Mesfer Al Duhayyim^1,*, Sidra Abbas², Abdullah Al Hejaili³, Natalia Kryvinska^4,*, Ahmad Almadhor⁵, Huma Mughal⁶

Computer Systems Science and Engineering, Vol.47, No.1, pp. 823-842, 2023, DOI:10.32604/csse.2023.037488

Abstract : Cardiotocography (CTG) represents the fetus’s health inside the womb during labor. However, assessment of its readings can be a highly subjective process depending on the expertise of the obstetrician. Digital signals from fetal monitors acquire parameters (i.e., fetal heart rate, contractions, acceleration). Objective:: This paper aims to classify the CTG readings containing imbalanced healthy, suspected, and pathological fetus readings. Method:: We perform two sets of experiments. Firstly, we employ five classifiers: Random Forest (RF), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM) without over-sampling to classify CTG readings into three categories:… More >

Ahmed ElShafee¹, Walid El-Shafai^2,3, Abeer D. Algarni^4,*, Naglaa F. Soliman⁴, Moustafa H. Aly⁵

Computer Systems Science and Engineering, Vol.47, No.1, pp. 349-374, 2023, DOI:10.32604/csse.2023.037408

Abstract COVID-19 has significantly impacted the growth prediction of a pandemic, and it is critical in determining how to battle and track the disease progression. In this case, COVID-19 data is a time-series dataset that can be projected using different methodologies. Thus, this work aims to gauge the spread of the outbreak severity over time. Furthermore, data analytics and Machine Learning (ML) techniques are employed to gain a broader understanding of virus infections. We have simulated, adjusted, and fitted several statistical time-series forecasting models, linear ML models, and nonlinear ML models. Examples of these models are Logistic Regression, Lasso, Ridge, ElasticNet,… More >

Shehab Khan Noor¹, Arif Mawardi Ismail¹, Mohd Najib Mohd Yasin^1,*, Mohamed Nasrun Osman¹, Thennarasan Sabapathy¹, Shakhirul Mat Salleh², Ping Jack Soh³, Ali Hanafiah Rambe⁴, Nurulazlina Ramli⁵

Computer Systems Science and Engineering, Vol.47, No.1, pp. 537-551, 2023, DOI:10.32604/csse.2023.037381

Abstract This paper presents a flexible and wearable textile array antenna designed to generate Orbital Angular Momentum (OAM) waves with Mode +2 at 3.5 GHz (3.4 to 3.6 GHz) of the sub-6 GHz fifth-generation (5G) New Radio (NR) band. The proposed antenna is based on a uniform circular array of eight microstrip patch antennas on a felt textile substrate. In contrast to previous works involving the use of rigid substrates to generate OAM waves, this work explored the use of flexible substrates to generate OAM waves for the first time. Other than that, the proposed antenna was simulated, analyzed, fabricated, and… More >

Ahmad A. A. Alkhatib^*, Adnan A. Hnaif, Thaer Sawalha

Computer Systems Science and Engineering, Vol.47, No.1, pp. 637-656, 2023, DOI:10.32604/csse.2023.037345

Abstract Large cities suffer from traffic congestion, particularly at intersections, due to a large number of vehicles, which leads to the loss of time by increasing carbon emissions, including fuel consumption. Therefore, the need for optimising the flow of vehicles at different intersections and reducing the waiting time is a critical challenge. Conventional traffic lights have been used to control traffic flow at different intersections and have been improved to become more efficient by using different algorithms, sensors and cameras. However, they also face some challenges, such as high-cost installation, operation, and maintenance issues. This paper develops a new system based… More >

Sami Saeed Binyamin¹, Mahmoud Ragab^2,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 105-119, 2023, DOI:10.32604/csse.2023.037311

Abstract Cognitive radio wireless sensor networks (CRWSN) can be defined as a promising technology for developing bandwidth-limited applications. CRWSN is widely utilized by future Internet of Things (IoT) applications. Since a promising technology, Cognitive Radio (CR) can be modelled to alleviate the spectrum scarcity issue. Generally, CRWSN has cognitive radio-enabled sensor nodes (SNs), which are energy limited. Hierarchical cluster-related techniques for overall network management can be suitable for the scalability and stability of the network. This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering (MDMO-EAC) Scheme for CRWSN. The MDMO-EAC technique mainly intends to group the… More >

Kai Yang^*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 317-332, 2023, DOI:10.32604/csse.2023.037266

Abstract With the rapid development of sports, the number of sports images has increased dramatically. Intelligent and automatic processing and analysis of moving images are significant, which can not only facilitate users to quickly search and access moving images but also facilitate staff to store and manage moving image data and contribute to the intellectual development of the sports industry. In this paper, a method of table tennis identification and positioning based on a convolutional neural network is proposed, which solves the problem that the identification and positioning method based on color features and contour features is not adaptable in various… More >

Mesfer Al Duhayyim^1,*, Sidra Abbas^2,*, Abdullah Al Hejaili³, Natalia Kryvinska⁴, Ahmad Almadhor⁵, Uzma Ghulam Mohammad⁶

Computer Systems Science and Engineering, Vol.47, No.1, pp. 413-429, 2023, DOI:10.32604/csse.2023.037127

Abstract Stroke is a life-threatening disease usually due to blockage of blood or insufficient blood flow to the brain. It has a tremendous impact on every aspect of life since it is the leading global factor of disability and morbidity. Strokes can range from minor to severe (extensive). Thus, early stroke assessment and treatment can enhance survival rates. Manual prediction is extremely time and resource intensive. Automated prediction methods such as Modern Information and Communication Technologies (ICTs), particularly those in Machine Learning (ML) area, are crucial for the early diagnosis and prognosis of stroke. Therefore, this research proposed an ensemble voting… More >

Zhaomin Liang, Tingting Liang^*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 205-219, 2023, DOI:10.32604/csse.2023.037124

Abstract The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry. Most book recommendation systems also use this algorithm. However, the traditional recommendation algorithm represented by the collaborative filtering algorithm cannot deal with the data sparsity well. This algorithm only uses the shallow feature design of the interaction between readers and books, so it fails to achieve the high-level abstract learning of the relevant attribute features of readers and books, leading to a decline in recommendation performance. Given the above problems, this study uses deep learning technology to model… More >

Zakariae Dlimi^*, Abdellah Ezzati, Said Ben Alla

Computer Systems Science and Engineering, Vol.47, No.1, pp. 79-103, 2023, DOI:10.32604/csse.2023.037099

Abstract The growing advent of the Internet of Things (IoT) users is driving the adoption of cloud computing technologies. The integration of IoT in the cloud enables storage and computational capabilities for IoT users. However, security has been one of the main concerns of cloud-integrated IoT. Existing work attempts to address the security concerns of cloud-integrated IoT through authentication, access control, and blockchain-based methods. However, existing frameworks are somewhat limited by scalability, privacy, and centralized structures. To mitigate the existing problems, we propose a blockchain-based distributed access control method for secure storage in the IoT cloud (BL-DAC). Initially, the BL-DAC performs… More >

G. Manimala^*, A. Chinnasamy

Computer Systems Science and Engineering, Vol.47, No.1, pp. 393-412, 2023, DOI:10.32604/csse.2023.037066

Abstract Cloud computing facilitates the great potentiality of storing and managing remote access to services in terms of software as a service (SaaS). Several organizations have moved towards outsourcing over the cloud to reduce the burden on local resources. In this context, the metaheuristic optimization method is determined to be highly suitable for selecting appropriate services that comply with the requirements of the client’s requests, as the services stored over the cloud are too complex and scalable. To achieve better service composition, the parameters of Quality of Service (QoS) related to each service considered to be the best resource need to… More >