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  • Open Access


    Deep Learning Convolutional Neural Network for ECG Signal Classification Aggregated Using IoT

    S. Karthiga*, A. M. Abirami

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 851-866, 2022, DOI:10.32604/csse.2022.021935

    Abstract Much attention has been given to the Internet of Things (IoT) by citizens, industries, governments, and universities for applications like smart buildings, environmental monitoring, health care and so on. With IoT, network connectivity is facilitated between smart devices from anyplace and anytime. IoT-based health monitoring systems are gaining popularity and acceptance for continuous monitoring and detect health abnormalities from the data collected. Electrocardiographic (ECG) signals are widely used for heart diseases detection. A novel method has been proposed in this work for ECG monitoring using IoT techniques. In this work, a two-stage approach is employed. In the first stage, a… More >

  • Open Access


    Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

    M. Manikandan1,*, R. Subramanian2, M. S. Kavitha3, S. Karthik3

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 935-948, 2022, DOI:10.32604/csse.2022.021816

    Abstract In today’s world, Cloud Computing (CC) enables the users to access computing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located in remote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and task scheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud is employed since it is a capable solution to process large-scale consumer applications in a pay-by-use… More >

  • Open Access


    Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases

    S. Prakash1,*, P. Vishnu Raja2, A. Baseera3, D. Mansoor Hussain4, V. R. Balaji5, K. Venkatachalam6

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1273-1287, 2022, DOI:10.32604/csse.2022.021784

    Abstract Urban living in large modern cities exerts considerable adverse effects on health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanized countries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples is becoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce functions. The relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, the iterative weighted… More >

  • Open Access


    A Usability Management Framework for Securing Healthcare Information System

    Hosam Alhakami1, Abdullah Baz2, Wajdi Alhakami3, Abhishek Kumar Pandey4, Alka Agrawal4, Raees Ahmad Khan4,*

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1015-1030, 2022, DOI:10.32604/csse.2022.021564

    Abstract Transformation from conventional business management systems to smart digital systems is a recurrent trend in the current era. This has led to digital revolution, and in this context, the hardwired technologies in the software industry play a significant role However, from the beginning, software security remains a serious issue for all levels of stakeholders. Software vulnerabilities lead to intrusions that cause data breaches and result in disclosure of sensitive data, compromising the organizations’ reputation that translates into, financial losses and compromising software usability as well. Most of the data breaches are financially motivated, especially in the healthcare sector. The cyber… More >

  • Open Access


    Fusion Recommendation System Based on Collaborative Filtering and Knowledge Graph

    Donglei Lu1, Dongjie Zhu2,*, Haiwen Du3, Yundong Sun3, Yansong Wang2, Xiaofang Li4, Rongning Qu4, Ning Cao1, Russell Higgs5

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1133-1146, 2022, DOI:10.32604/csse.2022.021525

    Abstract The recommendation algorithm based on collaborative filtering is currently the most successful recommendation method. It recommends items to the user based on the known historical interaction data of the target user. Furthermore, the combination of the recommended algorithm based on collaborative filtration and other auxiliary knowledge base is an effective way to improve the performance of the recommended system, of which the Co-Factorization Model (CoFM) is one representative research. CoFM, a fusion recommendation model combining the collaborative filtering model FM and the graph embedding model TransE, introduces the information of many entities and their relations in the knowledge graph into… More >

  • Open Access


    FASTER–RCNN for Skin Burn Analysis and Tissue Regeneration

    C. Pabitha*, B. Vanathi

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 949-961, 2022, DOI:10.32604/csse.2022.021086

    Abstract Skin is the largest body organ that is prone to the environment most specifically. Therefore the skin is susceptible to many damages, including burn damage. Burns can endanger life and are linked to high morbidity and mortality rates. Effective diagnosis with the help of accurate burn zone and wound depth evaluation is important for clinical efficacy. The following characteristics are associated with the skin burn wound, such as healing, infection, painand stress and keloid formation. Tissue regeneration also takes a significant amount of time for formation while considering skin healing after a burn injury. Deep neural networks can automatically assist… More >

  • Open Access


    An Approximate Numerical Methods for Mathematical and Physical Studies for Covid-19 Models

    Hammad Alotaibi, Khaled A. Gepreel, Mohamed S. Mohamed, Amr M. S. Mahdy*

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1147-1163, 2022, DOI:10.32604/csse.2022.020869

    Abstract The advancement in numerical models of serious resistant illnesses is a key research territory in different fields including the nature and the study of disease transmission. One of the aims of these models is to comprehend the elements of conduction of these infections. For the new strain of Covid-19 (Coronavirus), there has been no immunization to protect individuals from the virus and to forestall its spread so far. All things being equal, control procedures related to medical services, for example, social distancing or separation, isolation, and travel limitations can be adjusted to control this pandemic. This article reveals some insights… More >

  • Open Access


    Cross Layer QoS Aware Scheduling based on Loss-Based Proportional Fairness with Multihop CRN

    K. Saravanan1,*, G. M. Tamilselvan2, A. Rajendran3

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1063-1077, 2022, DOI:10.32604/csse.2022.020789

    Abstract As huge users are involved, there is a difficulty in spectrum allocation and scheduling in Cognitive Radio Networks (CRNs). Collision increases when there is no allocation of spectrum and these results in huge drop rate and network performance degradation. To solve these problems and allocate appropriate spectrum, a novel method is introduced termed as Quality of Service (QoS) Improvement Proper Scheduling (QIPS). The major contribution of the work is to design a new cross layer QoS Aware Scheduling based on Loss-based Proportional Fairness with Multihop (QoSAS-LBPFM). In Medium Access Control (MAC) multi-channel network environment mobile nodes practice concurrent broadcast between… More >

  • Open Access


    Modelling of the Slope Solute Loss Based on Fuzzy Neural Network Model

    Xiaona Zhang1,*, Jie Feng2, Zhen Hong3, Xiaona Rui4

    Computer Systems Science and Engineering, Vol.42, No.2, pp. 677-688, 2022, DOI:10.32604/csse.2022.023136

    Abstract In regards to soil macropores, the solute loss carried by overland flow is a very complex process. In this study, a fuzzy neural network (FNN) model was used to analyze the solute loss on slopes, taking into account the soil macropores. An artificial rainfall simulation experiment was conducted in indoor experimental tanks, and the verification of the model was based on the results. The characteristic scale of the macropores, the rainfall intensity and duration, the slope and the adsorption coefficient of ions, were chosen as the input variables to the Sugeno FNN model. The cumulative solute loss quantity on the… More >

  • Open Access


    Roosters Algorithm: A Novel Nature-Inspired Optimization Algorithm

    Mashar Gencal1,*, Mustafa Oral2

    Computer Systems Science and Engineering, Vol.42, No.2, pp. 727-737, 2022, DOI:10.32604/csse.2022.023018

    Abstract Some species of females, e.g., chicken, bird, fish etc., might mate with more than one males. In the mating of these polygamous creatures, there is competition between males as well as among their offspring. Thus, male reproductive success depends on both male competition and sperm rivalry. Inspired by this type of sexual life of roosters with chickens, a novel nature-inspired optimization algorithm called Roosters Algorithm (RA) is proposed. The algorithm was modelled and implemented based on the sexual behavior of roosters. 13 well-known benchmark optimization functions and 10 IEEE CEC 2018 test functions are utilized to compare the performance of… More >

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