Open Access

ARTICLE

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
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 is replaced by the proportion… More >

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676

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ARTICLE

Mohammad Yamin^1,*, 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
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, speech detection and Natural Language… More >

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378

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ARTICLE

Ou Ye^1,2, Mimi Wang¹, Zhenhua Yu^1,*, Yan Fu¹, Shun Yi¹, Jun Deng²
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5675-5696, 2023, DOI:10.32604/cmc.2023.037503
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 model are integrated to construct… More >

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457

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296

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ARTICLE

Seema Rani¹, Anju¹, Anupma Sangwan¹, Krishna Kumar², Kashif Nisar³, Tariq Rahim Soomro³, Ag. Asri Ag. Ibrahim^4,*, 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
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 and analyze underwater node localization… More >

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421

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REVIEW

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
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) flow method. This research aims… More >

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395

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ARTICLE

Yan Du^1,2, Shibin Zhang^1,2,*, Guogen Wan^1,2, Daohua Zhou³, Jiazhong Lu^1,2, Yuanyuan Huang^1,2, Xiaoman Cheng⁴, Yi Zhang⁴, Peilin He⁵
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5743-5759, 2023, DOI:10.32604/cmc.2023.035919
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) A request response-based convolutional neural… More >

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344

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ARTICLE

Yingkun Song¹, Shunhe Hong¹, Chentao Hu¹, Pingan He², Lingbing Tao¹, Zhixin Tie^1,3,*, Chengfu Ding⁴
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5761-5784, 2023, DOI:10.32604/cmc.2023.038910
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, Bidirectional Feature Pyramid Network (BiFPN)… More >

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ARTICLE

Yogesh Kumar^1,*, Jitender Kumar¹, Poonam Sheoran²
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5785-5800, 2023, DOI:10.32604/cmc.2023.039276
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 of service (QoS) for the… More >

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479

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349

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ARTICLE

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
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 Description of Images (ITMSC) as… More >

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765

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384

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ARTICLE

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
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 method that solves aforementioned limitations.… More >

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487

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ARTICLE

Shuang Liu^1,2,3, Xiao Liu¹, Shilong Chang¹, Yufeng Sun⁴, Kaiyuan Li¹, Ya Hou¹, Shiwei Wang¹, Jie Meng⁵, Qingliang Zhao⁶, Sibei Wu¹, Kun Yang^1,2,3,*, Linyan Xue^1,2,3,*
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5837-5852, 2023, DOI:10.32604/cmc.2023.034720
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 three sub-categories of tubular adenoma,… More >

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1

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ARTICLE

Pengju Zhang¹, Jingbing Li^1,2,*, Uzair Aslam Bhatti^1,2, 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
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 vector, and then the reshaped… More >

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281

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1

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ARTICLE

Fethi Fkih^1,2,*, Mohammed Alsuhaibani¹, Delel Rhouma^1,2, Ali Mustafa Qamar¹
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5871-5886, 2023, DOI:10.32604/cmc.2023.035910
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 to English using translation tools.… More >

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325

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ARTICLE

Kunda Suresh Babu, Yamarthi Narasimha Rao^*
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5887-5909, 2023, DOI:10.32604/cmc.2023.037486
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 issues and enhance detection performance.… More >

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397

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ARTICLE

Lei Gu^*, Furong Zhang, Li Ma
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5911-5928, 2023, DOI:10.32604/cmc.2023.038127
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 numerical and nominal distances. MCD… More >

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ARTICLE

Jiangyuan Yao¹, Kaiwen Zou², Zheng Jiang², Shuhua Weng¹, Deshun Li^1,*, Yahui Li³, Xingcan Cao⁴
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5929-5946, 2023, DOI:10.32604/cmc.2023.031284
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 layer parameters. The proposed model… More >

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605

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477

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ARTICLE

Ibrahim Aliyu¹, Tai-Won Um², Sang-Joon Lee³, Chang Gyoon Lim^4,*, Jinsul Kim^1,*
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5947-5964, 2023, DOI:10.32604/cmc.2023.037202
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 to its nominal computational complexity,… More >

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726

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339

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ARTICLE

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
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. Meanwhile, the reverse learning strategy… More >

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567

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466

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ARTICLE

Mohamad Afendee Mohamed¹, Talal Bonny², Aceng Sambas³, Sundarapandian Vaidyanathan⁴, Wafaa Al Nassan², Sen Zhang⁵, Khaled Obaideen², Mustafa Mamat¹, Mohd Kamal Mohd Nawawi^6,*
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5987-6006, 2023, DOI:10.32604/cmc.2023.035668
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 differential initial states. A detailed… More >

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ARTICLE

Ziwei Tang¹, Yaohua Yi^2,*, Changhui Yu², Aiguo Yin³
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6007-6022, 2023, DOI:10.32604/cmc.2023.037861
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 objects and quantization. Meanwhile, we… More >

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289

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ARTICLE

Haein Lee¹, Seon Hong Lee¹, Kyeo Re Lee², Jang Hyun Kim^3,*
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6023-6037, 2023, DOI:10.32604/cmc.2023.039104
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 that provides media monitoring, and… More >

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615

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ARTICLE

S. Ponnarasi^1,*, T. Rajendran²
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6039-6057, 2023, DOI:10.32604/cmc.2023.035499
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 these two metrics. A single… More >

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347

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ARTICLE

Savaş Takan^1,*, Duygu Ergün^2,*, Gökmen Katipoğlu³
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6059-6082, 2023, DOI:10.32604/cmc.2023.039505
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 indexing news, and keeping the… More >

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2

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ARTICLE

Sagheer Abbas¹, Shabib Aftab^1,2, Muhammad Adnan Khan^3,4, Taher M. Ghazal^5,6, Hussam Al Hamadi⁷, Chan Yeob Yeun^8,*
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6083-6100, 2023, DOI:10.32604/cmc.2023.037933
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 quality while reducing the cost… More >

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322

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ARTICLE

Xiuli Si¹, Biao Hong¹, Yuanhui Hu¹, Lidong Chu^2,*
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6101-6118, 2023, DOI:10.32604/cmc.2023.036829
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) module. The module follows the… More >

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ARTICLE

Sophea Horng, Pisal Yenradee^*
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6119-6143, 2023, DOI:10.32604/cmc.2023.038764
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 Google applications, a GPS tracking… More >

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322

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ARTICLE

Fadwa Alrowais¹, Jaber S. Alzahrani², Radwa Marzouk¹, Abdullah Mohamed³, Gouse Pasha Mohammed^4,*
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6145-6160, 2023, DOI:10.32604/cmc.2023.038300
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 and the emission of generation… More >

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327

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ARTICLE

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
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 to build a high-security and… More >

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543

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327

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ARTICLE

Jiansheng Zhang¹, Yang Xin^1,*, Yuyan Wang², Xiaohui Lei², Yixian Yang¹
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6185-6199, 2023, DOI:10.32604/cmc.2023.038029
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 propose an encryption scheme based… More >

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450

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ARTICLE

Ren Tao, Chen Shuang^*
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6201-6217, 2023, DOI:10.32604/cmc.2023.036178
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 proposed framework outperforms state-of-the-art approaches… More >

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270

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ARTICLE

Hongyuan Wang¹, Mohammed Dauwed², Imran Khan³, Nor Samsiah Sani^4,*, 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
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. At the moment, the focus… More >

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ARTICLE

Uzma Afzal¹, Tariq Mahmood², Ali Mustafa Qamar^3,*, Ayaz H. Khan^4,5
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6251-6267, 2023, DOI:10.32604/cmc.2023.037653
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 between features can remain unsatisfied.… More >

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ARTICLE

Wasif Akbar¹, Adbul Mannan², Qaisar Shaheen^3,*, Mohammad Hijji⁴, Muhammad Anwar⁵, Muhammad Ayaz⁶
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6269-6286, 2023, DOI:10.32604/cmc.2023.036141
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 by the regression analysis, the… More >

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323

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ARTICLE

Mesfer Al Duhayyim^*
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6287-6303, 2023, DOI:10.32604/cmc.2023.033045
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, improving human communication with computers,… More >

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ARTICLE

Muhammad Imran Khan Khalil¹, Syed Adeel Ali Shah¹, Izaz Ahmad Khan², Mohammad Hijji³, Muhammad Shiraz⁴, Qaisar Shaheen^5,*
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6305-6322, 2023, DOI:10.32604/cmc.2023.038163
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. The primary goal of this… More >

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Jiansheng Zhang¹, Yang Xin^1,*, Yuyan Wang², Xiaohui Lei², Yixian Yang¹
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6323-6336, 2023, DOI:10.32604/cmc.2023.034668
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 IoV system. Firstly, based on… More >

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329

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ARTICLE

Ki-Yeol Eom¹, Byungseok Min^2,*
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6337-6350, 2023, DOI:10.32604/cmc.2023.035336
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. In the first stage, the… More >

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Muhammad Aamir¹, Ziaur Rahman^1,*, 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
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 relevant input image to train… More >

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Neelam Mughees^1,2, Mujtaba Hussain Jaffery¹, Abdullah Mughees³, Anam Mughees⁴, Krzysztof Ejsmont^5,*
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6375-6393, 2023, DOI:10.32604/cmc.2023.038564
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, sequence-to-sequence autoencoder (S2SAE) forecasting model… More >

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Afrah Al-Bossly^*
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6395-6408, 2023, DOI:10.32604/cmc.2023.028433
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 involves two stages of pre-processing… More >

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