Journals / CMC / Vol.,
Table of Content

Research Article

BEST PAPER 2021

Intelligent Decision Support Systems for Complex Healthcare Applications


Submission Deadline: 31 January 2021 (closed)

Abstract

This article has no abstract.

Keywords

Advances in Metaheuristic Optimization Algorithms, Intelligent Decision Support Systems, Complex Data mining and knowledge discovery algorithms, Computer-aided diagnostic system, Complex healthcare applications, Complex healthcare applications, Big healthcare and rehabilitation data analytics, Machine learning techniques, Deep Learning
  • Research Article

    BEST PAPER 2021

    Scheduling Multi-Mode Resource-Constrained Projects Using Heuristic Rules Under Uncertainty Environment

    Mohamed Abdel-Basset1, Ahmed Sleem1, Asmaa Atef1, Yunyoung Nam2,*, Mohamed Abouhawwash3,4 CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 847-874, 2022, DOI:10.32604/cmc.2022.017106
    Abstract Project scheduling is a key objective of many models and is the proposed method for project planning and management. Project scheduling problems depend on precedence relationships and resource constraints, in addition to some other limitations for achieving a subset of goals. Project scheduling problems are dependent on many limitations, including limitations of precedence relationships, resource constraints, and some other limitations for achieving a subset of goals. Deterministic project scheduling models consider all information about the scheduling problem such as activity durations and precedence relationships information resources available and required, which are known and stable during the implementation process. The concept… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    An Optimized Framework for Surgical Team Selection

    Hemant Petwal*, Rinkle Rani CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2563-2582, 2021, DOI:10.32604/cmc.2021.017548
    Abstract In the healthcare system, a surgical team is a unit of experienced personnel who provide medical care to surgical patients during surgery. Selecting a surgical team is challenging for a multispecialty hospital as the performance of its members affects the efficiency and reliability of the hospital’s patient care. The effectiveness of a surgical team depends not only on its individual members but also on the coordination among them. In this paper, we addressed the challenges of surgical team selection faced by a multispecialty hospital and proposed a decision-making framework for selecting the optimal list of surgical teams for a given… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Classification of Epileptic Electroencephalograms Using Time-Frequency and Back Propagation Methods

    Sengul Bayrak1,2,*, Eylem Yucel2, Hidayet Takci3, Ruya Samli2 CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1427-1446, 2021, DOI:10.32604/cmc.2021.015524
    Abstract Today, electroencephalography is used to measure brain activity by creating signals that are viewed on a monitor. These signals are frequently used to obtain information about brain neurons and may detect disorders that affect the brain, such as epilepsy. Electroencephalogram (EEG) signals are however prone to artefacts. These artefacts must be removed to obtain accurate and meaningful signals. Currently, computer-aided systems have been used for this purpose. These systems provide high computing power, problem-specific development, and other advantages. In this study, a new clinical decision support system was developed for individuals to detect epileptic seizures using EEG signals. Comprehensive classification… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Blockchain-as-a-Utility for Next-Generation Healthcare Internet of Things

    Alaa Omran Almagrabi1, Rashid Ali2, Daniyal Alghazzawi1, Abdullah AlBarakati1, Tahir Khurshaid3,* CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 359-376, 2021, DOI:10.32604/cmc.2021.014753
    Abstract The scope of the Internet of Things (IoT) applications varies from strategic applications, such as smart grids, smart transportation, smart security, and smart healthcare, to industrial applications such as smart manufacturing, smart logistics, smart banking, and smart insurance. In the advancement of the IoT, connected devices become smart and intelligent with the help of sensors and actuators. However, issues and challenges need to be addressed regarding the data reliability and protection for significant next-generation IoT applications like smart healthcare. For these next-generation applications, there is a requirement for far-reaching privacy and security in the IoT. Recently, blockchain systems have emerged… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Computer Decision Support System for Skin Cancer Localization and Classification

    Muhammad Attique Khan1, Tallha Akram2, Muhammad Sharif1, Seifedine Kadry3, Yunyoung Nam4,* CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1041-1064, 2021, DOI:10.32604/cmc.2021.016307
    Abstract In this work, we propose a new, fully automated system for multiclass skin lesion localization and classification using deep learning. The main challenge is to address the problem of imbalanced data classes, found in HAM10000, ISBI2018, and ISBI2019 datasets. Initially, we consider a pre-trained deep neural network model, DarkeNet19, and fine-tune the parameters of third convolutional layer to generate the image gradients. All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network (HFaFFNN). The resultant image is further enhanced by employing a log-opening based activation function to generate a localized binary image. Later, two… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Deep Learning-Based Hookworm Detection in Wireless Capsule Endoscopic Image Using AdaBoost Classifier

    K. Lakshminarayanan1, N. Muthukumaran1, Y. Harold Robinson2, Vimal Shanmuganathan3, Seifedine Kadry4, Yunyoung Nam5,* CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3045-3055, 2021, DOI:10.32604/cmc.2021.014370
    Abstract Hookworm is an illness caused by an internal sponger called a roundworm. Inferable from deprived cleanliness in the developing nations, hookworm infection is a primary source of concern for both motherly and baby grimness. The current framework for hookworm detection is composed of hybrid convolutional neural networks; explicitly an edge extraction framework alongside a hookworm classification framework is developed. To consolidate the cylindrical zones obtained from the edge extraction framework and the trait map acquired into the hookworm scientific categorization framework, pooling layers are proposed. The hookworms display different profiles, widths, and bend directions. These challenges make it difficult for… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Medical Diagnosis Using Machine Learning: A Statistical Review

    Kaustubh Arun Bhavsar1, Jimmy Singla1, Yasser D. Al-Otaibi2, Oh-Young Song3,*, Yousaf Bin Zikria4, Ali Kashif Bashir5 CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 107-125, 2021, DOI:10.32604/cmc.2021.014604
    Abstract Decision making in case of medical diagnosis is a complicated process. A large number of overlapping structures and cases, and distractions, tiredness, and limitations with the human visual system can lead to inappropriate diagnosis. Machine learning (ML) methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis. Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published. Hence, to determine the use of ML to improve the diagnosis in varied medical disciplines, a systematic review is conducted in this study. To carry… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Blockchain-Enabled EHR Framework for Internet of Medical Things

    Lewis Nkenyereye1,*, S. M. Riazul Islam2, Mahmud Hossain3, M. Abdullah-Al-Wadud4, Atif Alamri4 CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 211-221, 2021, DOI:10.32604/cmc.2021.013796
    Abstract The Internet of Medical Things (IoMT) offers an infrastructure made of smart medical equipment and software applications for healthcare services. Through the internet, the IoMT is capable of providing remote medical diagnosis and timely health services. The patients can use their smart devices to create, store and share their electronic health records (EHR) with a variety of medical personnel including medical doctors and nurses. However, unless the underlying commination within IoMT is secured, malicious users can intercept, modify and even delete the sensitive EHR data of patients. Patients also lose full control of their EHR since most healthcare services within… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Deep Learning Based Optimal Multimodal Fusion Framework for Intrusion Detection Systems for Healthcare Data

    Phong Thanh Nguyen1, Vy Dang Bich Huynh2, Khoa Dang Vo1, Phuong Thanh Phan1, Mohamed Elhoseny3, Dac-Nhuong Le4,5,* CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2555-2571, 2021, DOI:10.32604/cmc.2021.012941
    Abstract Data fusion is a multidisciplinary research area that involves different domains. It is used to attain minimum detection error probability and maximum reliability with the help of data retrieved from multiple healthcare sources. The generation of huge quantity of data from medical devices resulted in the formation of big data during which data fusion techniques become essential. Securing medical data is a crucial issue of exponentially-pacing computing world and can be achieved by Intrusion Detection Systems (IDS). In this regard, since singular-modality is not adequate to attain high detection rate, there is a need exists to merge diverse techniques using… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    A New Decision-Making Model Based on Plithogenic Set for Supplier Selection

    Mohamed Abdel-Basset1,*, Rehab Mohamed1, Florentin Smarandache2, Mohamed Elhoseny3 CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2751-2769, 2021, DOI:10.32604/cmc.2021.013092
    Abstract Supplier selection is a common and relevant phase to initialize the supply chain processes and ensure its sustainability. The choice of supplier is a multi-criteria decision making (MCDM) to obtain the optimal decision based on a group of criteria. The health care sector faces several types of problems, and one of the most important is selecting an appropriate supplier that fits the desired performance level. The development of service/product quality in health care facilities in a country will improve the quality of the life of its population. This paper proposes an integrated multi-attribute border approximation area comparison (MABAC) based on… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Recognition and Classification of Pomegranate Leaves Diseases by Image Processing and Machine Learning Techniques

    Mangena Venu Madhavan1, Dang Ngoc Hoang Thanh2, Aditya Khamparia1,*, Sagar Pande1, Rahul Malik1, Deepak Gupta3 CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2939-2955, 2021, DOI:10.32604/cmc.2021.012466
    Abstract Disease recognition in plants is one of the essential problems in agricultural image processing. This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly. The framework utilizes image processing techniques such as image acquisition, image resizing, image enhancement, image segmentation, ROI extraction (region of interest), and feature extraction. An image dataset related to pomegranate leaf disease is utilized to implement the framework, divided into a training set and a test set. In the implementation process, techniques such as image enhancement and image segmentation are primarily used for identifying ROI and features. An image… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

    Ihsan Jasim Hussein1, M. A. Burhanuddin2, Mazin Abed Mohammed3,*, Mohamed Elhoseny4, Begonya Garcia-Zapirain5, Marwah Suliman Maashi6, Mashael S. Maashi7 CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3161-3182, 2021, DOI:10.32604/cmc.2021.012691
    Abstract One of the most complex tasks for computer-aided diagnosis (Intelligent decision support system) is the segmentation of lesions. Thus, this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images. The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases. In addition, proposed an approach that can efficiently generate region-of-interest (ROI) and new features that can be used in characterizing lesion boundaries. This study uses two databases in training and testing the proposed segmentation approach. The breast cancer… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    An Optimal Deep Learning Based Computer-Aided Diagnosis System for Diabetic Retinopathy

    Phong Thanh Nguyen1, Vy Dang Bich Huynh2, Khoa Dang Vo1, Phuong Thanh Phan1, Eunmok Yang3,*, Gyanendra Prasad Joshi4 CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2815-2830, 2021, DOI:10.32604/cmc.2021.012315
    Abstract Diabetic Retinopathy (DR) is a significant blinding disease that poses serious threat to human vision rapidly. Classification and severity grading of DR are difficult processes to accomplish. Traditionally, it depends on ophthalmoscopically-visible symptoms of growing severity, which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity. This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization (OPSO) algorithm-based Convolutional Neural Network (CNN) Model EOPSO-CNN in order to perform DR detection and grading. The proposed EOPSO-CNN model involves three main processes such as preprocessing, feature extraction, and classification. The proposed model initially involves… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Fog-Based Secure Framework for Personal Health Records Systems

    Lewis Nkenyereye1,*, S. M. Riazul Islam2, Mahmud Hossain3, M. Abdullah-Al-Wadud4, Atif Alamri4 CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1937-1948, 2021, DOI:10.32604/cmc.2020.013025
    Abstract The rapid development of personal health records (PHR) systems enables an individual to collect, create, store and share his PHR to authorized entities. Health care systems within the smart city environment require a patient to share his PRH data with a multitude of institutions’ repositories located in the cloud. The cloud computing paradigm cannot meet such a massive transformative healthcare systems due to drawbacks including network latency, scalability and bandwidth. Fog computing relieves the load of conventional cloud computing by availing intermediate fog nodes between the end users and the remote servers. Assuming a massive demand of PHR data within… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Deep Learning Based Intelligent and Sustainable Smart Healthcare Application in Cloud-Centric IoT

    K. V. Praveen1, P. M. Joe Prathap2, S. Dhanasekaran3, I. S. Hephzi Punithavathi4, P. Duraipandy5, Irina V. Pustokhina6, Denis A. Pustokhin7,* CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1987-2003, 2021, DOI:10.32604/cmc.2020.012398
    Abstract Recent developments in information technology can be attributed to the development of smart cities which act as a key enabler for next-generation intelligent systems to improve security, reliability, and efficiency. The healthcare sector becomes advantageous and offers different ways to manage patient information in order to improve healthcare service quality. The futuristic sustainable computing solutions in e-healthcare applications depend upon Internet of Things (IoT) in cloud computing environment. The energy consumed during data communication from IoT devices to cloud server is significantly high and it needs to be reduced with the help of clustering techniques. The current research article presents… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Intelligent Tunicate Swarm-Optimization-Algorithm-Based Lightweight Security Mechanism in Internet of Health Things

    Gia Nhu Nguyen1,2, Nin Ho Le Viet1,2, Gyanendra Prasad Joshi3, Bhanu Shrestha4,* CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 551-562, 2021, DOI:10.32604/cmc.2020.012441
    Abstract Fog computing in the Internet of Health Things (IoHT) is promising owing to the increasing need for energy- and latency-optimized health sector provisioning. Additionally, clinical data (particularly, medical image data) are a delicate, highly protected resource that should be utilized in an effective and responsible manner to fulfil consumer needs. Herein, we propose an energy-effi- cient fog-based IoHT with a tunicate swarm-optimization-(TSO)-based lightweight Simon cipher to enhance the energy efficiency at the fog layer and the security of data stored at the cloud server. The proposed Simon cipher uses the TSO algorithm to select the optimal keys that will minimize… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Artificial Intelligence-Based Semantic Segmentation of Ocular Regions for Biometrics and Healthcare Applications

    Rizwan Ali Naqvi1, Dildar Hussain2, Woong-Kee Loh3,* CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 715-732, 2021, DOI:10.32604/cmc.2020.013249
    Abstract Multiple ocular region segmentation plays an important role in different applications such as biometrics, liveness detection, healthcare, and gaze estimation. Typically, segmentation techniques focus on a single region of the eye at a time. Despite the number of obvious advantages, very limited research has focused on multiple regions of the eye. Similarly, accurate segmentation of multiple eye regions is necessary in challenging scenarios involving blur, ghost effects low resolution, off-angles, and unusual glints. Currently, the available segmentation methods cannot address these constraints. In this paper, to address the accurate segmentation of multiple eye regions in unconstrainted scenarios, a lightweight outer… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Emergency Prioritized and Congestion Handling Protocol for Medical Internet of Things

    Sabeen Tahir*, Sheikh Tahir Bakhsh, Rayed AlGhamdi CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 733-749, 2021, DOI:10.32604/cmc.2020.013261
    Abstract Medical Internet of Things (MIoTs) is a collection of small and energyefficient wireless sensor devices that monitor the patient’s body. The healthcare networks transmit continuous data monitoring for the patients to survive them independently. There are many improvements in MIoTs, but still, there are critical issues that might affect the Quality of Service (QoS) of a network. Congestion handling is one of the critical factors that directly affect the QoS of the network. The congestion in MIoT can cause more energy consumption, delay, and important data loss. If a patient has an emergency, then the life-critical signals must transmit with… More >

    Graphic Abstract

Share Link

WeChat scan