Tang1, Yaohua Yi2,*, Changhui Yu2, Aiguo Yin3
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6007-6022, 2023, DOI:10.32604/cmc.2023.037861 - 29 April 2023
    Abstract Existing image captioning models usually build the relation between visual information and words to generate captions, which lack spatial information and object classes. To address the issue, we propose a novel Position-Class Awareness Transformer (PCAT) network which can serve as a bridge between the visual features and captions by embedding spatial information and awareness of object classes. In our proposal, we construct our PCAT network by proposing a novel Grid Mapping Position Encoding (GMPE) method and refining the encoder-decoder framework. First, GMPE includes mapping the regions of objects to grids, calculating the relative distance among… More >

  • Open AccessOpen Access

    ARTICLE

    ESG Discourse Analysis Through BERTopic: Comparing News Articles and Academic Papers

    Haein Lee1, Seon Hong Lee1, Kyeo Re Lee2, Jang Hyun Kim3,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6023-6037, 2023, DOI:10.32604/cmc.2023.039104 - 29 April 2023
    Abstract Environmental, social, and governance (ESG) factors are critical in achieving sustainability in business management and are used as values aiming to enhance corporate value. Recently, non-financial indicators have been considered as important for the actual valuation of corporations, thus analyzing natural language data related to ESG is essential. Several previous studies limited their focus to specific countries or have not used big data. Past methodologies are insufficient for obtaining potential insights into the best practices to leverage ESG. To address this problem, in this study, the authors used data from two platforms: LexisNexis, a platform… More >

  • Open AccessOpen Access

    ARTICLE

    Real-Time Data Transmission with Data Carrier Support Value in Neighbor Strategic Collection in WSN

    S. Ponnarasi1,*, T. Rajendran2
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6039-6057, 2023, DOI:10.32604/cmc.2023.035499 - 29 April 2023
    Abstract An efficient trust-aware secure routing and network strategy-based data collection scheme is presented in this paper to enhance the performance and security of wireless sensor networks during data collection. The method first discovers the routes between the data sensors and the sink node. Several factors are considered for each sensor node along the route, including energy, number of neighbours, previous transmissions, and energy depletion ratio. Considering all these variables, the Sink Reachable Support Measure and the Secure Communication Support Measure, the method evaluates two distinct measures. The method calculates the data carrier support value using… More >

  • Open AccessOpen Access

    ARTICLE

    Fair-News: Digital Journalism Model to Prevent Information Pollution and Manipulation

    Savaş Takan1,*, Duygu Ergün2,*, Gökmen Katipoğlu3
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6059-6082, 2023, DOI:10.32604/cmc.2023.039505 - 29 April 2023
    Abstract As digital data circulation increases, information pollution and manipulation in journalism have become more prevalent. In this study, a new digital journalism model is designed to contribute to the solution of the main current problems, such as information pollution, manipulation, and accountability in digital journalism. The model uses blockchain technology due to its transparency, immutability, and traceability. However, it is tough to provide the mechanisms necessary for journalism, such as updating one piece of information, instantly updating all other information affected by the updated information, establishing logical relationships between news, making quick comparisons, sorting and… More >

  • Open AccessOpen Access

    ARTICLE

    Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System

    Sagheer Abbas1, Shabib Aftab1,2, Muhammad Adnan Khan3,4, Taher M. Ghazal5,6, Hussam Al Hamadi7, Chan Yeob Yeun8,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6083-6100, 2023, DOI:10.32604/cmc.2023.037933 - 29 April 2023
    Abstract The software engineering field has long focused on creating high-quality software despite limited resources. Detecting defects before the testing stage of software development can enable quality assurance engineers to concentrate on problematic modules rather than all the modules. This approach can enhance the quality of the final product while lowering development costs. Identifying defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development team. This process is known as software defect prediction, and it can improve end-product… More >

  • Open AccessOpen Access

    ARTICLE

    Crop Disease Recognition Based on Improved Model-Agnostic Meta-Learning

    Xiuli Si1, Biao Hong1, Yuanhui Hu1, Lidong Chu2,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6101-6118, 2023, DOI:10.32604/cmc.2023.036829 - 29 April 2023
    Abstract Currently, one of the most severe problems in the agricultural industry is the effect of diseases and pests on global crop production and economic development. Therefore, further research in the field of crop disease and pest detection is necessary to address the mentioned problem. Aiming to identify the diseased crops and insect pests timely and accurately and perform appropriate prevention measures to reduce the associated losses, this article proposes a Model-Agnostic Meta-Learning (MAML) attention model based on the meta-learning paradigm. The proposed model combines meta-learning with basic learning and adopts an Efficient Channel Attention (ECA)… More >

  • Open AccessOpen Access

    ARTICLE

    Delivery Service Management System Using Google Maps for SMEs in Emerging Countries

    Sophea Horng, Pisal Yenradee*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6119-6143, 2023, DOI:10.32604/cmc.2023.038764 - 29 April 2023
    Abstract This paper proposes a Delivery Service Management (DSM) system for Small and Medium Enterprises (SMEs) that own a delivery fleet of pickup trucks to manage Business-to-Business (B2B) delivery services. The proposed DSM system integrates four systems: Delivery Location Positioning (DLP), Delivery Route Planning (DRP), Arrival Time Prediction (ATP), and Communication and Data Sharing (CDS) systems. These systems are used to pinpoint the delivery locations of customers, plan the delivery route of each truck, predict arrival time (with an interval) at each delivery location, and communicate and share information among stakeholders, respectively. The DSM system deploys… More >

  • Open AccessOpen Access

    ARTICLE

    Modeling of Combined Economic and Emission Dispatch Using Improved Sand Cat Optimization Algorithm

    Fadwa Alrowais1, Jaber S. Alzahrani2, Radwa Marzouk1, Abdullah Mohamed3, Gouse Pasha Mohammed4,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6145-6160, 2023, DOI:10.32604/cmc.2023.038300 - 29 April 2023
    Abstract Combined Economic and Emission Dispatch (CEED) task forms multi-objective optimization problems to be resolved to minimize emission and fuel costs. The disadvantage of the conventional method is its incapability to avoid falling in local optimal, particularly when handling nonlinear and complex systems. Metaheuristics have recently received considerable attention due to their enhanced capacity to prevent local optimal solutions in addressing all the optimization problems as a black box. Therefore, this paper focuses on the design of an improved sand cat optimization algorithm based CEED (ISCOA-CEED) technique. The ISCOA-CEED technique majorly concentrates on reducing fuel costs More >

  • Open AccessOpen Access

    ARTICLE

    Secure Content Based Image Retrieval Scheme Based on Deep Hashing and Searchable Encryption

    Zhen Wang, Qiu-yu Zhang*, Ling-tao Meng, Yi-lin Liu
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6161-6184, 2023, DOI:10.32604/cmc.2023.037134 - 29 April 2023
    Abstract To solve the problem that the existing ciphertext domain image retrieval system is challenging to balance security, retrieval efficiency, and retrieval accuracy. This research suggests a searchable encryption and deep hashing-based secure image retrieval technique that extracts more expressive image features and constructs a secure, searchable encryption scheme. First, a deep learning framework based on residual network and transfer learning model is designed to extract more representative image deep features. Secondly, the central similarity is used to quantify and construct the deep hash sequence of features. The Paillier homomorphic encryption encrypts the deep hash sequence… More >

  • Open AccessOpen Access

    ARTICLE

    Secure Blockchain-Enabled Internet of Vehicles Scheme with Privacy Protection

    Jiansheng Zhang1, Yang Xin1,*, Yuyan Wang2, Xiaohui Lei2, Yixian Yang1
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6185-6199, 2023, DOI:10.32604/cmc.2023.038029 - 29 April 2023
    Abstract The car-hailing platform based on Internet of Vehicles (IoV) technology greatly facilitates passengers’ daily car-hailing, enabling drivers to obtain orders more efficiently and obtain more significant benefits. However, to match the driver closest to the passenger, it is often necessary to process the location information of the passenger and driver, which poses a considerable threat to privacy disclosure to the passenger and driver. Targeting these issues, in this paper, by combining blockchain and Paillier homomorphic encryption algorithm, we design a secure blockchain-enabled IoV scheme with privacy protection for online car-hailing. In this scheme, firstly, we… More >

  • Open AccessOpen Access

    ARTICLE

    Weakly Supervised Abstractive Summarization with Enhancing Factual Consistency for Chinese Complaint Reports

    Ren Tao, Chen Shuang*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6201-6217, 2023, DOI:10.32604/cmc.2023.036178 - 29 April 2023
    Abstract A large variety of complaint reports reflect subjective information expressed by citizens. A key challenge of text summarization for complaint reports is to ensure the factual consistency of generated summary. Therefore, in this paper, a simple and weakly supervised framework considering factual consistency is proposed to generate a summary of city-based complaint reports without pre-labeled sentences/words. Furthermore, it considers the importance of entity in complaint reports to ensure factual consistency of summary. Experimental results on the customer review datasets (Yelp and Amazon) and complaint report dataset (complaint reports of Shenyang in China) show that the More >

  • Open AccessOpen Access

    ARTICLE

    MEC-IoT-Healthcare: Analysis and Prospects

    Hongyuan Wang1, Mohammed Dauwed2, Imran Khan3, Nor Samsiah Sani4,*, Hasmila Amirah Omar4, Hirofumi Amano5, Samih M. Mostafa6
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6219-6250, 2023, DOI:10.32604/cmc.2022.030958 - 29 April 2023
    Abstract Physical sensors, intelligent sensors, and output recommendations are all examples of smart health technology that can be used to monitor patients’ health and change their behavior. Smart health is an Internet-of-Things (IoT)-aware network and sensing infrastructure that provides real-time, intelligent, and ubiquitous healthcare services. Because of the rapid development of cloud computing, as well as related technologies such as fog computing, smart health research is progressively moving in the right direction. Cloud, fog computing, IoT sensors, blockchain, privacy and security, and other related technologies have been the focus of smart health research in recent years.… More >

  • Open AccessOpen Access

    ARTICLE

    Managing Health Treatment by Optimizing Complex Lab-Developed Test Configurations: A Health Informatics Perspective

    Uzma Afzal1, Tariq Mahmood2, Ali Mustafa Qamar3,*, Ayaz H. Khan4,5
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6251-6267, 2023, DOI:10.32604/cmc.2023.037653 - 29 April 2023
    Abstract A complex Laboratory Developed Test (LDT) is a clinical test developed within a single laboratory. It is typically configured from many feature constraints from clinical repositories, which are part of the existing Laboratory Information Management System (LIMS). Although these clinical repositories are automated, support for managing patient information with test results of an LDT is also integrated within the existing LIMS. Still, the support to configure LDTs design needs to be made available even in standard LIMS packages. The manual configuration of LDTs is a complex process and can generate configuration inconsistencies because many constraints… More >

  • Open AccessOpen Access

    ARTICLE

    Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

    Wasif Akbar1, Adbul Mannan2, Qaisar Shaheen3,*, Mohammad Hijji4, Muhammad Anwar5, Muhammad Ayaz6
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6269-6286, 2023, DOI:10.32604/cmc.2023.036141 - 29 April 2023
    Abstract Machine Learning (ML) has changed clinical diagnostic procedures drastically. Especially in Cardiovascular Diseases (CVD), the use of ML is indispensable to reducing human errors. Enormous studies focused on disease prediction but depending on multiple parameters, further investigations are required to upgrade the clinical procedures. Multi-layered implementation of ML also called Deep Learning (DL) has unfolded new horizons in the field of clinical diagnostics. DL formulates reliable accuracy with big datasets but the reverse is the case with small datasets. This paper proposed a novel method that deals with the issue of less data dimensionality. Inspired… More >

  • Open AccessOpen Access

    ARTICLE

    Parameter-Tuned Deep Learning-Enabled Activity Recognition for Disabled People

    Mesfer Al Duhayyim*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6287-6303, 2023, DOI:10.32604/cmc.2023.033045 - 29 April 2023
    Abstract Elderly or disabled people can be supported by a human activity recognition (HAR) system that monitors their activity intervenes and patterns in case of changes in their behaviors or critical events have occurred. An automated HAR could assist these persons to have a more independent life. Providing appropriate and accurate data regarding the activity is the most crucial computation task in the activity recognition system. With the fast development of neural networks, computing, and machine learning algorithms, HAR system based on wearable sensors has gained popularity in several areas, such as medical services, smart homes,… More >

  • Open AccessOpen Access

    ARTICLE

    Energy Cost Minimization Using String Matching Algorithm in Geo-Distributed Data Centers

    Muhammad Imran Khan Khalil1, Syed Adeel Ali Shah1, Izaz Ahmad Khan2, Mohammad Hijji3, Muhammad Shiraz4, Qaisar Shaheen5,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6305-6322, 2023, DOI:10.32604/cmc.2023.038163 - 29 April 2023
    Abstract Data centers are being distributed worldwide by cloud service providers (CSPs) to save energy costs through efficient workload allocation strategies. Many CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload processing. Numerous research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers (DCs). However, operating cost savings during workload processing, which also considers string-matching techniques in geo-distributed DCs, remains unexplored. In this research, we propose a novel string matching-based geographical load balancing (SMGLB) technique to mitigate the operating cost of the geo-distributed DC.… More >

  • Open AccessOpen Access

    ARTICLE

    A Secure Energy Internet Scheme for IoV Based on Post-Quantum Blockchain

    Jiansheng Zhang1, Yang Xin1,*, Yuyan Wang2, Xiaohui Lei2, Yixian Yang1
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6323-6336, 2023, DOI:10.32604/cmc.2023.034668 - 29 April 2023
    Abstract With the increasing use of distributed electric vehicles (EV), energy management in the Internet of vehicles (IoV) has attracted more attention, especially demand response (DR) management to achieve efficient energy management in IoV. Therefore, it is a tendency to introduce distributed energy such as renewable energy into the existing supply system. For optimizing the energy internet (EI) for IoV, in this paper, we introduce blockchain into energy internet and propose a secure EI scheme for IoV based on post-quantum blockchain, which provides the new information services and an incentive cooperation mechanism for the current energy… More >

  • Open AccessOpen Access

    ARTICLE

    Automated X-ray Defect Inspection on Occluded BGA Balls Using Hybrid Algorithm

    Ki-Yeol Eom1, Byungseok Min2,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6337-6350, 2023, DOI:10.32604/cmc.2023.035336 - 29 April 2023
    Abstract Automated X-ray defect inspection of occluded objects has been an essential topic in semiconductors, autonomous vehicles, and artificial intelligence devices. However, there are few solutions to segment occluded objects in the X-ray inspection efficiently. In particular, in the Ball Grid Array inspection of X-ray images, it is difficult to accurately segment the regions of occluded solder balls and detect defects inside solder balls. In this paper, we present a novel automatic inspection algorithm that segments solder balls, and detects defects fast and efficiently when solder balls are occluded. The proposed algorithm consists of two stages.… More >

  • Open AccessOpen Access

    ARTICLE

    A Progressive Approach to Generic Object Detection: A Two-Stage Framework for Image Recognition

    Muhammad Aamir1, Ziaur Rahman1,*, Waheed Ahmed Abro2, Uzair Aslam Bhatti3, Zaheer Ahmed Dayo1, Muhammad Ishfaq1
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6351-6373, 2023, DOI:10.32604/cmc.2023.038173 - 29 April 2023
    Abstract Object detection in images has been identified as a critical area of research in computer vision image processing. Research has developed several novel methods for determining an object’s location and category from an image. However, there is still room for improvement in terms of detection efficiency. This study aims to develop a technique for detecting objects in images. To enhance overall detection performance, we considered object detection a two-fold problem, including localization and classification. The proposed method generates class-independent, high-quality, and precise proposals using an agglomerative clustering technique. We then combine these proposals with the… More >

  • Open AccessOpen Access

    ARTICLE

    Bi-LSTM-Based Deep Stacked Sequence-to-Sequence Autoencoder for Forecasting Solar Irradiation and Wind Speed

    Neelam Mughees1,2, Mujtaba Hussain Jaffery1, Abdullah Mughees3, Anam Mughees4, Krzysztof Ejsmont5,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6375-6393, 2023, DOI:10.32604/cmc.2023.038564 - 29 April 2023
    Abstract Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050. However, they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions. In microgrids, smart energy management systems, such as integrated demand response programs, are permanently established on a step-ahead basis, which means that accurate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids. With this in mind, a novel “bidirectional long short-term memory network” (Bi-LSTM)-based, deep stacked,… More >

  • Open AccessOpen Access

    ARTICLE

    Metaheuristic Optimization with Deep Learning Enabled Smart Grid Stability Prediction

    Afrah Al-Bossly*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6395-6408, 2023, DOI:10.32604/cmc.2023.028433 - 29 April 2023
    Abstract Due to the drastic increase in global population as well as economy, electricity demand becomes considerably high. The recently developed smart grid (SG) technology has the ability to minimize power loss at the time of power distribution. Machine learning (ML) and deep learning (DL) models can be effectually developed for the design of SG stability techniques. This article introduces a new Social Spider Optimization with Deep Learning Enabled Statistical Analysis for Smart Grid Stability (SSODLSA-SGS) prediction model. Primarily, class imbalance data handling process is performed using Synthetic minority oversampling technique (SMOTE) technique. The SSODLSA-SGS model… More >

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